diff --git a/data/2024-04-11_prussin/adapters.fasta b/data/2024-04-11_prussin/adapters.fasta
new file mode 100644
index 0000000..6e60a37
--- /dev/null
+++ b/data/2024-04-11_prussin/adapters.fasta
@@ -0,0 +1,45 @@
+>0
+CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT
+>1
+GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG
+>2
+AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>3
+GATCGGAAGAGCGGTTCAGCAGGAATGCCGAG
+>4
+CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT
+>5
+GGCTCAAACCATGCACCGAAGCTACGGGTATCATCTTTTGATGATGCGGTAGAGGAGCGT
+>6
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>7
+ACGCTCCTCTACCGCATCATCAAAAGATGATACCCGTAGCTTCGGTGCATGGTTTGAGCC
+>8
+heifigepsna
+>9
+unspecified
+>10
+TGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>11
+GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG
+>12
+CAAGCAGAAGACGGCATACGAGAT
+>13
+CTGTCTCTTATACACATCTCCGAGCCCACGAGAC
+>14
+CTGTCTCTTATACACATCTGACGCTGCCGACGA
+>15
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATC
+>16
+ACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>17
+TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG
+>18
+GATCGGAAGAGCACACGTCTGAACTCCAGTCAC
+>19
+CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATC
+T
+>20
+AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
+>21
+AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
diff --git a/data/2024-04-11_prussin/blast/putative-viral-best.blast.gz b/data/2024-04-11_prussin/blast/putative-viral-best.blast.gz
new file mode 100644
index 0000000..595568a
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diff --git a/data/2024-04-11_prussin/bracken_counts.tsv b/data/2024-04-11_prussin/bracken_counts.tsv
new file mode 100644
index 0000000..035ac9d
--- /dev/null
+++ b/data/2024-04-11_prussin/bracken_counts.tsv
@@ -0,0 +1,129 @@
+name	taxonomy_id	taxonomy_lvl	kraken_assigned_reads	added_reads	new_est_reads	fraction_total_reads	sample
+Eukaryota	2759	D	23114	29	23143	0.76011	SRR8663608
+Bacteria	2	D	6306	12	6318	0.20751	SRR8663608
+Archaea	2157	D	10	0	10	3.3e-4	SRR8663608
+Viruses	10239	D	975	0	975	0.03202	SRR8663608
+Bacteria	2	D	4858723	155	4858878	0.96863	SRR8663601
+Eukaryota	2759	D	157096	50	157146	0.03133	SRR8663601
+Archaea	2157	D	114	0	114	2e-5	SRR8663601
+Viruses	10239	D	112	0	112	2e-5	SRR8663601
+Eukaryota	2759	D	2020112	2501	2022613	0.87927	SRR8663623
+Bacteria	2	D	273553	521	274074	0.11915	SRR8663623
+Archaea	2157	D	998	0	998	4.3e-4	SRR8663623
+Viruses	10239	D	2608	25	2633	0.00114	SRR8663623
+Eukaryota	2759	D	12876337	8753	12885090	0.77634	SRR8663617
+Bacteria	2	D	3697581	2422	3700003	0.22293	SRR8663617
+Archaea	2157	D	6559	0	6559	4e-4	SRR8663617
+Viruses	10239	D	5444	89	5533	3.3e-4	SRR8663617
+Eukaryota	2759	D	15931509	12059	15943568	0.7655	SRR8663615
+Bacteria	2	D	4869174	3112	4872286	0.23393	SRR8663615
+Archaea	2157	D	6713	0	6713	3.2e-4	SRR8663615
+Viruses	10239	D	4982	125	5107	2.5e-4	SRR8663615
+Eukaryota	2759	D	2618585	2592	2621177	0.89805	SRR8663620
+Bacteria	2	D	291171	495	291666	0.09993	SRR8663620
+Archaea	2157	D	1103	0	1103	3.8e-4	SRR8663620
+Viruses	10239	D	4789	12	4801	0.00164	SRR8663620
+Eukaryota	2759	D	3179073	2287	3181360	0.63287	SRR8663619
+Bacteria	2	D	1841428	704	1842132	0.36646	SRR8663619
+Archaea	2157	D	1455	0	1455	2.9e-4	SRR8663619
+Viruses	10239	D	1888	15	1903	3.8e-4	SRR8663619
+Eukaryota	2759	D	2131400	2592	2133992	0.89031	SRR8663622
+Bacteria	2	D	258666	380	259046	0.10808	SRR8663622
+Archaea	2157	D	935	0	935	3.9e-4	SRR8663622
+Viruses	10239	D	2891	31	2922	0.00122	SRR8663622
+Eukaryota	2759	D	4468846	2761	4471607	0.90883	SRR8663627
+Bacteria	2	D	443530	658	444188	0.09028	SRR8663627
+Archaea	2157	D	1505	0	1505	3.1e-4	SRR8663627
+Viruses	10239	D	2842	21	2863	5.8e-4	SRR8663627
+Bacteria	2	D	6453514	198	6453712	0.9413	SRR8663600
+Eukaryota	2759	D	402127	104	402231	0.05867	SRR8663600
+Archaea	2157	D	148	0	148	2e-5	SRR8663600
+Viruses	10239	D	83	0	83	1e-5	SRR8663600
+Eukaryota	2759	D	6950380	4713	6955093	0.76802	SRR8663604
+Bacteria	2	D	2094895	1301	2096196	0.23147	SRR8663604
+Archaea	2157	D	2294	0	2294	2.5e-4	SRR8663604
+Viruses	10239	D	2212	48	2260	2.5e-4	SRR8663604
+Eukaryota	2759	D	2315070	2745	2317815	0.89866	SRR8663621
+Bacteria	2	D	256987	430	257417	0.09981	SRR8663621
+Archaea	2157	D	911	0	911	3.5e-4	SRR8663621
+Viruses	10239	D	3036	9	3045	0.00118	SRR8663621
+Eukaryota	2759	D	4329068	3381	4332449	0.65941	SRR8663610
+Bacteria	2	D	2230419	1179	2231598	0.33966	SRR8663610
+Archaea	2157	D	1934	0	1934	2.9e-4	SRR8663610
+Viruses	10239	D	4139	25	4164	6.3e-4	SRR8663610
+Eukaryota	2759	D	6715965	5636	6721601	0.66434	SRR8663605
+Bacteria	2	D	3387664	2144	3389808	0.33504	SRR8663605
+Archaea	2157	D	2830	0	2830	2.8e-4	SRR8663605
+Viruses	10239	D	3439	38	3477	3.4e-4	SRR8663605
+Bacteria	2	D	4804040	1353	4805393	0.71165	SRR8663606
+Eukaryota	2759	D	1939671	2381	1942052	0.28761	SRR8663606
+Archaea	2157	D	2418	0	2418	3.6e-4	SRR8663606
+Viruses	10239	D	2596	11	2607	3.9e-4	SRR8663606
+Eukaryota	2759	D	5797094	4420	5801514	0.70566	SRR8663613
+Bacteria	2	D	2412241	1172	2413413	0.29355	SRR8663613
+Archaea	2157	D	2612	0	2612	3.2e-4	SRR8663613
+Viruses	10239	D	3841	28	3869	4.7e-4	SRR8663613
+Eukaryota	2759	D	14475	19	14494	0.66925	SRR8663609
+Bacteria	2	D	6474	11	6485	0.29944	SRR8663609
+Archaea	2157	D	5	0	5	2.3e-4	SRR8663609
+Viruses	10239	D	672	0	672	0.03103	SRR8663609
+Eukaryota	2759	D	3261369	2536	3263905	0.90304	SRR8663628
+Bacteria	2	D	346222	544	346766	0.09594	SRR8663628
+Archaea	2157	D	1554	0	1554	4.3e-4	SRR8663628
+Viruses	10239	D	2098	28	2126	5.9e-4	SRR8663628
+Eukaryota	2759	D	3890470	3542	3894012	0.88618	SRR8663598
+Bacteria	2	D	488469	681	489150	0.11132	SRR8663598
+Archaea	2157	D	1902	0	1902	4.3e-4	SRR8663598
+Viruses	10239	D	9058	17	9075	0.00207	SRR8663598
+Eukaryota	2759	D	4554828	3549	4558377	0.71166	SRR8663611
+Bacteria	2	D	1841167	959	1842126	0.28759	SRR8663611
+Archaea	2157	D	1958	0	1958	3.1e-4	SRR8663611
+Viruses	10239	D	2794	51	2845	4.4e-4	SRR8663611
+Eukaryota	2759	D	740508	709	741217	0.65795	SRR8663629
+Bacteria	2	D	383307	272	383579	0.34049	SRR8663629
+Archaea	2157	D	699	0	699	6.2e-4	SRR8663629
+Viruses	10239	D	1056	2	1058	9.4e-4	SRR8663629
+Eukaryota	2759	D	4082341	2951	4085292	0.68913	SRR8663614
+Bacteria	2	D	1838109	784	1838893	0.3102	SRR8663614
+Archaea	2157	D	2149	0	2149	3.6e-4	SRR8663614
+Viruses	10239	D	1795	16	1811	3.1e-4	SRR8663614
+Eukaryota	2759	D	2354891	2349	2357240	0.86425	SRR8663626
+Bacteria	2	D	363337	580	363917	0.13343	SRR8663626
+Archaea	2157	D	906	0	906	3.3e-4	SRR8663626
+Viruses	10239	D	5413	9	5422	0.00199	SRR8663626
+Eukaryota	2759	D	6198690	3751	6202441	0.91084	SRR8663603
+Bacteria	2	D	599468	977	600445	0.08818	SRR8663603
+Archaea	2157	D	2598	0	2598	3.8e-4	SRR8663603
+Viruses	10239	D	4072	55	4127	6.1e-4	SRR8663603
+Eukaryota	2759	D	4519228	2808	4522036	0.90716	SRR8663599
+Bacteria	2	D	458033	691	458724	0.09202	SRR8663599
+Archaea	2157	D	1796	0	1796	3.6e-4	SRR8663599
+Viruses	10239	D	2264	28	2292	4.6e-4	SRR8663599
+Eukaryota	2759	D	6296110	5024	6301134	0.72846	SRR8663607
+Bacteria	2	D	2342386	1298	2343684	0.27095	SRR8663607
+Archaea	2157	D	3132	0	3132	3.6e-4	SRR8663607
+Viruses	10239	D	2002	31	2033	2.4e-4	SRR8663607
+Eukaryota	2759	D	4983881	3909	4987790	0.68704	SRR8663612
+Bacteria	2	D	2264835	1027	2265862	0.31211	SRR8663612
+Archaea	2157	D	2349	0	2349	3.2e-4	SRR8663612
+Viruses	10239	D	3767	20	3787	5.2e-4	SRR8663612
+Eukaryota	2759	D	1353957	1958	1355915	0.85703	SRR8663624
+Bacteria	2	D	221917	313	222230	0.14046	SRR8663624
+Archaea	2157	D	568	0	568	3.6e-4	SRR8663624
+Viruses	10239	D	3385	4	3389	0.00214	SRR8663624
+Eukaryota	2759	D	16240454	11556	16252010	0.77795	SRR8663616
+Bacteria	2	D	4622185	2974	4625159	0.2214	SRR8663616
+Archaea	2157	D	6551	0	6551	3.1e-4	SRR8663616
+Viruses	10239	D	7029	117	7146	3.4e-4	SRR8663616
+Eukaryota	2759	D	7994576	4641	7999217	0.91032	SRR8663602
+Bacteria	2	D	778005	1429	779434	0.0887	SRR8663602
+Archaea	2157	D	2960	0	2960	3.4e-4	SRR8663602
+Viruses	10239	D	5601	79	5680	6.5e-4	SRR8663602
+Eukaryota	2759	D	9282055	7402	9289457	0.63742	SRR8663618
+Bacteria	2	D	5270095	2864	5272959	0.36182	SRR8663618
+Archaea	2157	D	4454	0	4454	3.1e-4	SRR8663618
+Viruses	10239	D	6519	59	6578	4.5e-4	SRR8663618
+Eukaryota	2759	D	2074031	2319	2076350	0.86058	SRR8663625
+Bacteria	2	D	331748	444	332192	0.13768	SRR8663625
+Archaea	2157	D	869	0	869	3.6e-4	SRR8663625
+Viruses	10239	D	3315	11	3326	0.00138	SRR8663625
diff --git a/data/2024-04-11_prussin/hv_clade_counts.tsv.gz b/data/2024-04-11_prussin/hv_clade_counts.tsv.gz
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diff --git a/data/2024-04-11_prussin/qc_basic_stats.tsv b/data/2024-04-11_prussin/qc_basic_stats.tsv
new file mode 100644
index 0000000..f729813
--- /dev/null
+++ b/data/2024-04-11_prussin/qc_basic_stats.tsv
@@ -0,0 +1,161 @@
+sample	percent_gc	mean_seq_len	n_read_pairs	percent_duplicates	n_bases_approx	per_base_sequence_quality	per_sequence_quality_scores	per_base_sequence_content	per_sequence_gc_content	per_base_n_content	sequence_length_distribution	sequence_duplication_levels	overrepresented_sequences	adapter_content	stage
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diff --git a/data/2024-04-11_prussin/qc_quality_base_stats.tsv b/data/2024-04-11_prussin/qc_quality_base_stats.tsv
new file mode 100644
index 0000000..1ab90ab
--- /dev/null
+++ b/data/2024-04-11_prussin/qc_quality_base_stats.tsv
@@ -0,0 +1,12161 @@
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diff --git a/data/2024-04-11_prussin/qc_quality_sequence_stats.tsv b/data/2024-04-11_prussin/qc_quality_sequence_stats.tsv
new file mode 100644
index 0000000..4bef16b
--- /dev/null
+++ b/data/2024-04-11_prussin/qc_quality_sequence_stats.tsv
@@ -0,0 +1,4779 @@
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+31	552360	SRR8663623	2	ribo_secondary
+32	681977	SRR8663623	2	ribo_secondary
+33	730263	SRR8663623	2	ribo_secondary
+34	504007	SRR8663623	2	ribo_secondary
+35	112369	SRR8663623	2	ribo_secondary
+36	544	SRR8663623	2	ribo_secondary
+25	5068	SRR8663624	1	ribo_secondary
+26	24137	SRR8663624	1	ribo_secondary
+27	61695	SRR8663624	1	ribo_secondary
+28	118825	SRR8663624	1	ribo_secondary
+29	187857	SRR8663624	1	ribo_secondary
+30	267095	SRR8663624	1	ribo_secondary
+31	352438	SRR8663624	1	ribo_secondary
+32	443785	SRR8663624	1	ribo_secondary
+33	522904	SRR8663624	1	ribo_secondary
+34	488090	SRR8663624	1	ribo_secondary
+35	167066	SRR8663624	1	ribo_secondary
+36	251	SRR8663624	1	ribo_secondary
+25	20815	SRR8663624	2	ribo_secondary
+26	62840	SRR8663624	2	ribo_secondary
+27	112133	SRR8663624	2	ribo_secondary
+28	167870	SRR8663624	2	ribo_secondary
+29	229505	SRR8663624	2	ribo_secondary
+30	298200	SRR8663624	2	ribo_secondary
+31	377344	SRR8663624	2	ribo_secondary
+32	458106	SRR8663624	2	ribo_secondary
+33	490944	SRR8663624	2	ribo_secondary
+34	342312	SRR8663624	2	ribo_secondary
+35	78744	SRR8663624	2	ribo_secondary
+36	398	SRR8663624	2	ribo_secondary
+25	6053	SRR8663625	1	ribo_secondary
+26	29820	SRR8663625	1	ribo_secondary
+27	80048	SRR8663625	1	ribo_secondary
+28	161255	SRR8663625	1	ribo_secondary
+29	265711	SRR8663625	1	ribo_secondary
+30	387228	SRR8663625	1	ribo_secondary
+31	519448	SRR8663625	1	ribo_secondary
+32	660596	SRR8663625	1	ribo_secondary
+33	772944	SRR8663625	1	ribo_secondary
+34	713782	SRR8663625	1	ribo_secondary
+35	240573	SRR8663625	1	ribo_secondary
+36	396	SRR8663625	1	ribo_secondary
+25	26889	SRR8663625	2	ribo_secondary
+26	82602	SRR8663625	2	ribo_secondary
+27	153251	SRR8663625	2	ribo_secondary
+28	236655	SRR8663625	2	ribo_secondary
+29	333268	SRR8663625	2	ribo_secondary
+30	442495	SRR8663625	2	ribo_secondary
+31	563389	SRR8663625	2	ribo_secondary
+32	680478	SRR8663625	2	ribo_secondary
+33	719200	SRR8663625	2	ribo_secondary
+34	490351	SRR8663625	2	ribo_secondary
+35	108685	SRR8663625	2	ribo_secondary
+36	591	SRR8663625	2	ribo_secondary
+25	7981	SRR8663626	1	ribo_secondary
+26	38358	SRR8663626	1	ribo_secondary
+27	104436	SRR8663626	1	ribo_secondary
+28	213541	SRR8663626	1	ribo_secondary
+29	356268	SRR8663626	1	ribo_secondary
+30	519914	SRR8663626	1	ribo_secondary
+31	688510	SRR8663626	1	ribo_secondary
+32	835276	SRR8663626	1	ribo_secondary
+33	911299	SRR8663626	1	ribo_secondary
+34	745693	SRR8663626	1	ribo_secondary
+35	220092	SRR8663626	1	ribo_secondary
+36	383	SRR8663626	1	ribo_secondary
+25	32769	SRR8663626	2	ribo_secondary
+26	104279	SRR8663626	2	ribo_secondary
+27	198966	SRR8663626	2	ribo_secondary
+28	316156	SRR8663626	2	ribo_secondary
+29	452257	SRR8663626	2	ribo_secondary
+30	600494	SRR8663626	2	ribo_secondary
+31	745378	SRR8663626	2	ribo_secondary
+32	847835	SRR8663626	2	ribo_secondary
+33	796749	SRR8663626	2	ribo_secondary
+34	458650	SRR8663626	2	ribo_secondary
+35	87680	SRR8663626	2	ribo_secondary
+36	538	SRR8663626	2	ribo_secondary
+25	8444	SRR8663627	1	ribo_secondary
+26	43650	SRR8663627	1	ribo_secondary
+27	124117	SRR8663627	1	ribo_secondary
+28	266647	SRR8663627	1	ribo_secondary
+29	465776	SRR8663627	1	ribo_secondary
+30	705844	SRR8663627	1	ribo_secondary
+31	976122	SRR8663627	1	ribo_secondary
+32	1266519	SRR8663627	1	ribo_secondary
+33	1518692	SRR8663627	1	ribo_secondary
+34	1430933	SRR8663627	1	ribo_secondary
+35	492757	SRR8663627	1	ribo_secondary
+36	787	SRR8663627	1	ribo_secondary
+25	40947	SRR8663627	2	ribo_secondary
+26	126168	SRR8663627	2	ribo_secondary
+27	246656	SRR8663627	2	ribo_secondary
+28	402351	SRR8663627	2	ribo_secondary
+29	592388	SRR8663627	2	ribo_secondary
+30	811761	SRR8663627	2	ribo_secondary
+31	1062660	SRR8663627	2	ribo_secondary
+32	1322584	SRR8663627	2	ribo_secondary
+33	1439612	SRR8663627	2	ribo_secondary
+34	1021568	SRR8663627	2	ribo_secondary
+35	232331	SRR8663627	2	ribo_secondary
+36	1262	SRR8663627	2	ribo_secondary
+25	8621	SRR8663628	1	ribo_secondary
+26	41244	SRR8663628	1	ribo_secondary
+27	111485	SRR8663628	1	ribo_secondary
+28	230459	SRR8663628	1	ribo_secondary
+29	390718	SRR8663628	1	ribo_secondary
+30	580474	SRR8663628	1	ribo_secondary
+31	792537	SRR8663628	1	ribo_secondary
+32	1003181	SRR8663628	1	ribo_secondary
+33	1169073	SRR8663628	1	ribo_secondary
+34	1051431	SRR8663628	1	ribo_secondary
+35	342145	SRR8663628	1	ribo_secondary
+36	566	SRR8663628	1	ribo_secondary
+25	37602	SRR8663628	2	ribo_secondary
+26	113223	SRR8663628	2	ribo_secondary
+27	212432	SRR8663628	2	ribo_secondary
+28	337914	SRR8663628	2	ribo_secondary
+29	488245	SRR8663628	2	ribo_secondary
+30	657624	SRR8663628	2	ribo_secondary
+31	850115	SRR8663628	2	ribo_secondary
+32	1034968	SRR8663628	2	ribo_secondary
+33	1089651	SRR8663628	2	ribo_secondary
+34	734642	SRR8663628	2	ribo_secondary
+35	164570	SRR8663628	2	ribo_secondary
+36	948	SRR8663628	2	ribo_secondary
+25	3058	SRR8663629	1	ribo_secondary
+26	15636	SRR8663629	1	ribo_secondary
+27	41673	SRR8663629	1	ribo_secondary
+28	85112	SRR8663629	1	ribo_secondary
+29	141307	SRR8663629	1	ribo_secondary
+30	207548	SRR8663629	1	ribo_secondary
+31	281723	SRR8663629	1	ribo_secondary
+32	362563	SRR8663629	1	ribo_secondary
+33	435350	SRR8663629	1	ribo_secondary
+34	415170	SRR8663629	1	ribo_secondary
+35	141831	SRR8663629	1	ribo_secondary
+36	168	SRR8663629	1	ribo_secondary
+25	13840	SRR8663629	2	ribo_secondary
+26	41916	SRR8663629	2	ribo_secondary
+27	77614	SRR8663629	2	ribo_secondary
+28	122881	SRR8663629	2	ribo_secondary
+29	175764	SRR8663629	2	ribo_secondary
+30	235868	SRR8663629	2	ribo_secondary
+31	307020	SRR8663629	2	ribo_secondary
+32	380525	SRR8663629	2	ribo_secondary
+33	415920	SRR8663629	2	ribo_secondary
+34	293837	SRR8663629	2	ribo_secondary
+35	65680	SRR8663629	2	ribo_secondary
+36	274	SRR8663629	2	ribo_secondary
diff --git a/data/2024-04-11_prussin/sample-metadata.csv b/data/2024-04-11_prussin/sample-metadata.csv
new file mode 100644
index 0000000..a2db1ad
--- /dev/null
+++ b/data/2024-04-11_prussin/sample-metadata.csv
@@ -0,0 +1,33 @@
+sample,library,na_type,date,sample_alias,season
+SRR8663598,SRR8663598,RNA,2014-04-14,14_April_2014_12_May_2014,Spring
+SRR8663599,SRR8663599,RNA,2014-03-17,17_March_2014_14_April_2014,Spring
+SRR8663600,SRR8663600,DNA,2014-12-23,DNANegative_Control,not applicable
+SRR8663601,SRR8663601,DNA,2014-12-23,DNAUnexposed_Filter,not applicable
+SRR8663602,SRR8663602,RNA,2014-02-17,17_February_2014_17_March_2014,Winter
+SRR8663603,SRR8663603,RNA,2014-01-20,20_January_2014_17_February_2014,Winter
+SRR8663604,SRR8663604,DNA,2014-11-24,24_November_2014_23_December_2014,Winter
+SRR8663605,SRR8663605,DNA,2014-10-27,27_October_2014_24_November_2014,Fall
+SRR8663606,SRR8663606,DNA,2014-12-23,23_December_2014_05_January_2015,Closed
+SRR8663607,SRR8663607,DNA,2015-01-05,05_Janurary_2015_02_February_2015,Winter
+SRR8663608,SRR8663608,RNA,2014-12-23,RNANegative_Control,not applicable
+SRR8663609,SRR8663609,RNA,2014-12-23,RNAUnexposed_Filter,not applicable
+SRR8663610,SRR8663610,DNA,2014-08-04,04_August_2014_02_September_2014,Summer
+SRR8663611,SRR8663611,DNA,2014-07-07,07_July_2014_04_August_2014,Summer
+SRR8663612,SRR8663612,DNA,2014-06-09,09_June_2014_07_July_2014,Summer
+SRR8663613,SRR8663613,DNA,2014-05-12,12_May_2014_09_June_2014,Spring
+SRR8663614,SRR8663614,DNA,2014-04-14,14_April_2014_12_May_2014,Spring
+SRR8663615,SRR8663615,DNA,2014-03-17,17_March_2014_14_April_2014,Spring
+SRR8663616,SRR8663616,DNA,2014-02-17,17_February_2014_17_March_2014,Winter
+SRR8663617,SRR8663617,DNA,2014-01-20,20_January_2014_17_February_2014,Winter
+SRR8663618,SRR8663618,DNA,2014-09-29,29_September_2014_27_October_2014,Fall
+SRR8663619,SRR8663619,DNA,2014-09-02,02_September_2014_29_September_2014,Fall
+SRR8663620,SRR8663620,RNA,2014-05-12,12_May_2014_09_June_2014,Spring
+SRR8663621,SRR8663621,RNA,2014-06-09,09_June_2014_07_July_2014,Summer
+SRR8663622,SRR8663622,RNA,2014-07-07,07_July_2014_04_August_2014,Summer
+SRR8663623,SRR8663623,RNA,2014-08-04,04_August_2014_02_September_2014,Summer
+SRR8663624,SRR8663624,RNA,2014-09-02,02_September_2014_29_September_2014,Fall
+SRR8663625,SRR8663625,RNA,2014-09-29,29_September_2014_27_October_2014,Fall
+SRR8663626,SRR8663626,RNA,2014-10-27,27_October_2014_24_November_2014,Fall
+SRR8663627,SRR8663627,RNA,2014-11-24,24_November_2014_23_December_2014,Winter
+SRR8663628,SRR8663628,RNA,2015-01-05,05_Janurary_2015_02_February_2015,Winter
+SRR8663629,SRR8663629,RNA,2014-12-23,23_December_2014_05_January_2015,Closed
\ No newline at end of file
diff --git a/data/2024-04-11_prussin/taxid-names.tsv.gz b/data/2024-04-11_prussin/taxid-names.tsv.gz
new file mode 120000
index 0000000..626546b
--- /dev/null
+++ b/data/2024-04-11_prussin/taxid-names.tsv.gz
@@ -0,0 +1 @@
+../2024-04-01_spurbeck/taxid-names.tsv.gz
\ No newline at end of file
diff --git a/data/2024-04-11_prussin/taxonomic_composition.tsv b/data/2024-04-11_prussin/taxonomic_composition.tsv
new file mode 100644
index 0000000..a8f70a0
--- /dev/null
+++ b/data/2024-04-11_prussin/taxonomic_composition.tsv
@@ -0,0 +1,257 @@
+sample	classification	n_reads	p_reads
+SRR8663598	Filtered	1474598	0.07954458961468615
+SRR8663598	Duplicate	2572053	0.13874486494096858
+SRR8663598	Ribosomal	7075786	0.38169080221954843
+SRR8663598	Unassigned	3021429	0.16298566107841703
+SRR8663598	Bacterial	489150	0.026386334451846356
+SRR8663598	Archaeal	1902	1.0260003705900392e-4
+SRR8663598	Viral	9075	4.895348771348373e-4
+SRR8663598	Human	3894012	0.21005561278033963
+SRR8663599	Filtered	807400	0.07414364049315347
+SRR8663599	Duplicate	607279	0.05576650465078244
+SRR8663599	Ribosomal	2425257	0.22271164616237787
+SRR8663599	Unassigned	2064889	0.18961900876178742
+SRR8663599	Bacterial	458724	0.042124680878847326
+SRR8663599	Archaeal	1796	1.6492689909054203e-4
+SRR8663599	Viral	2292	2.1047463959661598e-4
+SRR8663599	Human	4522036	0.4152591175143643
+SRR8663600	Filtered	690428	0.06540866203310942
+SRR8663600	Duplicate	2071238	0.19622162822500464
+SRR8663600	Ribosomal	22138	0.0020972743864515584
+SRR8663600	Unassigned	915627	0.08674320420288557
+SRR8663600	Bacterial	6453712	0.6114014308038241
+SRR8663600	Archaeal	148	1.4020986954324266e-5
+SRR8663600	Viral	83	7.863121062222393e-6
+SRR8663600	Human	402231	0.03810591624070814
+SRR8663601	Filtered	478448	0.06916694146105998
+SRR8663601	Duplicate	626983	0.09063993674982396
+SRR8663601	Ribosomal	17266	0.002496063127584736
+SRR8663601	Unassigned	778346	0.11252176248714635
+SRR8663601	Bacterial	4858878	0.7024247780164871
+SRR8663601	Archaeal	114	1.6480435337927712e-5
+SRR8663601	Viral	112	1.6191304893402664e-5
+SRR8663601	Human	157146	0.022717846417666564
+SRR8663602	Filtered	1345619	0.06702240771113392
+SRR8663602	Duplicate	1640317	0.08170068552057012
+SRR8663602	Ribosomal	4518454	0.22505454085592125
+SRR8663602	Unassigned	3785470	0.1885461737076142
+SRR8663602	Bacterial	779434	0.03882194241603303
+SRR8663602	Archaeal	2960	1.474312764794168e-4
+SRR8663602	Viral	5680	2.829086656767188e-4
+SRR8663602	Human	7999217	0.39842390984657133
+SRR8663603	Filtered	1248815	0.07853680050779142
+SRR8663603	Duplicate	1308391	0.08228347910073928
+SRR8663603	Ribosomal	3690436	0.23208804820471546
+SRR8663603	Unassigned	2843764	0.17884164264461827
+SRR8663603	Bacterial	600445	0.0377614211719917
+SRR8663603	Archaeal	2598	1.633857758909383e-4
+SRR8663603	Viral	4127	2.595431474603165e-4
+SRR8663603	Human	6202441	0.3900656794467926
+SRR8663604	Filtered	956499	0.054478182476073285
+SRR8663604	Duplicate	238224	0.013568242666411656
+SRR8663604	Ribosomal	53492	0.0030466805893264
+SRR8663604	Unassigned	7253411	0.4131239531164771
+SRR8663604	Bacterial	2096196	0.11939055680519783
+SRR8663604	Archaeal	2294	1.3065664532854935e-4
+SRR8663604	Viral	2260	1.2872014753379318e-4
+SRR8663604	Human	6955093	0.3961330075536514
+SRR8663605	Filtered	1190721	0.057095207060097984
+SRR8663605	Duplicate	244129	0.011706013251109757
+SRR8663605	Ribosomal	67899	0.003255764754441715
+SRR8663605	Unassigned	9234543	0.4427973846857311
+SRR8663605	Bacterial	3389808	0.1625416782386274
+SRR8663605	Archaeal	2830	1.356988211176903e-4
+SRR8663605	Viral	3477	1.6672254453222937e-4
+SRR8663605	Human	6721601	0.32230153064434214
+SRR8663606	Filtered	1054042	0.0713967886408036
+SRR8663606	Duplicate	166383	0.01127015041565974
+SRR8663606	Ribosomal	50428	0.0034158005635244546
+SRR8663606	Unassigned	6739834	0.456530672944818
+SRR8663606	Bacterial	4805393	0.3254990108145568
+SRR8663606	Archaeal	2418	1.6378610618311516e-4
+SRR8663606	Viral	2607	1.7658824599643558e-4
+SRR8663606	Human	1942052	0.1315472022684579
+SRR8663607	Filtered	1031731	0.057850799437915934
+SRR8663607	Duplicate	192918	0.010817219339114427
+SRR8663607	Ribosomal	49270	0.0027626473260046643
+SRR8663607	Unassigned	7910441	0.4435510183918746
+SRR8663607	Bacterial	2343684	0.13141409246194266
+SRR8663607	Archaeal	3132	1.7561622539165024e-4
+SRR8663607	Viral	2033	1.1399354604764527e-4
+SRR8663607	Human	6301134	0.35331461327170843
+SRR8663608	Filtered	64092	0.35435787425083487
+SRR8663608	Duplicate	10631	0.05877767211447022
+SRR8663608	Ribosomal	22903	0.12662825928301302
+SRR8663608	Unassigned	52796	0.2919034876263352
+SRR8663608	Bacterial	6318	0.034931552292279455
+SRR8663608	Archaeal	10	5.528894000044231e-5
+SRR8663608	Viral	975	0.005390671650043126
+SRR8663608	Human	23143	0.12795519384302365
+SRR8663609	Filtered	686507	0.8166616307370935
+SRR8663609	Duplicate	22189	0.026395805031012604
+SRR8663609	Ribosomal	47167	0.05610937563196951
+SRR8663609	Unassigned	63107	0.07507143485925964
+SRR8663609	Bacterial	6485	0.007714488964176697
+SRR8663609	Archaeal	5	5.9479483147083245e-6
+SRR8663609	Viral	672	7.994042534967988e-4
+SRR8663609	Human	14494	0.017241912574676492
+SRR8663610	Filtered	727440	0.05682559851664752
+SRR8663610	Duplicate	130965	0.010230623157556283
+SRR8663610	Ribosomal	36670	0.00286455886066956
+SRR8663610	Unassigned	5336053	0.4168376848146274
+SRR8663610	Bacterial	2231598	0.1743262564590256
+SRR8663610	Archaeal	1934	1.5107872474870272e-4
+SRR8663610	Viral	4164	3.252801498725947e-4
+SRR8663610	Human	4332449	0.3384389193168523
+SRR8663611	Filtered	722984	0.05490716496641817
+SRR8663611	Duplicate	153685	0.011671638166078331
+SRR8663611	Ribosomal	41619	0.0031607633069851587
+SRR8663611	Unassigned	5843795	0.44380818399152633
+SRR8663611	Bacterial	1842126	0.13990062874272188
+SRR8663611	Archaeal	1958	1.487007029259939e-4
+SRR8663611	Viral	2845	2.1606409592668676e-4
+SRR8663611	Human	4558377	0.34618685602741744
+SRR8663612	Filtered	810665	0.05568044760578162
+SRR8663612	Duplicate	158410	0.010880375624002351
+SRR8663612	Ribosomal	44235	0.0030382767232355534
+SRR8663612	Unassigned	6286142	0.43176305906077517
+SRR8663612	Bacterial	2265862	0.15563051368065917
+SRR8663612	Archaeal	2349	1.613408392196296e-4
+SRR8663612	Viral	3787	2.6010973100244246e-4
+SRR8663612	Human	4987790	0.3425858767353241
+SRR8663613	Filtered	966902	0.0553238843683429
+SRR8663613	Duplicate	203669	0.011653466644412806
+SRR8663613	Ribosomal	52921	0.0030280165773336645
+SRR8663613	Unassigned	8032217	0.4595847816318904
+SRR8663613	Bacterial	2413413	0.13808988061360464
+SRR8663613	Archaeal	2612	1.4945256703379625e-4
+SRR8663613	Viral	3869	2.2137518447693633e-4
+SRR8663613	Human	5801514	0.33194914241290485
+SRR8663614	Filtered	649242	0.05302962360714354
+SRR8663614	Duplicate	124290	0.010151918572938705
+SRR8663614	Ribosomal	34957	0.002855262833327044
+SRR8663614	Unassigned	5506372	0.4497565385494379
+SRR8663614	Bacterial	1838893	0.1501994689866198
+SRR8663614	Archaeal	2149	1.7552878761964178e-4
+SRR8663614	Viral	1811	1.479211886361895e-4
+SRR8663614	Human	4085292	0.33368373747427715
+SRR8663615	Filtered	2028244	0.04794762744296042
+SRR8663615	Duplicate	1001852	0.02368375128879207
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+<div class="quarto-post image-right" data-index="14" data-listing-date-sort="1699419600000" data-listing-file-modified-sort="1699450659122" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="13" data-listing-word-count-sort="2479">
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@@ -472,7 +494,7 @@ <h3 class="no-anchor listing-title">
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+<div class="quarto-post image-right" data-index="15" data-listing-date-sort="1698897600000" data-listing-file-modified-sort="1698943032000" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="14" data-listing-word-count-sort="2641">
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+<div class="quarto-post image-right" data-index="16" data-listing-date-sort="1698724800000" data-listing-file-modified-sort="1698941598593" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="17" data-listing-word-count-sort="3340">
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+<div class="quarto-post image-right" data-index="17" data-listing-date-sort="1697688000000" data-listing-file-modified-sort="1697766328595" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="11" data-listing-word-count-sort="2178">
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+<div class="quarto-post image-right" data-index="18" data-listing-date-sort="1697428800000" data-listing-file-modified-sort="1697493211896" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="15" data-listing-word-count-sort="2863">
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+<div class="quarto-post image-right" data-index="20" data-listing-date-sort="1696996800000" data-listing-file-modified-sort="1697148020355" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="10" data-listing-word-count-sort="1888">
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+<div class="quarto-post image-right" data-index="21" data-listing-date-sort="1695268800000" data-listing-file-modified-sort="1695331351195" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="11" data-listing-word-count-sort="2167">
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diff --git a/docs/listings.json b/docs/listings.json
index 5d40cea..7519bdd 100644
--- a/docs/listings.json
+++ b/docs/listings.json
@@ -2,6 +2,7 @@
   {
     "listing": "/index.html",
     "items": [
+      "/notebooks/2024-04-12_prussin.html",
       "/notebooks/2024-04-08_brumfield.html",
       "/notebooks/2024-04-01_spurbeck.html",
       "/notebooks/2024-03-19_yang-2.html",
diff --git a/docs/notebooks/2024-04-08_brumfield.html b/docs/notebooks/2024-04-08_brumfield.html
index f86b507..e3be542 100644
--- a/docs/notebooks/2024-04-08_brumfield.html
+++ b/docs/notebooks/2024-04-08_brumfield.html
@@ -2096,465 +2096,464 @@
 <span id="cb36-504"><a href="#cb36-504" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb36-507"><a href="#cb36-507" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
 <span id="cb36-508"><a href="#cb36-508" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-read-counts</span></span>
-<span id="cb36-509"><a href="#cb36-509" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-510"><a href="#cb36-510" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"hv_hits_putative_filtered.tsv.gz"</span>)</span>
-<span id="cb36-511"><a href="#cb36-511" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(hv_reads_filtered_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-512"><a href="#cb36-512" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb36-513"><a href="#cb36-513" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(date, na_type)</span>
-<span id="cb36-514"><a href="#cb36-514" aria-hidden="true" tabindex="-1"></a>n_hv_filtered <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample, date, na_type) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">inner_join</span>(basic_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"ribo_initial"</span>) <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, n_read_pairs), <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">n_putative =</span> n, <span class="at">n_total =</span> n_read_pairs) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads <span class="sc">*</span> <span class="dv">100</span>)</span>
-<span id="cb36-515"><a href="#cb36-515" aria-hidden="true" tabindex="-1"></a>n_hv_filtered_summ <span class="ot">&lt;-</span> n_hv_filtered <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type) <span class="sc">%&gt;%</span> <span class="fu">summarize</span>(<span class="at">n_putative =</span> <span class="fu">sum</span>(n_putative), <span class="at">n_total =</span> <span class="fu">sum</span>(n_total)) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads<span class="sc">*</span><span class="dv">100</span>)</span>
-<span id="cb36-516"><a href="#cb36-516" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-517"><a href="#cb36-517" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-520"><a href="#cb36-520" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-521"><a href="#cb36-521" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-scores</span></span>
-<span id="cb36-522"><a href="#cb36-522" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
-<span id="cb36-523"><a href="#cb36-523" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 8</span></span>
-<span id="cb36-524"><a href="#cb36-524" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-525"><a href="#cb36-525" aria-hidden="true" tabindex="-1"></a>mrg <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span></span>
-<span id="cb36-526"><a href="#cb36-526" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">kraken_label =</span> <span class="fu">ifelse</span>(assigned_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">assignment"</span>,</span>
-<span id="cb36-527"><a href="#cb36-527" aria-hidden="true" tabindex="-1"></a>                               <span class="fu">ifelse</span>(hit_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">hit"</span>,</span>
-<span id="cb36-528"><a href="#cb36-528" aria-hidden="true" tabindex="-1"></a>                                      <span class="st">"No hit or</span><span class="sc">\n</span><span class="st">assignment"</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb36-529"><a href="#cb36-529" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, na_type) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(adj_score_fwd), <span class="fu">desc</span>(adj_score_rev)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-530"><a href="#cb36-530" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_num =</span> <span class="fu">row_number</span>(),</span>
-<span id="cb36-531"><a href="#cb36-531" aria-hidden="true" tabindex="-1"></a>         <span class="at">adj_score_max =</span> <span class="fu">pmax</span>(adj_score_fwd, adj_score_rev))</span>
-<span id="cb36-532"><a href="#cb36-532" aria-hidden="true" tabindex="-1"></a><span class="co"># Import Bowtie2/Kraken data and perform filtering steps</span></span>
-<span id="cb36-533"><a href="#cb36-533" aria-hidden="true" tabindex="-1"></a>g_mrg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev)) <span class="sc">+</span></span>
-<span id="cb36-534"><a href="#cb36-534" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span> </span>
-<span id="cb36-535"><a href="#cb36-535" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb36-536"><a href="#cb36-536" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb36-537"><a href="#cb36-537" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
-<span id="cb36-538"><a href="#cb36-538" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>)</span>
-<span id="cb36-539"><a href="#cb36-539" aria-hidden="true" tabindex="-1"></a>g_mrg</span>
-<span id="cb36-540"><a href="#cb36-540" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-541"><a href="#cb36-541" aria-hidden="true" tabindex="-1"></a>g_hist_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>,<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>kraken_label, <span class="at">scales=</span><span class="st">"free_y"</span>) <span class="sc">+</span> <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Dark2"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>)</span>
-<span id="cb36-542"><a href="#cb36-542" aria-hidden="true" tabindex="-1"></a>g_hist_0</span>
-<span id="cb36-543"><a href="#cb36-543" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-544"><a href="#cb36-544" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-545"><a href="#cb36-545" aria-hidden="true" tabindex="-1"></a>As previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I've used for previous datasets.</span>
-<span id="cb36-546"><a href="#cb36-546" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-549"><a href="#cb36-549" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-550"><a href="#cb36-550" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: make-blast-fasta</span></span>
-<span id="cb36-551"><a href="#cb36-551" aria-hidden="true" tabindex="-1"></a>mrg_fasta <span class="ot">&lt;-</span>  mrg <span class="sc">%&gt;%</span></span>
-<span id="cb36-552"><a href="#cb36-552" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_head =</span> <span class="fu">paste0</span>(<span class="st">"&gt;"</span>, seq_id)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-553"><a href="#cb36-553" aria-hidden="true" tabindex="-1"></a>  ungroup <span class="sc">%&gt;%</span></span>
-<span id="cb36-554"><a href="#cb36-554" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">header1=</span>seq_head, <span class="at">seq1=</span>query_seq_fwd, </span>
-<span id="cb36-555"><a href="#cb36-555" aria-hidden="true" tabindex="-1"></a>         <span class="at">header2=</span>seq_head, <span class="at">seq2=</span>query_seq_rev) <span class="sc">%&gt;%</span></span>
-<span id="cb36-556"><a href="#cb36-556" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">header1=</span><span class="fu">paste0</span>(header1, <span class="st">"_1"</span>), <span class="at">header2=</span><span class="fu">paste0</span>(header2, <span class="st">"_2"</span>))</span>
-<span id="cb36-557"><a href="#cb36-557" aria-hidden="true" tabindex="-1"></a>mrg_fasta_out <span class="ot">&lt;-</span> <span class="fu">do.call</span>(paste, <span class="fu">c</span>(mrg_fasta, <span class="at">sep=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)) <span class="sc">%&gt;%</span> <span class="fu">paste</span>(<span class="at">collapse=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
-<span id="cb36-558"><a href="#cb36-558" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(mrg_fasta_out, <span class="fu">file.path</span>(data_dir, <span class="st">"brumfield-putative-viral.fasta"</span>))</span>
-<span id="cb36-559"><a href="#cb36-559" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-560"><a href="#cb36-560" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-563"><a href="#cb36-563" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-564"><a href="#cb36-564" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: process-blast-data</span></span>
-<span id="cb36-565"><a href="#cb36-565" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
-<span id="cb36-566"><a href="#cb36-566" aria-hidden="true" tabindex="-1"></a><span class="co"># Import BLAST results</span></span>
-<span id="cb36-567"><a href="#cb36-567" aria-hidden="true" tabindex="-1"></a>blast_results_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"blast/brumfield-putative-viral.blast.gz"</span>)</span>
-<span id="cb36-568"><a href="#cb36-568" aria-hidden="true" tabindex="-1"></a>blast_cols <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"qseqid"</span>, <span class="st">"sseqid"</span>, <span class="st">"sgi"</span>, <span class="st">"staxid"</span>, <span class="st">"qlen"</span>, <span class="st">"evalue"</span>, <span class="st">"bitscore"</span>, <span class="st">"qcovs"</span>, <span class="st">"length"</span>, <span class="st">"pident"</span>, <span class="st">"mismatch"</span>, <span class="st">"gapopen"</span>, <span class="st">"sstrand"</span>, <span class="st">"qstart"</span>, <span class="st">"qend"</span>, <span class="st">"sstart"</span>, <span class="st">"send"</span>)</span>
-<span id="cb36-569"><a href="#cb36-569" aria-hidden="true" tabindex="-1"></a>blast_results <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(blast_results_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>, </span>
-<span id="cb36-570"><a href="#cb36-570" aria-hidden="true" tabindex="-1"></a>                          <span class="at">col_names =</span> blast_cols, <span class="at">col_types =</span> <span class="fu">cols</span>(<span class="at">.default=</span><span class="st">"c"</span>))</span>
-<span id="cb36-571"><a href="#cb36-571" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-572"><a href="#cb36-572" aria-hidden="true" tabindex="-1"></a><span class="co"># Add viral status</span></span>
-<span id="cb36-573"><a href="#cb36-573" aria-hidden="true" tabindex="-1"></a>blast_results_viral <span class="ot">&lt;-</span> <span class="fu">mutate</span>(blast_results, <span class="at">viral =</span> staxid <span class="sc">%in%</span> viral_taxa<span class="sc">$</span>taxid)</span>
-<span id="cb36-574"><a href="#cb36-574" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-575"><a href="#cb36-575" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter for best hit for each query/subject, then rank hits for each query</span></span>
-<span id="cb36-576"><a href="#cb36-576" aria-hidden="true" tabindex="-1"></a>blast_results_best <span class="ot">&lt;-</span> blast_results_viral <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(qseqid, staxid) <span class="sc">%&gt;%</span> </span>
-<span id="cb36-577"><a href="#cb36-577" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(bitscore <span class="sc">==</span> <span class="fu">max</span>(bitscore)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-578"><a href="#cb36-578" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(length <span class="sc">==</span> <span class="fu">max</span>(length)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>)</span>
-<span id="cb36-579"><a href="#cb36-579" aria-hidden="true" tabindex="-1"></a>blast_results_ranked <span class="ot">&lt;-</span> blast_results_best <span class="sc">%&gt;%</span> </span>
-<span id="cb36-580"><a href="#cb36-580" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(qseqid) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">rank =</span> <span class="fu">dense_rank</span>(<span class="fu">desc</span>(bitscore)))</span>
-<span id="cb36-581"><a href="#cb36-581" aria-hidden="true" tabindex="-1"></a>blast_results_highrank <span class="ot">&lt;-</span> blast_results_ranked <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">&lt;=</span> <span class="dv">5</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-582"><a href="#cb36-582" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">read_pair =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="sc">-</span><span class="dv">1</span>), </span>
-<span id="cb36-583"><a href="#cb36-583" aria-hidden="true" tabindex="-1"></a>         <span class="at">seq_id =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="dv">1</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-584"><a href="#cb36-584" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">bitscore =</span> <span class="fu">as.numeric</span>(bitscore))</span>
-<span id="cb36-585"><a href="#cb36-585" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-586"><a href="#cb36-586" aria-hidden="true" tabindex="-1"></a><span class="co"># Summarize by read pair and taxid</span></span>
-<span id="cb36-587"><a href="#cb36-587" aria-hidden="true" tabindex="-1"></a>blast_results_paired <span class="ot">&lt;-</span> blast_results_highrank <span class="sc">%&gt;%</span></span>
-<span id="cb36-588"><a href="#cb36-588" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id, staxid, viral) <span class="sc">%&gt;%</span></span>
-<span id="cb36-589"><a href="#cb36-589" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">bitscore_max =</span> <span class="fu">max</span>(bitscore), <span class="at">bitscore_min =</span> <span class="fu">min</span>(bitscore),</span>
-<span id="cb36-590"><a href="#cb36-590" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads =</span> <span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-591"><a href="#cb36-591" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_full =</span> viral <span class="sc">&amp;</span> n_reads <span class="sc">==</span> <span class="dv">2</span>)</span>
-<span id="cb36-592"><a href="#cb36-592" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-593"><a href="#cb36-593" aria-hidden="true" tabindex="-1"></a><span class="co"># Compare to Kraken &amp; Bowtie assignments</span></span>
-<span id="cb36-594"><a href="#cb36-594" aria-hidden="true" tabindex="-1"></a>mrg_assign <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, seq_id, taxid, assigned_taxid, adj_score_max, na_type)</span>
-<span id="cb36-595"><a href="#cb36-595" aria-hidden="true" tabindex="-1"></a>blast_results_assign <span class="ot">&lt;-</span> <span class="fu">left_join</span>(blast_results_paired, mrg_assign, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-596"><a href="#cb36-596" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">taxid_match_bowtie =</span> (staxid <span class="sc">==</span> taxid),</span>
-<span id="cb36-597"><a href="#cb36-597" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_kraken =</span> (staxid <span class="sc">==</span> assigned_taxid),</span>
-<span id="cb36-598"><a href="#cb36-598" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_any =</span> taxid_match_bowtie <span class="sc">|</span> taxid_match_kraken)</span>
-<span id="cb36-599"><a href="#cb36-599" aria-hidden="true" tabindex="-1"></a>blast_results_out <span class="ot">&lt;-</span> blast_results_assign <span class="sc">%&gt;%</span></span>
-<span id="cb36-600"><a href="#cb36-600" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id) <span class="sc">%&gt;%</span></span>
-<span id="cb36-601"><a href="#cb36-601" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">viral_status =</span> <span class="fu">ifelse</span>(<span class="fu">any</span>(viral_full), <span class="dv">2</span>,</span>
-<span id="cb36-602"><a href="#cb36-602" aria-hidden="true" tabindex="-1"></a>                                  <span class="fu">ifelse</span>(<span class="fu">any</span>(taxid_match_any), <span class="dv">2</span>,</span>
-<span id="cb36-603"><a href="#cb36-603" aria-hidden="true" tabindex="-1"></a>                                             <span class="fu">ifelse</span>(<span class="fu">any</span>(viral), <span class="dv">1</span>, <span class="dv">0</span>))),</span>
-<span id="cb36-604"><a href="#cb36-604" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
-<span id="cb36-605"><a href="#cb36-605" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-606"><a href="#cb36-606" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-609"><a href="#cb36-609" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-610"><a href="#cb36-610" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-blast-results</span></span>
-<span id="cb36-611"><a href="#cb36-611" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
-<span id="cb36-612"><a href="#cb36-612" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
-<span id="cb36-613"><a href="#cb36-613" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-614"><a href="#cb36-614" aria-hidden="true" tabindex="-1"></a><span class="co"># Merge BLAST results with unenriched read data</span></span>
-<span id="cb36-615"><a href="#cb36-615" aria-hidden="true" tabindex="-1"></a>mrg_blast <span class="ot">&lt;-</span> <span class="fu">full_join</span>(mrg, blast_results_out, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-616"><a href="#cb36-616" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_status =</span> <span class="fu">replace_na</span>(viral_status, <span class="dv">0</span>),</span>
-<span id="cb36-617"><a href="#cb36-617" aria-hidden="true" tabindex="-1"></a>         <span class="at">viral_status_out =</span> <span class="fu">ifelse</span>(viral_status <span class="sc">==</span> <span class="dv">0</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>))</span>
-<span id="cb36-618"><a href="#cb36-618" aria-hidden="true" tabindex="-1"></a>mrg_blast_rna <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(na_type <span class="sc">==</span> <span class="st">"RNA"</span>)</span>
-<span id="cb36-619"><a href="#cb36-619" aria-hidden="true" tabindex="-1"></a>mrg_blast_dna <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(na_type <span class="sc">==</span> <span class="st">"DNA"</span>)</span>
-<span id="cb36-620"><a href="#cb36-620" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-621"><a href="#cb36-621" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot RNA</span></span>
-<span id="cb36-622"><a href="#cb36-622" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_rna <span class="ot">&lt;-</span> mrg_blast_rna <span class="sc">%&gt;%</span></span>
-<span id="cb36-623"><a href="#cb36-623" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev, <span class="at">color=</span>viral_status_out)) <span class="sc">+</span></span>
-<span id="cb36-624"><a href="#cb36-624" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span></span>
-<span id="cb36-625"><a href="#cb36-625" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb36-626"><a href="#cb36-626" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb36-627"><a href="#cb36-627" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Viral status"</span>) <span class="sc">+</span></span>
-<span id="cb36-628"><a href="#cb36-628" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(viral_status_out<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
-<span id="cb36-629"><a href="#cb36-629" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"RNA"</span>) <span class="sc">+</span> </span>
-<span id="cb36-630"><a href="#cb36-630" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>, <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="dv">2</span>), <span class="at">hjust=</span><span class="dv">0</span>))</span>
-<span id="cb36-631"><a href="#cb36-631" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_rna</span>
-<span id="cb36-632"><a href="#cb36-632" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-633"><a href="#cb36-633" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot DNA</span></span>
-<span id="cb36-634"><a href="#cb36-634" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_dna <span class="ot">&lt;-</span> mrg_blast_dna <span class="sc">%&gt;%</span></span>
-<span id="cb36-635"><a href="#cb36-635" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev, <span class="at">color=</span>viral_status_out)) <span class="sc">+</span></span>
-<span id="cb36-636"><a href="#cb36-636" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span></span>
-<span id="cb36-637"><a href="#cb36-637" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb36-638"><a href="#cb36-638" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb36-639"><a href="#cb36-639" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Viral status"</span>) <span class="sc">+</span></span>
-<span id="cb36-640"><a href="#cb36-640" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(viral_status_out<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
-<span id="cb36-641"><a href="#cb36-641" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"DNA"</span>) <span class="sc">+</span> </span>
-<span id="cb36-642"><a href="#cb36-642" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>, <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="dv">2</span>), <span class="at">hjust=</span><span class="dv">0</span>))</span>
-<span id="cb36-643"><a href="#cb36-643" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_dna</span>
-<span id="cb36-644"><a href="#cb36-644" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-645"><a href="#cb36-645" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-648"><a href="#cb36-648" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-649"><a href="#cb36-649" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-blast-histogram</span></span>
-<span id="cb36-650"><a href="#cb36-650" aria-hidden="true" tabindex="-1"></a>g_hist_1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg_blast, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>,<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>viral_status_out, <span class="at">scales =</span> <span class="st">"free_y"</span>) <span class="sc">+</span> <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Dark2"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>)</span>
-<span id="cb36-651"><a href="#cb36-651" aria-hidden="true" tabindex="-1"></a>g_hist_1</span>
-<span id="cb36-652"><a href="#cb36-652" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-653"><a href="#cb36-653" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-654"><a href="#cb36-654" aria-hidden="true" tabindex="-1"></a>These results look very good on visual inspection, and indeed precision and sensitivity are both very high. For a disjunctive score threshold of 20, my updated workflow achieves an F1 score of 99.0% for RNA sequences and 99.2% for DNA sequences; more than high enough to continue on to human viral analysis.</span>
-<span id="cb36-655"><a href="#cb36-655" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-658"><a href="#cb36-658" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-659"><a href="#cb36-659" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-f1</span></span>
-<span id="cb36-660"><a href="#cb36-660" aria-hidden="true" tabindex="-1"></a>test_sens_spec <span class="ot">&lt;-</span> <span class="cf">function</span>(tab, score_threshold, conjunctive, include_special){</span>
-<span id="cb36-661"><a href="#cb36-661" aria-hidden="true" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span>include_special) tab <span class="ot">&lt;-</span> <span class="fu">filter</span>(tab, viral_status_out <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"TRUE"</span>, <span class="st">"FALSE"</span>))</span>
-<span id="cb36-662"><a href="#cb36-662" aria-hidden="true" tabindex="-1"></a>  tab_retained <span class="ot">&lt;-</span> tab <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(</span>
-<span id="cb36-663"><a href="#cb36-663" aria-hidden="true" tabindex="-1"></a>    <span class="at">conjunctive =</span> conjunctive,</span>
-<span id="cb36-664"><a href="#cb36-664" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score_conjunctive =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">&amp;</span> adj_score_rev <span class="sc">&gt;</span> score_threshold), </span>
-<span id="cb36-665"><a href="#cb36-665" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score_disjunctive =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">|</span> adj_score_rev <span class="sc">&gt;</span> score_threshold),</span>
-<span id="cb36-666"><a href="#cb36-666" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score =</span> <span class="fu">ifelse</span>(conjunctive, retain_score_conjunctive, retain_score_disjunctive),</span>
-<span id="cb36-667"><a href="#cb36-667" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> retain_score) <span class="sc">%&gt;%</span></span>
-<span id="cb36-668"><a href="#cb36-668" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(viral_status_out, retain) <span class="sc">%&gt;%</span> count</span>
-<span id="cb36-669"><a href="#cb36-669" aria-hidden="true" tabindex="-1"></a>  pos_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
-<span id="cb36-670"><a href="#cb36-670" aria-hidden="true" tabindex="-1"></a>  pos_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
-<span id="cb36-671"><a href="#cb36-671" aria-hidden="true" tabindex="-1"></a>  neg_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
-<span id="cb36-672"><a href="#cb36-672" aria-hidden="true" tabindex="-1"></a>  neg_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
-<span id="cb36-673"><a href="#cb36-673" aria-hidden="true" tabindex="-1"></a>  sensitivity <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> neg_fls)</span>
-<span id="cb36-674"><a href="#cb36-674" aria-hidden="true" tabindex="-1"></a>  specificity <span class="ot">&lt;-</span> neg_tru <span class="sc">/</span> (neg_tru <span class="sc">+</span> pos_fls)</span>
-<span id="cb36-675"><a href="#cb36-675" aria-hidden="true" tabindex="-1"></a>  precision   <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> pos_fls)</span>
-<span id="cb36-676"><a href="#cb36-676" aria-hidden="true" tabindex="-1"></a>  f1 <span class="ot">&lt;-</span> <span class="dv">2</span> <span class="sc">*</span> precision <span class="sc">*</span> sensitivity <span class="sc">/</span> (precision <span class="sc">+</span> sensitivity)</span>
-<span id="cb36-677"><a href="#cb36-677" aria-hidden="true" tabindex="-1"></a>  out <span class="ot">&lt;-</span> <span class="fu">tibble</span>(<span class="at">threshold=</span>score_threshold, <span class="at">include_special =</span> include_special, </span>
-<span id="cb36-678"><a href="#cb36-678" aria-hidden="true" tabindex="-1"></a>                <span class="at">conjunctive =</span> conjunctive, <span class="at">sensitivity=</span>sensitivity, </span>
-<span id="cb36-679"><a href="#cb36-679" aria-hidden="true" tabindex="-1"></a>                <span class="at">specificity=</span>specificity, <span class="at">precision=</span>precision, <span class="at">f1=</span>f1)</span>
-<span id="cb36-680"><a href="#cb36-680" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(out)</span>
-<span id="cb36-681"><a href="#cb36-681" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb36-682"><a href="#cb36-682" aria-hidden="true" tabindex="-1"></a>range_f1 <span class="ot">&lt;-</span> <span class="cf">function</span>(intab, inc_special, <span class="at">inrange=</span><span class="dv">15</span><span class="sc">:</span><span class="dv">45</span>){</span>
-<span id="cb36-683"><a href="#cb36-683" aria-hidden="true" tabindex="-1"></a>  tss <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(test_sens_spec, <span class="at">tab=</span>intab, <span class="at">include_special=</span>inc_special)</span>
-<span id="cb36-684"><a href="#cb36-684" aria-hidden="true" tabindex="-1"></a>  stats_conj <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss, <span class="at">conjunctive=</span><span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
-<span id="cb36-685"><a href="#cb36-685" aria-hidden="true" tabindex="-1"></a>  stats_disj <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss, <span class="at">conjunctive=</span><span class="cn">FALSE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
-<span id="cb36-686"><a href="#cb36-686" aria-hidden="true" tabindex="-1"></a>  stats_all <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(stats_conj, stats_disj) <span class="sc">%&gt;%</span></span>
-<span id="cb36-687"><a href="#cb36-687" aria-hidden="true" tabindex="-1"></a>    <span class="fu">pivot_longer</span>(<span class="sc">!</span>(threshold<span class="sc">:</span>conjunctive), <span class="at">names_to=</span><span class="st">"metric"</span>, <span class="at">values_to=</span><span class="st">"value"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-688"><a href="#cb36-688" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">conj_label =</span> <span class="fu">ifelse</span>(conjunctive, <span class="st">"Conjunctive"</span>, <span class="st">"Disjunctive"</span>))</span>
-<span id="cb36-689"><a href="#cb36-689" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(stats_all)</span>
-<span id="cb36-690"><a href="#cb36-690" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb36-691"><a href="#cb36-691" aria-hidden="true" tabindex="-1"></a>inc_special <span class="ot">&lt;-</span> <span class="cn">FALSE</span></span>
-<span id="cb36-692"><a href="#cb36-692" aria-hidden="true" tabindex="-1"></a>stats_rna <span class="ot">&lt;-</span> <span class="fu">range_f1</span>(mrg_blast_rna, inc_special) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">na_type =</span> <span class="st">"RNA"</span>)</span>
-<span id="cb36-693"><a href="#cb36-693" aria-hidden="true" tabindex="-1"></a>stats_dna <span class="ot">&lt;-</span> <span class="fu">range_f1</span>(mrg_blast_dna, inc_special) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">na_type =</span> <span class="st">"DNA"</span>)</span>
-<span id="cb36-694"><a href="#cb36-694" aria-hidden="true" tabindex="-1"></a>stats_0 <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(stats_rna, stats_dna) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">attempt=</span><span class="dv">0</span>)</span>
-<span id="cb36-695"><a href="#cb36-695" aria-hidden="true" tabindex="-1"></a>threshold_opt_0 <span class="ot">&lt;-</span> stats_0 <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(conj_label,attempt,na_type) <span class="sc">%&gt;%</span> </span>
-<span id="cb36-696"><a href="#cb36-696" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(metric <span class="sc">==</span> <span class="st">"f1"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-697"><a href="#cb36-697" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="fu">max</span>(value)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(threshold <span class="sc">==</span> <span class="fu">min</span>(threshold))</span>
-<span id="cb36-698"><a href="#cb36-698" aria-hidden="true" tabindex="-1"></a>g_stats_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(stats_0, <span class="fu">aes</span>(<span class="at">x=</span>threshold, <span class="at">y=</span>value, <span class="at">color=</span>metric)) <span class="sc">+</span></span>
-<span id="cb36-699"><a href="#cb36-699" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">data =</span> threshold_opt_0, <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">xintercept=</span>threshold),</span>
-<span id="cb36-700"><a href="#cb36-700" aria-hidden="true" tabindex="-1"></a>             <span class="at">color =</span> <span class="st">"red"</span>, <span class="at">linetype =</span> <span class="st">"dashed"</span>) <span class="sc">+</span></span>
-<span id="cb36-701"><a href="#cb36-701" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
-<span id="cb36-702"><a href="#cb36-702" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"Value"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb36-703"><a href="#cb36-703" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Threshold"</span>, <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb36-704"><a href="#cb36-704" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette=</span><span class="st">"Set3"</span>) <span class="sc">+</span></span>
-<span id="cb36-705"><a href="#cb36-705" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>conj_label) <span class="sc">+</span></span>
-<span id="cb36-706"><a href="#cb36-706" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
-<span id="cb36-707"><a href="#cb36-707" aria-hidden="true" tabindex="-1"></a>g_stats_0</span>
-<span id="cb36-708"><a href="#cb36-708" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-709"><a href="#cb36-709" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-710"><a href="#cb36-710" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting virus reads: analysis</span></span>
-<span id="cb36-711"><a href="#cb36-711" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-714"><a href="#cb36-714" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-715"><a href="#cb36-715" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: count-hv-reads</span></span>
-<span id="cb36-716"><a href="#cb36-716" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-717"><a href="#cb36-717" aria-hidden="true" tabindex="-1"></a><span class="co"># Get raw read counts</span></span>
-<span id="cb36-718"><a href="#cb36-718" aria-hidden="true" tabindex="-1"></a>read_counts_raw <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span></span>
-<span id="cb36-719"><a href="#cb36-719" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, date, na_type, <span class="at">n_reads_raw =</span> n_read_pairs)</span>
-<span id="cb36-720"><a href="#cb36-720" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-721"><a href="#cb36-721" aria-hidden="true" tabindex="-1"></a><span class="co"># Get HV read counts</span></span>
-<span id="cb36-722"><a href="#cb36-722" aria-hidden="true" tabindex="-1"></a>mrg_hv <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">hv_status =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> adj_score_max <span class="sc">&gt;=</span> <span class="dv">20</span>)</span>
-<span id="cb36-723"><a href="#cb36-723" aria-hidden="true" tabindex="-1"></a>read_counts_hv <span class="ot">&lt;-</span> mrg_hv <span class="sc">%&gt;%</span> <span class="fu">filter</span>(hv_status) <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">count</span>(<span class="at">name=</span><span class="st">"n_reads_hv"</span>)</span>
-<span id="cb36-724"><a href="#cb36-724" aria-hidden="true" tabindex="-1"></a>read_counts <span class="ot">&lt;-</span> read_counts_raw <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(read_counts_hv, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-725"><a href="#cb36-725" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads_hv =</span> <span class="fu">replace_na</span>(n_reads_hv, <span class="dv">0</span>))</span>
-<span id="cb36-726"><a href="#cb36-726" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-727"><a href="#cb36-727" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate</span></span>
-<span id="cb36-728"><a href="#cb36-728" aria-hidden="true" tabindex="-1"></a>read_counts_total <span class="ot">&lt;-</span> read_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type) <span class="sc">%&gt;%</span></span>
-<span id="cb36-729"><a href="#cb36-729" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_raw =</span> <span class="fu">sum</span>(n_reads_raw),</span>
-<span id="cb36-730"><a href="#cb36-730" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-731"><a href="#cb36-731" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample=</span> <span class="st">"All samples"</span>, <span class="at">date =</span> <span class="st">"All dates"</span>)</span>
-<span id="cb36-732"><a href="#cb36-732" aria-hidden="true" tabindex="-1"></a>read_counts_agg <span class="ot">&lt;-</span> read_counts <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(date) <span class="sc">%&gt;%</span> </span>
-<span id="cb36-733"><a href="#cb36-733" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">date =</span> <span class="fu">as.character</span>(date)) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(sample) <span class="sc">%&gt;%</span></span>
-<span id="cb36-734"><a href="#cb36-734" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bind_rows</span>(read_counts_total) <span class="sc">%&gt;%</span> </span>
-<span id="cb36-735"><a href="#cb36-735" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> <span class="fu">fct_inorder</span>(sample),</span>
-<span id="cb36-736"><a href="#cb36-736" aria-hidden="true" tabindex="-1"></a>         <span class="at">date =</span> <span class="fu">fct_inorder</span>(date),</span>
-<span id="cb36-737"><a href="#cb36-737" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span>n_reads_raw)</span>
-<span id="cb36-738"><a href="#cb36-738" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-739"><a href="#cb36-739" aria-hidden="true" tabindex="-1"></a><span class="co"># Get old counts</span></span>
-<span id="cb36-740"><a href="#cb36-740" aria-hidden="true" tabindex="-1"></a>n_hv_reads_old <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">691</span>, <span class="dv">121</span>, <span class="dv">18</span>, <span class="dv">224</span>, <span class="dv">2</span>, <span class="dv">26</span>, <span class="dv">6</span>, <span class="dv">21</span>, <span class="dv">4</span>, <span class="dv">29</span>, <span class="dv">12</span>, <span class="dv">22</span>)</span>
-<span id="cb36-741"><a href="#cb36-741" aria-hidden="true" tabindex="-1"></a>n_hv_reads_old[<span class="dv">13</span>] <span class="ot">&lt;-</span> <span class="fu">sum</span>(n_hv_reads_old[<span class="fu">which</span>(read_counts<span class="sc">$</span>na_type<span class="sc">==</span><span class="st">"RNA"</span>)])</span>
-<span id="cb36-742"><a href="#cb36-742" aria-hidden="true" tabindex="-1"></a>n_hv_reads_old[<span class="dv">14</span>] <span class="ot">&lt;-</span> <span class="fu">sum</span>(n_hv_reads_old[<span class="fu">which</span>(read_counts<span class="sc">$</span>na_type<span class="sc">==</span><span class="st">"DNA"</span>)])</span>
-<span id="cb36-743"><a href="#cb36-743" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-744"><a href="#cb36-744" aria-hidden="true" tabindex="-1"></a>read_counts_comp <span class="ot">&lt;-</span> read_counts_agg <span class="sc">%&gt;%</span></span>
-<span id="cb36-745"><a href="#cb36-745" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads_hv_old =</span> n_hv_reads_old,</span>
-<span id="cb36-746"><a href="#cb36-746" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_hv_old =</span> n_reads_hv_old<span class="sc">/</span>n_reads_raw)</span>
-<span id="cb36-747"><a href="#cb36-747" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-748"><a href="#cb36-748" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-749"><a href="#cb36-749" aria-hidden="true" tabindex="-1"></a>Applying a disjunctive cutoff at S=20 identifies 13,809 RNA reads and 66 DNA reads as human-viral. This gives an overall relative HV abundance of $5.00 \times 10^{-5}$ for RNA reads and $3.66 \times 10^{-7}$ for DNA reads. In contrast, the overall HV in the public dashboard is $3.93 \times 10^{-6}$ for RNA reads and $4.54 \times 10^{-7}$; the measured HV has thus increased 12.7x for RNA reads, but *decreased* slightly for DNA reads.</span>
-<span id="cb36-750"><a href="#cb36-750" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-753"><a href="#cb36-753" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-754"><a href="#cb36-754" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-ra</span></span>
-<span id="cb36-755"><a href="#cb36-755" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
-<span id="cb36-756"><a href="#cb36-756" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize</span></span>
-<span id="cb36-757"><a href="#cb36-757" aria-hidden="true" tabindex="-1"></a>g_phv_agg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(read_counts_agg, <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">color=</span>na_type)) <span class="sc">+</span></span>
-<span id="cb36-758"><a href="#cb36-758" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">y=</span>p_reads_hv)) <span class="sc">+</span></span>
-<span id="cb36-759"><a href="#cb36-759" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_log10</span>(<span class="st">"Relative abundance of human virus reads"</span>) <span class="sc">+</span></span>
-<span id="cb36-760"><a href="#cb36-760" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span> theme_kit</span>
-<span id="cb36-761"><a href="#cb36-761" aria-hidden="true" tabindex="-1"></a>g_phv_agg</span>
-<span id="cb36-762"><a href="#cb36-762" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-763"><a href="#cb36-763" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-764"><a href="#cb36-764" aria-hidden="true" tabindex="-1"></a>Digging into individual viruses, we see that fecal-oral viruses predominate, especially *Mamastrovirus*, *Rotavirus*, *Salivirus*, and fecal-oral *Enterovirus* species (especially *Enterovirus C*, which includes poliovirus):</span>
-<span id="cb36-765"><a href="#cb36-765" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-768"><a href="#cb36-768" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-769"><a href="#cb36-769" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: raise-hv-taxa</span></span>
-<span id="cb36-770"><a href="#cb36-770" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-771"><a href="#cb36-771" aria-hidden="true" tabindex="-1"></a><span class="co"># Get viral taxon names for putative HV reads</span></span>
-<span id="cb36-772"><a href="#cb36-772" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">249588</span>] <span class="ot">&lt;-</span> <span class="st">"Mamastrovirus"</span></span>
-<span id="cb36-773"><a href="#cb36-773" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">194960</span>] <span class="ot">&lt;-</span> <span class="st">"Kobuvirus"</span></span>
-<span id="cb36-774"><a href="#cb36-774" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">688449</span>] <span class="ot">&lt;-</span> <span class="st">"Salivirus"</span></span>
-<span id="cb36-775"><a href="#cb36-775" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">585893</span>] <span class="ot">&lt;-</span> <span class="st">"Picobirnaviridae"</span></span>
-<span id="cb36-776"><a href="#cb36-776" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333922</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus"</span></span>
-<span id="cb36-777"><a href="#cb36-777" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-778"><a href="#cb36-778" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334207</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 3"</span></span>
-<span id="cb36-779"><a href="#cb36-779" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">369960</span>] <span class="ot">&lt;-</span> <span class="st">"Porcine type-C oncovirus"</span></span>
-<span id="cb36-780"><a href="#cb36-780" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333924</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 2"</span></span>
-<span id="cb36-781"><a href="#cb36-781" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-782"><a href="#cb36-782" aria-hidden="true" tabindex="-1"></a>mrg_hv_named <span class="ot">&lt;-</span> mrg_hv <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
-<span id="cb36-783"><a href="#cb36-783" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-784"><a href="#cb36-784" aria-hidden="true" tabindex="-1"></a><span class="co"># Discover viral species &amp; genera for HV reads</span></span>
-<span id="cb36-785"><a href="#cb36-785" aria-hidden="true" tabindex="-1"></a>raise_rank <span class="ot">&lt;-</span> <span class="cf">function</span>(read_db, taxid_db, <span class="at">out_rank =</span> <span class="st">"species"</span>, <span class="at">verbose =</span> <span class="cn">FALSE</span>){</span>
-<span id="cb36-786"><a href="#cb36-786" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Get higher ranks than search rank</span></span>
-<span id="cb36-787"><a href="#cb36-787" aria-hidden="true" tabindex="-1"></a>  ranks <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"subspecies"</span>, <span class="st">"species"</span>, <span class="st">"subgenus"</span>, <span class="st">"genus"</span>, <span class="st">"subfamily"</span>, <span class="st">"family"</span>, <span class="st">"suborder"</span>, <span class="st">"order"</span>, <span class="st">"class"</span>, <span class="st">"subphylum"</span>, <span class="st">"phylum"</span>, <span class="st">"kingdom"</span>, <span class="st">"superkingdom"</span>)</span>
-<span id="cb36-788"><a href="#cb36-788" aria-hidden="true" tabindex="-1"></a>  rank_match <span class="ot">&lt;-</span> <span class="fu">which.max</span>(ranks <span class="sc">==</span> out_rank)</span>
-<span id="cb36-789"><a href="#cb36-789" aria-hidden="true" tabindex="-1"></a>  high_ranks <span class="ot">&lt;-</span> ranks[rank_match<span class="sc">:</span><span class="fu">length</span>(ranks)]</span>
-<span id="cb36-790"><a href="#cb36-790" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Merge read DB and taxid DB</span></span>
-<span id="cb36-791"><a href="#cb36-791" aria-hidden="true" tabindex="-1"></a>  reads <span class="ot">&lt;-</span> read_db <span class="sc">%&gt;%</span> <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
-<span id="cb36-792"><a href="#cb36-792" aria-hidden="true" tabindex="-1"></a>    <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
-<span id="cb36-793"><a href="#cb36-793" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Extract sequences that are already at appropriate rank</span></span>
-<span id="cb36-794"><a href="#cb36-794" aria-hidden="true" tabindex="-1"></a>  reads_rank <span class="ot">&lt;-</span> <span class="fu">filter</span>(reads, rank <span class="sc">==</span> out_rank)</span>
-<span id="cb36-795"><a href="#cb36-795" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Drop sequences at a higher rank and return unclassified sequences</span></span>
-<span id="cb36-796"><a href="#cb36-796" aria-hidden="true" tabindex="-1"></a>  reads_norank <span class="ot">&lt;-</span> reads <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">!=</span> out_rank, <span class="sc">!</span>rank <span class="sc">%in%</span> high_ranks, <span class="sc">!</span><span class="fu">is.na</span>(taxid))</span>
-<span id="cb36-797"><a href="#cb36-797" aria-hidden="true" tabindex="-1"></a>  <span class="cf">while</span>(<span class="fu">nrow</span>(reads_norank) <span class="sc">&gt;</span> <span class="dv">0</span>){ <span class="co"># As long as there are unclassified sequences...</span></span>
-<span id="cb36-798"><a href="#cb36-798" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Promote read taxids and re-merge with taxid DB, then re-classify and filter</span></span>
-<span id="cb36-799"><a href="#cb36-799" aria-hidden="true" tabindex="-1"></a>    reads_remaining <span class="ot">&lt;-</span> reads_norank <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">taxid =</span> parent_taxid) <span class="sc">%&gt;%</span></span>
-<span id="cb36-800"><a href="#cb36-800" aria-hidden="true" tabindex="-1"></a>      <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
-<span id="cb36-801"><a href="#cb36-801" aria-hidden="true" tabindex="-1"></a>      <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
-<span id="cb36-802"><a href="#cb36-802" aria-hidden="true" tabindex="-1"></a>    reads_rank <span class="ot">&lt;-</span> reads_remaining <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> out_rank) <span class="sc">%&gt;%</span></span>
-<span id="cb36-803"><a href="#cb36-803" aria-hidden="true" tabindex="-1"></a>      <span class="fu">bind_rows</span>(reads_rank)</span>
-<span id="cb36-804"><a href="#cb36-804" aria-hidden="true" tabindex="-1"></a>    reads_norank <span class="ot">&lt;-</span> reads_remaining <span class="sc">%&gt;%</span></span>
-<span id="cb36-805"><a href="#cb36-805" aria-hidden="true" tabindex="-1"></a>      <span class="fu">filter</span>(rank <span class="sc">!=</span> out_rank, <span class="sc">!</span>rank <span class="sc">%in%</span> high_ranks, <span class="sc">!</span><span class="fu">is.na</span>(taxid))</span>
-<span id="cb36-806"><a href="#cb36-806" aria-hidden="true" tabindex="-1"></a>  }</span>
-<span id="cb36-807"><a href="#cb36-807" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Finally, extract and append reads that were excluded during the process</span></span>
-<span id="cb36-808"><a href="#cb36-808" aria-hidden="true" tabindex="-1"></a>  reads_dropped <span class="ot">&lt;-</span> reads <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span>seq_id <span class="sc">%in%</span> reads_rank<span class="sc">$</span>seq_id)</span>
-<span id="cb36-809"><a href="#cb36-809" aria-hidden="true" tabindex="-1"></a>  reads_out <span class="ot">&lt;-</span> reads_rank <span class="sc">%&gt;%</span> <span class="fu">bind_rows</span>(reads_dropped) <span class="sc">%&gt;%</span></span>
-<span id="cb36-810"><a href="#cb36-810" aria-hidden="true" tabindex="-1"></a>    <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
-<span id="cb36-811"><a href="#cb36-811" aria-hidden="true" tabindex="-1"></a>    <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
-<span id="cb36-812"><a href="#cb36-812" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(reads_out)</span>
-<span id="cb36-813"><a href="#cb36-813" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb36-814"><a href="#cb36-814" aria-hidden="true" tabindex="-1"></a>hv_reads_species <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"species"</span>)</span>
-<span id="cb36-815"><a href="#cb36-815" aria-hidden="true" tabindex="-1"></a>hv_reads_genus <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"genus"</span>)</span>
-<span id="cb36-816"><a href="#cb36-816" aria-hidden="true" tabindex="-1"></a>hv_reads_family <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"family"</span>)</span>
-<span id="cb36-817"><a href="#cb36-817" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-818"><a href="#cb36-818" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-821"><a href="#cb36-821" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-822"><a href="#cb36-822" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-families</span></span>
-<span id="cb36-823"><a href="#cb36-823" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-824"><a href="#cb36-824" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral family</span></span>
-<span id="cb36-825"><a href="#cb36-825" aria-hidden="true" tabindex="-1"></a>hv_family_counts <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
-<span id="cb36-826"><a href="#cb36-826" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
-<span id="cb36-827"><a href="#cb36-827" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-828"><a href="#cb36-828" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type) <span class="sc">%&gt;%</span></span>
-<span id="cb36-829"><a href="#cb36-829" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
-<span id="cb36-830"><a href="#cb36-830" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-831"><a href="#cb36-831" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
-<span id="cb36-832"><a href="#cb36-832" aria-hidden="true" tabindex="-1"></a>hv_family_major_tab <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
-<span id="cb36-833"><a href="#cb36-833" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-834"><a href="#cb36-834" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> <span class="fl">0.01</span>)</span>
-<span id="cb36-835"><a href="#cb36-835" aria-hidden="true" tabindex="-1"></a>hv_family_counts_major <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span></span>
-<span id="cb36-836"><a href="#cb36-836" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_family_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-837"><a href="#cb36-837" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name_display) <span class="sc">%&gt;%</span></span>
-<span id="cb36-838"><a href="#cb36-838" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
-<span id="cb36-839"><a href="#cb36-839" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-840"><a href="#cb36-840" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
-<span id="cb36-841"><a href="#cb36-841" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_family_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
-<span id="cb36-842"><a href="#cb36-842" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-843"><a href="#cb36-843" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot family composition</span></span>
-<span id="cb36-844"><a href="#cb36-844" aria-hidden="true" tabindex="-1"></a>palette <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="st">"#AAAAAA"</span>)</span>
-<span id="cb36-845"><a href="#cb36-845" aria-hidden="true" tabindex="-1"></a>g_hv_families <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(hv_family_counts_major, </span>
-<span id="cb36-846"><a href="#cb36-846" aria-hidden="true" tabindex="-1"></a>                        <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>p_reads_hv, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
-<span id="cb36-847"><a href="#cb36-847" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
-<span id="cb36-848"><a href="#cb36-848" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">name=</span><span class="st">"Viral</span><span class="sc">\n</span><span class="st">family"</span>, <span class="at">values=</span>palette) <span class="sc">+</span></span>
-<span id="cb36-849"><a href="#cb36-849" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>)) <span class="sc">+</span></span>
-<span id="cb36-850"><a href="#cb36-850" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>na_type) <span class="sc">+</span></span>
-<span id="cb36-851"><a href="#cb36-851" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
-<span id="cb36-852"><a href="#cb36-852" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
-<span id="cb36-853"><a href="#cb36-853" aria-hidden="true" tabindex="-1"></a>g_hv_families</span>
-<span id="cb36-854"><a href="#cb36-854" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-855"><a href="#cb36-855" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-858"><a href="#cb36-858" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-859"><a href="#cb36-859" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-genera</span></span>
-<span id="cb36-860"><a href="#cb36-860" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-861"><a href="#cb36-861" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral genus</span></span>
-<span id="cb36-862"><a href="#cb36-862" aria-hidden="true" tabindex="-1"></a>hv_genus_counts <span class="ot">&lt;-</span> hv_reads_genus <span class="sc">%&gt;%</span> </span>
-<span id="cb36-863"><a href="#cb36-863" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
-<span id="cb36-864"><a href="#cb36-864" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-865"><a href="#cb36-865" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type) <span class="sc">%&gt;%</span></span>
-<span id="cb36-866"><a href="#cb36-866" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
-<span id="cb36-867"><a href="#cb36-867" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-868"><a href="#cb36-868" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking genera and group others</span></span>
-<span id="cb36-869"><a href="#cb36-869" aria-hidden="true" tabindex="-1"></a>hv_genus_major_tab <span class="ot">&lt;-</span> hv_genus_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
-<span id="cb36-870"><a href="#cb36-870" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-871"><a href="#cb36-871" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> <span class="fl">0.02</span>)</span>
-<span id="cb36-872"><a href="#cb36-872" aria-hidden="true" tabindex="-1"></a>hv_genus_counts_major <span class="ot">&lt;-</span> hv_genus_counts <span class="sc">%&gt;%</span></span>
-<span id="cb36-873"><a href="#cb36-873" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_genus_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-874"><a href="#cb36-874" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name_display) <span class="sc">%&gt;%</span></span>
-<span id="cb36-875"><a href="#cb36-875" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
-<span id="cb36-876"><a href="#cb36-876" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-877"><a href="#cb36-877" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
-<span id="cb36-878"><a href="#cb36-878" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_genus_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
-<span id="cb36-879"><a href="#cb36-879" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-880"><a href="#cb36-880" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot genus composition</span></span>
-<span id="cb36-881"><a href="#cb36-881" aria-hidden="true" tabindex="-1"></a>palette <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="st">"#AAAAAA"</span>)</span>
-<span id="cb36-882"><a href="#cb36-882" aria-hidden="true" tabindex="-1"></a>g_hv_genera <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(hv_genus_counts_major, </span>
-<span id="cb36-883"><a href="#cb36-883" aria-hidden="true" tabindex="-1"></a>                        <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>p_reads_hv, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
-<span id="cb36-884"><a href="#cb36-884" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
-<span id="cb36-885"><a href="#cb36-885" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">name=</span><span class="st">"Viral</span><span class="sc">\n</span><span class="st">genus"</span>, <span class="at">values=</span>palette) <span class="sc">+</span></span>
-<span id="cb36-886"><a href="#cb36-886" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>)) <span class="sc">+</span></span>
-<span id="cb36-887"><a href="#cb36-887" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>na_type) <span class="sc">+</span></span>
-<span id="cb36-888"><a href="#cb36-888" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
-<span id="cb36-889"><a href="#cb36-889" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
-<span id="cb36-890"><a href="#cb36-890" aria-hidden="true" tabindex="-1"></a>g_hv_genera</span>
-<span id="cb36-891"><a href="#cb36-891" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-892"><a href="#cb36-892" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-895"><a href="#cb36-895" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-896"><a href="#cb36-896" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-species</span></span>
-<span id="cb36-897"><a href="#cb36-897" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-898"><a href="#cb36-898" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral species</span></span>
-<span id="cb36-899"><a href="#cb36-899" aria-hidden="true" tabindex="-1"></a>hv_species_counts <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span> </span>
-<span id="cb36-900"><a href="#cb36-900" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
-<span id="cb36-901"><a href="#cb36-901" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-902"><a href="#cb36-902" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type) <span class="sc">%&gt;%</span></span>
-<span id="cb36-903"><a href="#cb36-903" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
-<span id="cb36-904"><a href="#cb36-904" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-905"><a href="#cb36-905" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking species and group others</span></span>
-<span id="cb36-906"><a href="#cb36-906" aria-hidden="true" tabindex="-1"></a>hv_species_major_tab <span class="ot">&lt;-</span> hv_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span> </span>
-<span id="cb36-907"><a href="#cb36-907" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-908"><a href="#cb36-908" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> <span class="fl">0.05</span>)</span>
-<span id="cb36-909"><a href="#cb36-909" aria-hidden="true" tabindex="-1"></a>hv_species_counts_major <span class="ot">&lt;-</span> hv_species_counts <span class="sc">%&gt;%</span></span>
-<span id="cb36-910"><a href="#cb36-910" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_species_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-911"><a href="#cb36-911" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name_display) <span class="sc">%&gt;%</span></span>
-<span id="cb36-912"><a href="#cb36-912" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
-<span id="cb36-913"><a href="#cb36-913" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-914"><a href="#cb36-914" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
-<span id="cb36-915"><a href="#cb36-915" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_species_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
-<span id="cb36-916"><a href="#cb36-916" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-917"><a href="#cb36-917" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot species composition</span></span>
-<span id="cb36-918"><a href="#cb36-918" aria-hidden="true" tabindex="-1"></a>palette <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="st">"#AAAAAA"</span>)</span>
-<span id="cb36-919"><a href="#cb36-919" aria-hidden="true" tabindex="-1"></a>g_hv_species <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(hv_species_counts_major, </span>
-<span id="cb36-920"><a href="#cb36-920" aria-hidden="true" tabindex="-1"></a>                      <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>p_reads_hv, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
-<span id="cb36-921"><a href="#cb36-921" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
-<span id="cb36-922"><a href="#cb36-922" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">name=</span><span class="st">"Viral</span><span class="sc">\n</span><span class="st">species"</span>, <span class="at">values=</span>palette) <span class="sc">+</span></span>
-<span id="cb36-923"><a href="#cb36-923" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>)) <span class="sc">+</span></span>
-<span id="cb36-924"><a href="#cb36-924" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>na_type) <span class="sc">+</span></span>
-<span id="cb36-925"><a href="#cb36-925" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
-<span id="cb36-926"><a href="#cb36-926" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
-<span id="cb36-927"><a href="#cb36-927" aria-hidden="true" tabindex="-1"></a>g_hv_species</span>
-<span id="cb36-928"><a href="#cb36-928" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-929"><a href="#cb36-929" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-930"><a href="#cb36-930" aria-hidden="true" tabindex="-1"></a>By far the most common virus according to my pipeline (with over 91% of human-viral reads) is *Rotavirus A*; second (with 8%) is *Orthopicobirnavirus hominis*. Together, these two enteric RNA viruses dominate HV RNA reads in all samples. Both appear to be real; at least, BLASTN primarily maps these reads to the same or closely-related viruses to that identified by Bowtie2:</span>
-<span id="cb36-931"><a href="#cb36-931" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-934"><a href="#cb36-934" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-935"><a href="#cb36-935" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-rotavirus</span></span>
-<span id="cb36-936"><a href="#cb36-936" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-937"><a href="#cb36-937" aria-hidden="true" tabindex="-1"></a><span class="co"># Import names db</span></span>
-<span id="cb36-938"><a href="#cb36-938" aria-hidden="true" tabindex="-1"></a>tax_names_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"taxid-names.tsv.gz"</span>)</span>
-<span id="cb36-939"><a href="#cb36-939" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(tax_names_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
-<span id="cb36-940"><a href="#cb36-940" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-941"><a href="#cb36-941" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing names</span></span>
-<span id="cb36-942"><a href="#cb36-942" aria-hidden="true" tabindex="-1"></a>tax_names_new <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
-<span id="cb36-943"><a href="#cb36-943" aria-hidden="true" tabindex="-1"></a>  <span class="at">staxid =</span> <span class="fu">c</span>(<span class="dv">557241</span>, <span class="dv">557245</span>, <span class="dv">557232</span>, <span class="dv">557247</span>, <span class="dv">557242</span>, <span class="dv">557245</span>),</span>
-<span id="cb36-944"><a href="#cb36-944" aria-hidden="true" tabindex="-1"></a>  <span class="at">name =</span> <span class="fu">c</span>(<span class="st">"Canine rotavirus A79-10/G3P[3]"</span>, <span class="st">"Human rotavirus Ro1845"</span>, <span class="st">"Canine rotavirus K9"</span>,</span>
-<span id="cb36-945"><a href="#cb36-945" aria-hidden="true" tabindex="-1"></a>           <span class="st">"Human rotavirus HCR3A"</span>, <span class="st">"Feline rotavirus Cat97/G3P[3]"</span>, <span class="st">"Feline rotavirus Cat2/G3P[9]"</span>)</span>
-<span id="cb36-946"><a href="#cb36-946" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb36-947"><a href="#cb36-947" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(tax_names, tax_names_new)</span>
-<span id="cb36-948"><a href="#cb36-948" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-949"><a href="#cb36-949" aria-hidden="true" tabindex="-1"></a>rota_ids <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid<span class="sc">==</span><span class="dv">28875</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(seq_id)</span>
-<span id="cb36-950"><a href="#cb36-950" aria-hidden="true" tabindex="-1"></a>rota_hits <span class="ot">&lt;-</span> blast_results_paired <span class="sc">%&gt;%</span></span>
-<span id="cb36-951"><a href="#cb36-951" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> rota_ids) <span class="sc">%&gt;%</span></span>
-<span id="cb36-952"><a href="#cb36-952" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">staxid =</span> <span class="fu">as.integer</span>(staxid))</span>
-<span id="cb36-953"><a href="#cb36-953" aria-hidden="true" tabindex="-1"></a>rota_hits_sorted <span class="ot">&lt;-</span> rota_hits <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(staxid) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(n)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-954"><a href="#cb36-954" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names, <span class="at">by=</span><span class="st">"staxid"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-955"><a href="#cb36-955" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">fct_inorder</span>(name))</span>
-<span id="cb36-956"><a href="#cb36-956" aria-hidden="true" tabindex="-1"></a>rota_hits_sorted_head <span class="ot">&lt;-</span> rota_hits_sorted <span class="sc">%&gt;%</span> <span class="fu">head</span>(<span class="dv">10</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-957"><a href="#cb36-957" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name=</span><span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(name)))</span>
-<span id="cb36-958"><a href="#cb36-958" aria-hidden="true" tabindex="-1"></a>g_rota <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(rota_hits_sorted_head,</span>
-<span id="cb36-959"><a href="#cb36-959" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">aes</span>(<span class="at">x=</span>n, <span class="at">y=</span><span class="fu">fct_inorder</span>(name))) <span class="sc">+</span> <span class="fu">geom_col</span>() <span class="sc">+</span></span>
-<span id="cb36-960"><a href="#cb36-960" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"# Mapped read pairs"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span></span>
-<span id="cb36-961"><a href="#cb36-961" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
-<span id="cb36-962"><a href="#cb36-962" aria-hidden="true" tabindex="-1"></a>g_rota</span>
-<span id="cb36-963"><a href="#cb36-963" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-964"><a href="#cb36-964" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-967"><a href="#cb36-967" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb36-968"><a href="#cb36-968" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-orthopicobirnavirus</span></span>
-<span id="cb36-969"><a href="#cb36-969" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-970"><a href="#cb36-970" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing names</span></span>
-<span id="cb36-971"><a href="#cb36-971" aria-hidden="true" tabindex="-1"></a>tax_names_new <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
-<span id="cb36-972"><a href="#cb36-972" aria-hidden="true" tabindex="-1"></a>  <span class="at">staxid =</span> <span class="fu">c</span>(<span class="dv">442302</span>, <span class="dv">3003954</span>, <span class="dv">585895</span>),</span>
-<span id="cb36-973"><a href="#cb36-973" aria-hidden="true" tabindex="-1"></a>  <span class="at">name =</span> <span class="fu">c</span>(<span class="st">"Porcine picobirnavirus"</span>, <span class="st">"ticpantry virus 7"</span>, <span class="st">"uncultured picobirnavirus"</span>)</span>
-<span id="cb36-974"><a href="#cb36-974" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb36-975"><a href="#cb36-975" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(tax_names, tax_names_new)</span>
-<span id="cb36-976"><a href="#cb36-976" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-977"><a href="#cb36-977" aria-hidden="true" tabindex="-1"></a>pico_ids <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid<span class="sc">==</span><span class="dv">2956252</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(seq_id)</span>
-<span id="cb36-978"><a href="#cb36-978" aria-hidden="true" tabindex="-1"></a>pico_hits <span class="ot">&lt;-</span> blast_results_paired <span class="sc">%&gt;%</span></span>
-<span id="cb36-979"><a href="#cb36-979" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> pico_ids) <span class="sc">%&gt;%</span></span>
-<span id="cb36-980"><a href="#cb36-980" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">staxid =</span> <span class="fu">as.integer</span>(staxid))</span>
-<span id="cb36-981"><a href="#cb36-981" aria-hidden="true" tabindex="-1"></a>pico_hits_sorted <span class="ot">&lt;-</span> pico_hits <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(staxid) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(n)) <span class="sc">%&gt;%</span></span>
-<span id="cb36-982"><a href="#cb36-982" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names, <span class="at">by=</span><span class="st">"staxid"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-983"><a href="#cb36-983" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">fct_inorder</span>(name))</span>
-<span id="cb36-984"><a href="#cb36-984" aria-hidden="true" tabindex="-1"></a>pico_hits_sorted_head <span class="ot">&lt;-</span> pico_hits_sorted <span class="sc">%&gt;%</span> <span class="fu">head</span>(<span class="dv">10</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb36-985"><a href="#cb36-985" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name=</span><span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(name)))</span>
-<span id="cb36-986"><a href="#cb36-986" aria-hidden="true" tabindex="-1"></a>g_pico <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(pico_hits_sorted_head,</span>
-<span id="cb36-987"><a href="#cb36-987" aria-hidden="true" tabindex="-1"></a>                 <span class="fu">aes</span>(<span class="at">x=</span>n, <span class="at">y=</span><span class="fu">fct_inorder</span>(name))) <span class="sc">+</span> <span class="fu">geom_col</span>() <span class="sc">+</span></span>
-<span id="cb36-988"><a href="#cb36-988" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"# Mapped read pairs"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span></span>
-<span id="cb36-989"><a href="#cb36-989" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
-<span id="cb36-990"><a href="#cb36-990" aria-hidden="true" tabindex="-1"></a>g_pico</span>
-<span id="cb36-991"><a href="#cb36-991" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb36-992"><a href="#cb36-992" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-993"><a href="#cb36-993" aria-hidden="true" tabindex="-1"></a><span class="fu"># Conclusion</span></span>
-<span id="cb36-994"><a href="#cb36-994" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-995"><a href="#cb36-995" aria-hidden="true" tabindex="-1"></a>I'm quite happy with the quality of the human-viral selection process for this dataset, which ended up achieving very high precision and sensitivity. The most striking result coming out of this analysis is probably the drastic difference in total HV abundance between RNA and DNA reads, with the former <span class="sc">\&gt;</span>100x higher despite very similar processing methods. In the past we've attributed low HV abundance in the DNA datasets we've analyzed to the lack of viral enrichment in available DNA datasets; in this case, however, there is very little difference between the DNA and RNA processing methods, so this explanation doesn't really hold. This makes me more pessimistic about achieving good HV relative abundance with DNA sequencing, even with improved viral enrichment methods.</span>
+<span id="cb36-509"><a href="#cb36-509" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"hv_hits_putative_filtered.tsv.gz"</span>)</span>
+<span id="cb36-510"><a href="#cb36-510" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(hv_reads_filtered_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-511"><a href="#cb36-511" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb36-512"><a href="#cb36-512" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(date, na_type)</span>
+<span id="cb36-513"><a href="#cb36-513" aria-hidden="true" tabindex="-1"></a>n_hv_filtered <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample, date, na_type) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">inner_join</span>(basic_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"ribo_initial"</span>) <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, n_read_pairs), <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">n_putative =</span> n, <span class="at">n_total =</span> n_read_pairs) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads <span class="sc">*</span> <span class="dv">100</span>)</span>
+<span id="cb36-514"><a href="#cb36-514" aria-hidden="true" tabindex="-1"></a>n_hv_filtered_summ <span class="ot">&lt;-</span> n_hv_filtered <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type) <span class="sc">%&gt;%</span> <span class="fu">summarize</span>(<span class="at">n_putative =</span> <span class="fu">sum</span>(n_putative), <span class="at">n_total =</span> <span class="fu">sum</span>(n_total)) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb36-515"><a href="#cb36-515" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-516"><a href="#cb36-516" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-519"><a href="#cb36-519" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-520"><a href="#cb36-520" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-scores</span></span>
+<span id="cb36-521"><a href="#cb36-521" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb36-522"><a href="#cb36-522" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 8</span></span>
+<span id="cb36-523"><a href="#cb36-523" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-524"><a href="#cb36-524" aria-hidden="true" tabindex="-1"></a>mrg <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span></span>
+<span id="cb36-525"><a href="#cb36-525" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">kraken_label =</span> <span class="fu">ifelse</span>(assigned_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">assignment"</span>,</span>
+<span id="cb36-526"><a href="#cb36-526" aria-hidden="true" tabindex="-1"></a>                               <span class="fu">ifelse</span>(hit_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">hit"</span>,</span>
+<span id="cb36-527"><a href="#cb36-527" aria-hidden="true" tabindex="-1"></a>                                      <span class="st">"No hit or</span><span class="sc">\n</span><span class="st">assignment"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb36-528"><a href="#cb36-528" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, na_type) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(adj_score_fwd), <span class="fu">desc</span>(adj_score_rev)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-529"><a href="#cb36-529" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_num =</span> <span class="fu">row_number</span>(),</span>
+<span id="cb36-530"><a href="#cb36-530" aria-hidden="true" tabindex="-1"></a>         <span class="at">adj_score_max =</span> <span class="fu">pmax</span>(adj_score_fwd, adj_score_rev))</span>
+<span id="cb36-531"><a href="#cb36-531" aria-hidden="true" tabindex="-1"></a><span class="co"># Import Bowtie2/Kraken data and perform filtering steps</span></span>
+<span id="cb36-532"><a href="#cb36-532" aria-hidden="true" tabindex="-1"></a>g_mrg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev)) <span class="sc">+</span></span>
+<span id="cb36-533"><a href="#cb36-533" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span> </span>
+<span id="cb36-534"><a href="#cb36-534" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb36-535"><a href="#cb36-535" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb36-536"><a href="#cb36-536" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb36-537"><a href="#cb36-537" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>)</span>
+<span id="cb36-538"><a href="#cb36-538" aria-hidden="true" tabindex="-1"></a>g_mrg</span>
+<span id="cb36-539"><a href="#cb36-539" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-540"><a href="#cb36-540" aria-hidden="true" tabindex="-1"></a>g_hist_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>,<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>kraken_label, <span class="at">scales=</span><span class="st">"free_y"</span>) <span class="sc">+</span> <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Dark2"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>)</span>
+<span id="cb36-541"><a href="#cb36-541" aria-hidden="true" tabindex="-1"></a>g_hist_0</span>
+<span id="cb36-542"><a href="#cb36-542" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-543"><a href="#cb36-543" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-544"><a href="#cb36-544" aria-hidden="true" tabindex="-1"></a>As previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I've used for previous datasets.</span>
+<span id="cb36-545"><a href="#cb36-545" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-548"><a href="#cb36-548" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-549"><a href="#cb36-549" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: make-blast-fasta</span></span>
+<span id="cb36-550"><a href="#cb36-550" aria-hidden="true" tabindex="-1"></a>mrg_fasta <span class="ot">&lt;-</span>  mrg <span class="sc">%&gt;%</span></span>
+<span id="cb36-551"><a href="#cb36-551" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_head =</span> <span class="fu">paste0</span>(<span class="st">"&gt;"</span>, seq_id)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-552"><a href="#cb36-552" aria-hidden="true" tabindex="-1"></a>  ungroup <span class="sc">%&gt;%</span></span>
+<span id="cb36-553"><a href="#cb36-553" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">header1=</span>seq_head, <span class="at">seq1=</span>query_seq_fwd, </span>
+<span id="cb36-554"><a href="#cb36-554" aria-hidden="true" tabindex="-1"></a>         <span class="at">header2=</span>seq_head, <span class="at">seq2=</span>query_seq_rev) <span class="sc">%&gt;%</span></span>
+<span id="cb36-555"><a href="#cb36-555" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">header1=</span><span class="fu">paste0</span>(header1, <span class="st">"_1"</span>), <span class="at">header2=</span><span class="fu">paste0</span>(header2, <span class="st">"_2"</span>))</span>
+<span id="cb36-556"><a href="#cb36-556" aria-hidden="true" tabindex="-1"></a>mrg_fasta_out <span class="ot">&lt;-</span> <span class="fu">do.call</span>(paste, <span class="fu">c</span>(mrg_fasta, <span class="at">sep=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)) <span class="sc">%&gt;%</span> <span class="fu">paste</span>(<span class="at">collapse=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
+<span id="cb36-557"><a href="#cb36-557" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(mrg_fasta_out, <span class="fu">file.path</span>(data_dir, <span class="st">"brumfield-putative-viral.fasta"</span>))</span>
+<span id="cb36-558"><a href="#cb36-558" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-559"><a href="#cb36-559" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-562"><a href="#cb36-562" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-563"><a href="#cb36-563" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: process-blast-data</span></span>
+<span id="cb36-564"><a href="#cb36-564" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb36-565"><a href="#cb36-565" aria-hidden="true" tabindex="-1"></a><span class="co"># Import BLAST results</span></span>
+<span id="cb36-566"><a href="#cb36-566" aria-hidden="true" tabindex="-1"></a>blast_results_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"blast/brumfield-putative-viral.blast.gz"</span>)</span>
+<span id="cb36-567"><a href="#cb36-567" aria-hidden="true" tabindex="-1"></a>blast_cols <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"qseqid"</span>, <span class="st">"sseqid"</span>, <span class="st">"sgi"</span>, <span class="st">"staxid"</span>, <span class="st">"qlen"</span>, <span class="st">"evalue"</span>, <span class="st">"bitscore"</span>, <span class="st">"qcovs"</span>, <span class="st">"length"</span>, <span class="st">"pident"</span>, <span class="st">"mismatch"</span>, <span class="st">"gapopen"</span>, <span class="st">"sstrand"</span>, <span class="st">"qstart"</span>, <span class="st">"qend"</span>, <span class="st">"sstart"</span>, <span class="st">"send"</span>)</span>
+<span id="cb36-568"><a href="#cb36-568" aria-hidden="true" tabindex="-1"></a>blast_results <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(blast_results_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>, </span>
+<span id="cb36-569"><a href="#cb36-569" aria-hidden="true" tabindex="-1"></a>                          <span class="at">col_names =</span> blast_cols, <span class="at">col_types =</span> <span class="fu">cols</span>(<span class="at">.default=</span><span class="st">"c"</span>))</span>
+<span id="cb36-570"><a href="#cb36-570" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-571"><a href="#cb36-571" aria-hidden="true" tabindex="-1"></a><span class="co"># Add viral status</span></span>
+<span id="cb36-572"><a href="#cb36-572" aria-hidden="true" tabindex="-1"></a>blast_results_viral <span class="ot">&lt;-</span> <span class="fu">mutate</span>(blast_results, <span class="at">viral =</span> staxid <span class="sc">%in%</span> viral_taxa<span class="sc">$</span>taxid)</span>
+<span id="cb36-573"><a href="#cb36-573" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-574"><a href="#cb36-574" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter for best hit for each query/subject, then rank hits for each query</span></span>
+<span id="cb36-575"><a href="#cb36-575" aria-hidden="true" tabindex="-1"></a>blast_results_best <span class="ot">&lt;-</span> blast_results_viral <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(qseqid, staxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb36-576"><a href="#cb36-576" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(bitscore <span class="sc">==</span> <span class="fu">max</span>(bitscore)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-577"><a href="#cb36-577" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(length <span class="sc">==</span> <span class="fu">max</span>(length)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>)</span>
+<span id="cb36-578"><a href="#cb36-578" aria-hidden="true" tabindex="-1"></a>blast_results_ranked <span class="ot">&lt;-</span> blast_results_best <span class="sc">%&gt;%</span> </span>
+<span id="cb36-579"><a href="#cb36-579" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(qseqid) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">rank =</span> <span class="fu">dense_rank</span>(<span class="fu">desc</span>(bitscore)))</span>
+<span id="cb36-580"><a href="#cb36-580" aria-hidden="true" tabindex="-1"></a>blast_results_highrank <span class="ot">&lt;-</span> blast_results_ranked <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">&lt;=</span> <span class="dv">5</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-581"><a href="#cb36-581" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">read_pair =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="sc">-</span><span class="dv">1</span>), </span>
+<span id="cb36-582"><a href="#cb36-582" aria-hidden="true" tabindex="-1"></a>         <span class="at">seq_id =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="dv">1</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-583"><a href="#cb36-583" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">bitscore =</span> <span class="fu">as.numeric</span>(bitscore))</span>
+<span id="cb36-584"><a href="#cb36-584" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-585"><a href="#cb36-585" aria-hidden="true" tabindex="-1"></a><span class="co"># Summarize by read pair and taxid</span></span>
+<span id="cb36-586"><a href="#cb36-586" aria-hidden="true" tabindex="-1"></a>blast_results_paired <span class="ot">&lt;-</span> blast_results_highrank <span class="sc">%&gt;%</span></span>
+<span id="cb36-587"><a href="#cb36-587" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id, staxid, viral) <span class="sc">%&gt;%</span></span>
+<span id="cb36-588"><a href="#cb36-588" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">bitscore_max =</span> <span class="fu">max</span>(bitscore), <span class="at">bitscore_min =</span> <span class="fu">min</span>(bitscore),</span>
+<span id="cb36-589"><a href="#cb36-589" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads =</span> <span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-590"><a href="#cb36-590" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_full =</span> viral <span class="sc">&amp;</span> n_reads <span class="sc">==</span> <span class="dv">2</span>)</span>
+<span id="cb36-591"><a href="#cb36-591" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-592"><a href="#cb36-592" aria-hidden="true" tabindex="-1"></a><span class="co"># Compare to Kraken &amp; Bowtie assignments</span></span>
+<span id="cb36-593"><a href="#cb36-593" aria-hidden="true" tabindex="-1"></a>mrg_assign <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, seq_id, taxid, assigned_taxid, adj_score_max, na_type)</span>
+<span id="cb36-594"><a href="#cb36-594" aria-hidden="true" tabindex="-1"></a>blast_results_assign <span class="ot">&lt;-</span> <span class="fu">left_join</span>(blast_results_paired, mrg_assign, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-595"><a href="#cb36-595" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">taxid_match_bowtie =</span> (staxid <span class="sc">==</span> taxid),</span>
+<span id="cb36-596"><a href="#cb36-596" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_kraken =</span> (staxid <span class="sc">==</span> assigned_taxid),</span>
+<span id="cb36-597"><a href="#cb36-597" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_any =</span> taxid_match_bowtie <span class="sc">|</span> taxid_match_kraken)</span>
+<span id="cb36-598"><a href="#cb36-598" aria-hidden="true" tabindex="-1"></a>blast_results_out <span class="ot">&lt;-</span> blast_results_assign <span class="sc">%&gt;%</span></span>
+<span id="cb36-599"><a href="#cb36-599" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id) <span class="sc">%&gt;%</span></span>
+<span id="cb36-600"><a href="#cb36-600" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">viral_status =</span> <span class="fu">ifelse</span>(<span class="fu">any</span>(viral_full), <span class="dv">2</span>,</span>
+<span id="cb36-601"><a href="#cb36-601" aria-hidden="true" tabindex="-1"></a>                                  <span class="fu">ifelse</span>(<span class="fu">any</span>(taxid_match_any), <span class="dv">2</span>,</span>
+<span id="cb36-602"><a href="#cb36-602" aria-hidden="true" tabindex="-1"></a>                                             <span class="fu">ifelse</span>(<span class="fu">any</span>(viral), <span class="dv">1</span>, <span class="dv">0</span>))),</span>
+<span id="cb36-603"><a href="#cb36-603" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb36-604"><a href="#cb36-604" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-605"><a href="#cb36-605" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-608"><a href="#cb36-608" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-609"><a href="#cb36-609" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-blast-results</span></span>
+<span id="cb36-610"><a href="#cb36-610" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb36-611"><a href="#cb36-611" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb36-612"><a href="#cb36-612" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-613"><a href="#cb36-613" aria-hidden="true" tabindex="-1"></a><span class="co"># Merge BLAST results with unenriched read data</span></span>
+<span id="cb36-614"><a href="#cb36-614" aria-hidden="true" tabindex="-1"></a>mrg_blast <span class="ot">&lt;-</span> <span class="fu">full_join</span>(mrg, blast_results_out, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-615"><a href="#cb36-615" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_status =</span> <span class="fu">replace_na</span>(viral_status, <span class="dv">0</span>),</span>
+<span id="cb36-616"><a href="#cb36-616" aria-hidden="true" tabindex="-1"></a>         <span class="at">viral_status_out =</span> <span class="fu">ifelse</span>(viral_status <span class="sc">==</span> <span class="dv">0</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>))</span>
+<span id="cb36-617"><a href="#cb36-617" aria-hidden="true" tabindex="-1"></a>mrg_blast_rna <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(na_type <span class="sc">==</span> <span class="st">"RNA"</span>)</span>
+<span id="cb36-618"><a href="#cb36-618" aria-hidden="true" tabindex="-1"></a>mrg_blast_dna <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(na_type <span class="sc">==</span> <span class="st">"DNA"</span>)</span>
+<span id="cb36-619"><a href="#cb36-619" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-620"><a href="#cb36-620" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot RNA</span></span>
+<span id="cb36-621"><a href="#cb36-621" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_rna <span class="ot">&lt;-</span> mrg_blast_rna <span class="sc">%&gt;%</span></span>
+<span id="cb36-622"><a href="#cb36-622" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev, <span class="at">color=</span>viral_status_out)) <span class="sc">+</span></span>
+<span id="cb36-623"><a href="#cb36-623" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span></span>
+<span id="cb36-624"><a href="#cb36-624" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb36-625"><a href="#cb36-625" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb36-626"><a href="#cb36-626" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Viral status"</span>) <span class="sc">+</span></span>
+<span id="cb36-627"><a href="#cb36-627" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(viral_status_out<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb36-628"><a href="#cb36-628" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"RNA"</span>) <span class="sc">+</span> </span>
+<span id="cb36-629"><a href="#cb36-629" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>, <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="dv">2</span>), <span class="at">hjust=</span><span class="dv">0</span>))</span>
+<span id="cb36-630"><a href="#cb36-630" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_rna</span>
+<span id="cb36-631"><a href="#cb36-631" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-632"><a href="#cb36-632" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot DNA</span></span>
+<span id="cb36-633"><a href="#cb36-633" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_dna <span class="ot">&lt;-</span> mrg_blast_dna <span class="sc">%&gt;%</span></span>
+<span id="cb36-634"><a href="#cb36-634" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev, <span class="at">color=</span>viral_status_out)) <span class="sc">+</span></span>
+<span id="cb36-635"><a href="#cb36-635" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span></span>
+<span id="cb36-636"><a href="#cb36-636" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb36-637"><a href="#cb36-637" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb36-638"><a href="#cb36-638" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Viral status"</span>) <span class="sc">+</span></span>
+<span id="cb36-639"><a href="#cb36-639" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(viral_status_out<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb36-640"><a href="#cb36-640" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"DNA"</span>) <span class="sc">+</span> </span>
+<span id="cb36-641"><a href="#cb36-641" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>, <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="dv">2</span>), <span class="at">hjust=</span><span class="dv">0</span>))</span>
+<span id="cb36-642"><a href="#cb36-642" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_dna</span>
+<span id="cb36-643"><a href="#cb36-643" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-644"><a href="#cb36-644" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-647"><a href="#cb36-647" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-648"><a href="#cb36-648" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-blast-histogram</span></span>
+<span id="cb36-649"><a href="#cb36-649" aria-hidden="true" tabindex="-1"></a>g_hist_1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg_blast, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>,<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>viral_status_out, <span class="at">scales =</span> <span class="st">"free_y"</span>) <span class="sc">+</span> <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Dark2"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>)</span>
+<span id="cb36-650"><a href="#cb36-650" aria-hidden="true" tabindex="-1"></a>g_hist_1</span>
+<span id="cb36-651"><a href="#cb36-651" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-652"><a href="#cb36-652" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-653"><a href="#cb36-653" aria-hidden="true" tabindex="-1"></a>These results look very good on visual inspection, and indeed precision and sensitivity are both very high. For a disjunctive score threshold of 20, my updated workflow achieves an F1 score of 99.0% for RNA sequences and 99.2% for DNA sequences; more than high enough to continue on to human viral analysis.</span>
+<span id="cb36-654"><a href="#cb36-654" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-657"><a href="#cb36-657" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-658"><a href="#cb36-658" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-f1</span></span>
+<span id="cb36-659"><a href="#cb36-659" aria-hidden="true" tabindex="-1"></a>test_sens_spec <span class="ot">&lt;-</span> <span class="cf">function</span>(tab, score_threshold, conjunctive, include_special){</span>
+<span id="cb36-660"><a href="#cb36-660" aria-hidden="true" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span>include_special) tab <span class="ot">&lt;-</span> <span class="fu">filter</span>(tab, viral_status_out <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"TRUE"</span>, <span class="st">"FALSE"</span>))</span>
+<span id="cb36-661"><a href="#cb36-661" aria-hidden="true" tabindex="-1"></a>  tab_retained <span class="ot">&lt;-</span> tab <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(</span>
+<span id="cb36-662"><a href="#cb36-662" aria-hidden="true" tabindex="-1"></a>    <span class="at">conjunctive =</span> conjunctive,</span>
+<span id="cb36-663"><a href="#cb36-663" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score_conjunctive =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">&amp;</span> adj_score_rev <span class="sc">&gt;</span> score_threshold), </span>
+<span id="cb36-664"><a href="#cb36-664" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score_disjunctive =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">|</span> adj_score_rev <span class="sc">&gt;</span> score_threshold),</span>
+<span id="cb36-665"><a href="#cb36-665" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score =</span> <span class="fu">ifelse</span>(conjunctive, retain_score_conjunctive, retain_score_disjunctive),</span>
+<span id="cb36-666"><a href="#cb36-666" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> retain_score) <span class="sc">%&gt;%</span></span>
+<span id="cb36-667"><a href="#cb36-667" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(viral_status_out, retain) <span class="sc">%&gt;%</span> count</span>
+<span id="cb36-668"><a href="#cb36-668" aria-hidden="true" tabindex="-1"></a>  pos_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb36-669"><a href="#cb36-669" aria-hidden="true" tabindex="-1"></a>  pos_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb36-670"><a href="#cb36-670" aria-hidden="true" tabindex="-1"></a>  neg_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb36-671"><a href="#cb36-671" aria-hidden="true" tabindex="-1"></a>  neg_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb36-672"><a href="#cb36-672" aria-hidden="true" tabindex="-1"></a>  sensitivity <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> neg_fls)</span>
+<span id="cb36-673"><a href="#cb36-673" aria-hidden="true" tabindex="-1"></a>  specificity <span class="ot">&lt;-</span> neg_tru <span class="sc">/</span> (neg_tru <span class="sc">+</span> pos_fls)</span>
+<span id="cb36-674"><a href="#cb36-674" aria-hidden="true" tabindex="-1"></a>  precision   <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> pos_fls)</span>
+<span id="cb36-675"><a href="#cb36-675" aria-hidden="true" tabindex="-1"></a>  f1 <span class="ot">&lt;-</span> <span class="dv">2</span> <span class="sc">*</span> precision <span class="sc">*</span> sensitivity <span class="sc">/</span> (precision <span class="sc">+</span> sensitivity)</span>
+<span id="cb36-676"><a href="#cb36-676" aria-hidden="true" tabindex="-1"></a>  out <span class="ot">&lt;-</span> <span class="fu">tibble</span>(<span class="at">threshold=</span>score_threshold, <span class="at">include_special =</span> include_special, </span>
+<span id="cb36-677"><a href="#cb36-677" aria-hidden="true" tabindex="-1"></a>                <span class="at">conjunctive =</span> conjunctive, <span class="at">sensitivity=</span>sensitivity, </span>
+<span id="cb36-678"><a href="#cb36-678" aria-hidden="true" tabindex="-1"></a>                <span class="at">specificity=</span>specificity, <span class="at">precision=</span>precision, <span class="at">f1=</span>f1)</span>
+<span id="cb36-679"><a href="#cb36-679" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(out)</span>
+<span id="cb36-680"><a href="#cb36-680" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb36-681"><a href="#cb36-681" aria-hidden="true" tabindex="-1"></a>range_f1 <span class="ot">&lt;-</span> <span class="cf">function</span>(intab, inc_special, <span class="at">inrange=</span><span class="dv">15</span><span class="sc">:</span><span class="dv">45</span>){</span>
+<span id="cb36-682"><a href="#cb36-682" aria-hidden="true" tabindex="-1"></a>  tss <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(test_sens_spec, <span class="at">tab=</span>intab, <span class="at">include_special=</span>inc_special)</span>
+<span id="cb36-683"><a href="#cb36-683" aria-hidden="true" tabindex="-1"></a>  stats_conj <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss, <span class="at">conjunctive=</span><span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb36-684"><a href="#cb36-684" aria-hidden="true" tabindex="-1"></a>  stats_disj <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss, <span class="at">conjunctive=</span><span class="cn">FALSE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb36-685"><a href="#cb36-685" aria-hidden="true" tabindex="-1"></a>  stats_all <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(stats_conj, stats_disj) <span class="sc">%&gt;%</span></span>
+<span id="cb36-686"><a href="#cb36-686" aria-hidden="true" tabindex="-1"></a>    <span class="fu">pivot_longer</span>(<span class="sc">!</span>(threshold<span class="sc">:</span>conjunctive), <span class="at">names_to=</span><span class="st">"metric"</span>, <span class="at">values_to=</span><span class="st">"value"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-687"><a href="#cb36-687" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">conj_label =</span> <span class="fu">ifelse</span>(conjunctive, <span class="st">"Conjunctive"</span>, <span class="st">"Disjunctive"</span>))</span>
+<span id="cb36-688"><a href="#cb36-688" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(stats_all)</span>
+<span id="cb36-689"><a href="#cb36-689" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb36-690"><a href="#cb36-690" aria-hidden="true" tabindex="-1"></a>inc_special <span class="ot">&lt;-</span> <span class="cn">FALSE</span></span>
+<span id="cb36-691"><a href="#cb36-691" aria-hidden="true" tabindex="-1"></a>stats_rna <span class="ot">&lt;-</span> <span class="fu">range_f1</span>(mrg_blast_rna, inc_special) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">na_type =</span> <span class="st">"RNA"</span>)</span>
+<span id="cb36-692"><a href="#cb36-692" aria-hidden="true" tabindex="-1"></a>stats_dna <span class="ot">&lt;-</span> <span class="fu">range_f1</span>(mrg_blast_dna, inc_special) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">na_type =</span> <span class="st">"DNA"</span>)</span>
+<span id="cb36-693"><a href="#cb36-693" aria-hidden="true" tabindex="-1"></a>stats_0 <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(stats_rna, stats_dna) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">attempt=</span><span class="dv">0</span>)</span>
+<span id="cb36-694"><a href="#cb36-694" aria-hidden="true" tabindex="-1"></a>threshold_opt_0 <span class="ot">&lt;-</span> stats_0 <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(conj_label,attempt,na_type) <span class="sc">%&gt;%</span> </span>
+<span id="cb36-695"><a href="#cb36-695" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(metric <span class="sc">==</span> <span class="st">"f1"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-696"><a href="#cb36-696" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="fu">max</span>(value)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(threshold <span class="sc">==</span> <span class="fu">min</span>(threshold))</span>
+<span id="cb36-697"><a href="#cb36-697" aria-hidden="true" tabindex="-1"></a>g_stats_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(stats_0, <span class="fu">aes</span>(<span class="at">x=</span>threshold, <span class="at">y=</span>value, <span class="at">color=</span>metric)) <span class="sc">+</span></span>
+<span id="cb36-698"><a href="#cb36-698" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">data =</span> threshold_opt_0, <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">xintercept=</span>threshold),</span>
+<span id="cb36-699"><a href="#cb36-699" aria-hidden="true" tabindex="-1"></a>             <span class="at">color =</span> <span class="st">"red"</span>, <span class="at">linetype =</span> <span class="st">"dashed"</span>) <span class="sc">+</span></span>
+<span id="cb36-700"><a href="#cb36-700" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
+<span id="cb36-701"><a href="#cb36-701" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"Value"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb36-702"><a href="#cb36-702" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Threshold"</span>, <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb36-703"><a href="#cb36-703" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette=</span><span class="st">"Set3"</span>) <span class="sc">+</span></span>
+<span id="cb36-704"><a href="#cb36-704" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>conj_label) <span class="sc">+</span></span>
+<span id="cb36-705"><a href="#cb36-705" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb36-706"><a href="#cb36-706" aria-hidden="true" tabindex="-1"></a>g_stats_0</span>
+<span id="cb36-707"><a href="#cb36-707" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-708"><a href="#cb36-708" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-709"><a href="#cb36-709" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting virus reads: analysis</span></span>
+<span id="cb36-710"><a href="#cb36-710" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-713"><a href="#cb36-713" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-714"><a href="#cb36-714" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: count-hv-reads</span></span>
+<span id="cb36-715"><a href="#cb36-715" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-716"><a href="#cb36-716" aria-hidden="true" tabindex="-1"></a><span class="co"># Get raw read counts</span></span>
+<span id="cb36-717"><a href="#cb36-717" aria-hidden="true" tabindex="-1"></a>read_counts_raw <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span></span>
+<span id="cb36-718"><a href="#cb36-718" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, date, na_type, <span class="at">n_reads_raw =</span> n_read_pairs)</span>
+<span id="cb36-719"><a href="#cb36-719" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-720"><a href="#cb36-720" aria-hidden="true" tabindex="-1"></a><span class="co"># Get HV read counts</span></span>
+<span id="cb36-721"><a href="#cb36-721" aria-hidden="true" tabindex="-1"></a>mrg_hv <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">hv_status =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> adj_score_max <span class="sc">&gt;=</span> <span class="dv">20</span>)</span>
+<span id="cb36-722"><a href="#cb36-722" aria-hidden="true" tabindex="-1"></a>read_counts_hv <span class="ot">&lt;-</span> mrg_hv <span class="sc">%&gt;%</span> <span class="fu">filter</span>(hv_status) <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">count</span>(<span class="at">name=</span><span class="st">"n_reads_hv"</span>)</span>
+<span id="cb36-723"><a href="#cb36-723" aria-hidden="true" tabindex="-1"></a>read_counts <span class="ot">&lt;-</span> read_counts_raw <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(read_counts_hv, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-724"><a href="#cb36-724" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads_hv =</span> <span class="fu">replace_na</span>(n_reads_hv, <span class="dv">0</span>))</span>
+<span id="cb36-725"><a href="#cb36-725" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-726"><a href="#cb36-726" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate</span></span>
+<span id="cb36-727"><a href="#cb36-727" aria-hidden="true" tabindex="-1"></a>read_counts_total <span class="ot">&lt;-</span> read_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type) <span class="sc">%&gt;%</span></span>
+<span id="cb36-728"><a href="#cb36-728" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_raw =</span> <span class="fu">sum</span>(n_reads_raw),</span>
+<span id="cb36-729"><a href="#cb36-729" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-730"><a href="#cb36-730" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample=</span> <span class="st">"All samples"</span>, <span class="at">date =</span> <span class="st">"All dates"</span>)</span>
+<span id="cb36-731"><a href="#cb36-731" aria-hidden="true" tabindex="-1"></a>read_counts_agg <span class="ot">&lt;-</span> read_counts <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(date) <span class="sc">%&gt;%</span> </span>
+<span id="cb36-732"><a href="#cb36-732" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">date =</span> <span class="fu">as.character</span>(date)) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(sample) <span class="sc">%&gt;%</span></span>
+<span id="cb36-733"><a href="#cb36-733" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bind_rows</span>(read_counts_total) <span class="sc">%&gt;%</span> </span>
+<span id="cb36-734"><a href="#cb36-734" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> <span class="fu">fct_inorder</span>(sample),</span>
+<span id="cb36-735"><a href="#cb36-735" aria-hidden="true" tabindex="-1"></a>         <span class="at">date =</span> <span class="fu">fct_inorder</span>(date),</span>
+<span id="cb36-736"><a href="#cb36-736" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span>n_reads_raw)</span>
+<span id="cb36-737"><a href="#cb36-737" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-738"><a href="#cb36-738" aria-hidden="true" tabindex="-1"></a><span class="co"># Get old counts</span></span>
+<span id="cb36-739"><a href="#cb36-739" aria-hidden="true" tabindex="-1"></a>n_hv_reads_old <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">691</span>, <span class="dv">121</span>, <span class="dv">18</span>, <span class="dv">224</span>, <span class="dv">2</span>, <span class="dv">26</span>, <span class="dv">6</span>, <span class="dv">21</span>, <span class="dv">4</span>, <span class="dv">29</span>, <span class="dv">12</span>, <span class="dv">22</span>)</span>
+<span id="cb36-740"><a href="#cb36-740" aria-hidden="true" tabindex="-1"></a>n_hv_reads_old[<span class="dv">13</span>] <span class="ot">&lt;-</span> <span class="fu">sum</span>(n_hv_reads_old[<span class="fu">which</span>(read_counts<span class="sc">$</span>na_type<span class="sc">==</span><span class="st">"RNA"</span>)])</span>
+<span id="cb36-741"><a href="#cb36-741" aria-hidden="true" tabindex="-1"></a>n_hv_reads_old[<span class="dv">14</span>] <span class="ot">&lt;-</span> <span class="fu">sum</span>(n_hv_reads_old[<span class="fu">which</span>(read_counts<span class="sc">$</span>na_type<span class="sc">==</span><span class="st">"DNA"</span>)])</span>
+<span id="cb36-742"><a href="#cb36-742" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-743"><a href="#cb36-743" aria-hidden="true" tabindex="-1"></a>read_counts_comp <span class="ot">&lt;-</span> read_counts_agg <span class="sc">%&gt;%</span></span>
+<span id="cb36-744"><a href="#cb36-744" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads_hv_old =</span> n_hv_reads_old,</span>
+<span id="cb36-745"><a href="#cb36-745" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_hv_old =</span> n_reads_hv_old<span class="sc">/</span>n_reads_raw)</span>
+<span id="cb36-746"><a href="#cb36-746" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-747"><a href="#cb36-747" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-748"><a href="#cb36-748" aria-hidden="true" tabindex="-1"></a>Applying a disjunctive cutoff at S=20 identifies 13,809 RNA reads and 66 DNA reads as human-viral. This gives an overall relative HV abundance of $5.00 \times 10^{-5}$ for RNA reads and $3.66 \times 10^{-7}$ for DNA reads. In contrast, the overall HV in the public dashboard is $3.93 \times 10^{-6}$ for RNA reads and $4.54 \times 10^{-7}$; the measured HV has thus increased 12.7x for RNA reads, but *decreased* slightly for DNA reads.</span>
+<span id="cb36-749"><a href="#cb36-749" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-752"><a href="#cb36-752" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-753"><a href="#cb36-753" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-ra</span></span>
+<span id="cb36-754"><a href="#cb36-754" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb36-755"><a href="#cb36-755" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize</span></span>
+<span id="cb36-756"><a href="#cb36-756" aria-hidden="true" tabindex="-1"></a>g_phv_agg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(read_counts_agg, <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">color=</span>na_type)) <span class="sc">+</span></span>
+<span id="cb36-757"><a href="#cb36-757" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">y=</span>p_reads_hv)) <span class="sc">+</span></span>
+<span id="cb36-758"><a href="#cb36-758" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_log10</span>(<span class="st">"Relative abundance of human virus reads"</span>) <span class="sc">+</span></span>
+<span id="cb36-759"><a href="#cb36-759" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span> theme_kit</span>
+<span id="cb36-760"><a href="#cb36-760" aria-hidden="true" tabindex="-1"></a>g_phv_agg</span>
+<span id="cb36-761"><a href="#cb36-761" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-762"><a href="#cb36-762" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-763"><a href="#cb36-763" aria-hidden="true" tabindex="-1"></a>Digging into individual viruses, we see that fecal-oral viruses predominate, especially *Mamastrovirus*, *Rotavirus*, *Salivirus*, and fecal-oral *Enterovirus* species (especially *Enterovirus C*, which includes poliovirus):</span>
+<span id="cb36-764"><a href="#cb36-764" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-767"><a href="#cb36-767" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-768"><a href="#cb36-768" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: raise-hv-taxa</span></span>
+<span id="cb36-769"><a href="#cb36-769" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-770"><a href="#cb36-770" aria-hidden="true" tabindex="-1"></a><span class="co"># Get viral taxon names for putative HV reads</span></span>
+<span id="cb36-771"><a href="#cb36-771" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">249588</span>] <span class="ot">&lt;-</span> <span class="st">"Mamastrovirus"</span></span>
+<span id="cb36-772"><a href="#cb36-772" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">194960</span>] <span class="ot">&lt;-</span> <span class="st">"Kobuvirus"</span></span>
+<span id="cb36-773"><a href="#cb36-773" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">688449</span>] <span class="ot">&lt;-</span> <span class="st">"Salivirus"</span></span>
+<span id="cb36-774"><a href="#cb36-774" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">585893</span>] <span class="ot">&lt;-</span> <span class="st">"Picobirnaviridae"</span></span>
+<span id="cb36-775"><a href="#cb36-775" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333922</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus"</span></span>
+<span id="cb36-776"><a href="#cb36-776" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-777"><a href="#cb36-777" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334207</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 3"</span></span>
+<span id="cb36-778"><a href="#cb36-778" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">369960</span>] <span class="ot">&lt;-</span> <span class="st">"Porcine type-C oncovirus"</span></span>
+<span id="cb36-779"><a href="#cb36-779" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333924</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 2"</span></span>
+<span id="cb36-780"><a href="#cb36-780" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-781"><a href="#cb36-781" aria-hidden="true" tabindex="-1"></a>mrg_hv_named <span class="ot">&lt;-</span> mrg_hv <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb36-782"><a href="#cb36-782" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-783"><a href="#cb36-783" aria-hidden="true" tabindex="-1"></a><span class="co"># Discover viral species &amp; genera for HV reads</span></span>
+<span id="cb36-784"><a href="#cb36-784" aria-hidden="true" tabindex="-1"></a>raise_rank <span class="ot">&lt;-</span> <span class="cf">function</span>(read_db, taxid_db, <span class="at">out_rank =</span> <span class="st">"species"</span>, <span class="at">verbose =</span> <span class="cn">FALSE</span>){</span>
+<span id="cb36-785"><a href="#cb36-785" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Get higher ranks than search rank</span></span>
+<span id="cb36-786"><a href="#cb36-786" aria-hidden="true" tabindex="-1"></a>  ranks <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"subspecies"</span>, <span class="st">"species"</span>, <span class="st">"subgenus"</span>, <span class="st">"genus"</span>, <span class="st">"subfamily"</span>, <span class="st">"family"</span>, <span class="st">"suborder"</span>, <span class="st">"order"</span>, <span class="st">"class"</span>, <span class="st">"subphylum"</span>, <span class="st">"phylum"</span>, <span class="st">"kingdom"</span>, <span class="st">"superkingdom"</span>)</span>
+<span id="cb36-787"><a href="#cb36-787" aria-hidden="true" tabindex="-1"></a>  rank_match <span class="ot">&lt;-</span> <span class="fu">which.max</span>(ranks <span class="sc">==</span> out_rank)</span>
+<span id="cb36-788"><a href="#cb36-788" aria-hidden="true" tabindex="-1"></a>  high_ranks <span class="ot">&lt;-</span> ranks[rank_match<span class="sc">:</span><span class="fu">length</span>(ranks)]</span>
+<span id="cb36-789"><a href="#cb36-789" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Merge read DB and taxid DB</span></span>
+<span id="cb36-790"><a href="#cb36-790" aria-hidden="true" tabindex="-1"></a>  reads <span class="ot">&lt;-</span> read_db <span class="sc">%&gt;%</span> <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb36-791"><a href="#cb36-791" aria-hidden="true" tabindex="-1"></a>    <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb36-792"><a href="#cb36-792" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Extract sequences that are already at appropriate rank</span></span>
+<span id="cb36-793"><a href="#cb36-793" aria-hidden="true" tabindex="-1"></a>  reads_rank <span class="ot">&lt;-</span> <span class="fu">filter</span>(reads, rank <span class="sc">==</span> out_rank)</span>
+<span id="cb36-794"><a href="#cb36-794" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Drop sequences at a higher rank and return unclassified sequences</span></span>
+<span id="cb36-795"><a href="#cb36-795" aria-hidden="true" tabindex="-1"></a>  reads_norank <span class="ot">&lt;-</span> reads <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">!=</span> out_rank, <span class="sc">!</span>rank <span class="sc">%in%</span> high_ranks, <span class="sc">!</span><span class="fu">is.na</span>(taxid))</span>
+<span id="cb36-796"><a href="#cb36-796" aria-hidden="true" tabindex="-1"></a>  <span class="cf">while</span>(<span class="fu">nrow</span>(reads_norank) <span class="sc">&gt;</span> <span class="dv">0</span>){ <span class="co"># As long as there are unclassified sequences...</span></span>
+<span id="cb36-797"><a href="#cb36-797" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Promote read taxids and re-merge with taxid DB, then re-classify and filter</span></span>
+<span id="cb36-798"><a href="#cb36-798" aria-hidden="true" tabindex="-1"></a>    reads_remaining <span class="ot">&lt;-</span> reads_norank <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">taxid =</span> parent_taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb36-799"><a href="#cb36-799" aria-hidden="true" tabindex="-1"></a>      <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb36-800"><a href="#cb36-800" aria-hidden="true" tabindex="-1"></a>      <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb36-801"><a href="#cb36-801" aria-hidden="true" tabindex="-1"></a>    reads_rank <span class="ot">&lt;-</span> reads_remaining <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> out_rank) <span class="sc">%&gt;%</span></span>
+<span id="cb36-802"><a href="#cb36-802" aria-hidden="true" tabindex="-1"></a>      <span class="fu">bind_rows</span>(reads_rank)</span>
+<span id="cb36-803"><a href="#cb36-803" aria-hidden="true" tabindex="-1"></a>    reads_norank <span class="ot">&lt;-</span> reads_remaining <span class="sc">%&gt;%</span></span>
+<span id="cb36-804"><a href="#cb36-804" aria-hidden="true" tabindex="-1"></a>      <span class="fu">filter</span>(rank <span class="sc">!=</span> out_rank, <span class="sc">!</span>rank <span class="sc">%in%</span> high_ranks, <span class="sc">!</span><span class="fu">is.na</span>(taxid))</span>
+<span id="cb36-805"><a href="#cb36-805" aria-hidden="true" tabindex="-1"></a>  }</span>
+<span id="cb36-806"><a href="#cb36-806" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Finally, extract and append reads that were excluded during the process</span></span>
+<span id="cb36-807"><a href="#cb36-807" aria-hidden="true" tabindex="-1"></a>  reads_dropped <span class="ot">&lt;-</span> reads <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span>seq_id <span class="sc">%in%</span> reads_rank<span class="sc">$</span>seq_id)</span>
+<span id="cb36-808"><a href="#cb36-808" aria-hidden="true" tabindex="-1"></a>  reads_out <span class="ot">&lt;-</span> reads_rank <span class="sc">%&gt;%</span> <span class="fu">bind_rows</span>(reads_dropped) <span class="sc">%&gt;%</span></span>
+<span id="cb36-809"><a href="#cb36-809" aria-hidden="true" tabindex="-1"></a>    <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb36-810"><a href="#cb36-810" aria-hidden="true" tabindex="-1"></a>    <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb36-811"><a href="#cb36-811" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(reads_out)</span>
+<span id="cb36-812"><a href="#cb36-812" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb36-813"><a href="#cb36-813" aria-hidden="true" tabindex="-1"></a>hv_reads_species <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"species"</span>)</span>
+<span id="cb36-814"><a href="#cb36-814" aria-hidden="true" tabindex="-1"></a>hv_reads_genus <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"genus"</span>)</span>
+<span id="cb36-815"><a href="#cb36-815" aria-hidden="true" tabindex="-1"></a>hv_reads_family <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"family"</span>)</span>
+<span id="cb36-816"><a href="#cb36-816" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-817"><a href="#cb36-817" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-820"><a href="#cb36-820" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-821"><a href="#cb36-821" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-families</span></span>
+<span id="cb36-822"><a href="#cb36-822" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-823"><a href="#cb36-823" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral family</span></span>
+<span id="cb36-824"><a href="#cb36-824" aria-hidden="true" tabindex="-1"></a>hv_family_counts <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
+<span id="cb36-825"><a href="#cb36-825" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb36-826"><a href="#cb36-826" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-827"><a href="#cb36-827" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type) <span class="sc">%&gt;%</span></span>
+<span id="cb36-828"><a href="#cb36-828" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb36-829"><a href="#cb36-829" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-830"><a href="#cb36-830" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb36-831"><a href="#cb36-831" aria-hidden="true" tabindex="-1"></a>hv_family_major_tab <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb36-832"><a href="#cb36-832" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-833"><a href="#cb36-833" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> <span class="fl">0.01</span>)</span>
+<span id="cb36-834"><a href="#cb36-834" aria-hidden="true" tabindex="-1"></a>hv_family_counts_major <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span></span>
+<span id="cb36-835"><a href="#cb36-835" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_family_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-836"><a href="#cb36-836" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb36-837"><a href="#cb36-837" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
+<span id="cb36-838"><a href="#cb36-838" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-839"><a href="#cb36-839" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb36-840"><a href="#cb36-840" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_family_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb36-841"><a href="#cb36-841" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-842"><a href="#cb36-842" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot family composition</span></span>
+<span id="cb36-843"><a href="#cb36-843" aria-hidden="true" tabindex="-1"></a>palette <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="st">"#AAAAAA"</span>)</span>
+<span id="cb36-844"><a href="#cb36-844" aria-hidden="true" tabindex="-1"></a>g_hv_families <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(hv_family_counts_major, </span>
+<span id="cb36-845"><a href="#cb36-845" aria-hidden="true" tabindex="-1"></a>                        <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>p_reads_hv, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
+<span id="cb36-846"><a href="#cb36-846" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb36-847"><a href="#cb36-847" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">name=</span><span class="st">"Viral</span><span class="sc">\n</span><span class="st">family"</span>, <span class="at">values=</span>palette) <span class="sc">+</span></span>
+<span id="cb36-848"><a href="#cb36-848" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>)) <span class="sc">+</span></span>
+<span id="cb36-849"><a href="#cb36-849" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>na_type) <span class="sc">+</span></span>
+<span id="cb36-850"><a href="#cb36-850" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
+<span id="cb36-851"><a href="#cb36-851" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb36-852"><a href="#cb36-852" aria-hidden="true" tabindex="-1"></a>g_hv_families</span>
+<span id="cb36-853"><a href="#cb36-853" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-854"><a href="#cb36-854" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-857"><a href="#cb36-857" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-858"><a href="#cb36-858" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-genera</span></span>
+<span id="cb36-859"><a href="#cb36-859" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-860"><a href="#cb36-860" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral genus</span></span>
+<span id="cb36-861"><a href="#cb36-861" aria-hidden="true" tabindex="-1"></a>hv_genus_counts <span class="ot">&lt;-</span> hv_reads_genus <span class="sc">%&gt;%</span> </span>
+<span id="cb36-862"><a href="#cb36-862" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb36-863"><a href="#cb36-863" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-864"><a href="#cb36-864" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type) <span class="sc">%&gt;%</span></span>
+<span id="cb36-865"><a href="#cb36-865" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb36-866"><a href="#cb36-866" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-867"><a href="#cb36-867" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking genera and group others</span></span>
+<span id="cb36-868"><a href="#cb36-868" aria-hidden="true" tabindex="-1"></a>hv_genus_major_tab <span class="ot">&lt;-</span> hv_genus_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb36-869"><a href="#cb36-869" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-870"><a href="#cb36-870" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> <span class="fl">0.02</span>)</span>
+<span id="cb36-871"><a href="#cb36-871" aria-hidden="true" tabindex="-1"></a>hv_genus_counts_major <span class="ot">&lt;-</span> hv_genus_counts <span class="sc">%&gt;%</span></span>
+<span id="cb36-872"><a href="#cb36-872" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_genus_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-873"><a href="#cb36-873" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb36-874"><a href="#cb36-874" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
+<span id="cb36-875"><a href="#cb36-875" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-876"><a href="#cb36-876" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb36-877"><a href="#cb36-877" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_genus_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb36-878"><a href="#cb36-878" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-879"><a href="#cb36-879" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot genus composition</span></span>
+<span id="cb36-880"><a href="#cb36-880" aria-hidden="true" tabindex="-1"></a>palette <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="st">"#AAAAAA"</span>)</span>
+<span id="cb36-881"><a href="#cb36-881" aria-hidden="true" tabindex="-1"></a>g_hv_genera <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(hv_genus_counts_major, </span>
+<span id="cb36-882"><a href="#cb36-882" aria-hidden="true" tabindex="-1"></a>                        <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>p_reads_hv, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
+<span id="cb36-883"><a href="#cb36-883" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb36-884"><a href="#cb36-884" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">name=</span><span class="st">"Viral</span><span class="sc">\n</span><span class="st">genus"</span>, <span class="at">values=</span>palette) <span class="sc">+</span></span>
+<span id="cb36-885"><a href="#cb36-885" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>)) <span class="sc">+</span></span>
+<span id="cb36-886"><a href="#cb36-886" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>na_type) <span class="sc">+</span></span>
+<span id="cb36-887"><a href="#cb36-887" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
+<span id="cb36-888"><a href="#cb36-888" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb36-889"><a href="#cb36-889" aria-hidden="true" tabindex="-1"></a>g_hv_genera</span>
+<span id="cb36-890"><a href="#cb36-890" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-891"><a href="#cb36-891" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-894"><a href="#cb36-894" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-895"><a href="#cb36-895" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-species</span></span>
+<span id="cb36-896"><a href="#cb36-896" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-897"><a href="#cb36-897" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral species</span></span>
+<span id="cb36-898"><a href="#cb36-898" aria-hidden="true" tabindex="-1"></a>hv_species_counts <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span> </span>
+<span id="cb36-899"><a href="#cb36-899" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb36-900"><a href="#cb36-900" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-901"><a href="#cb36-901" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type) <span class="sc">%&gt;%</span></span>
+<span id="cb36-902"><a href="#cb36-902" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb36-903"><a href="#cb36-903" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-904"><a href="#cb36-904" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking species and group others</span></span>
+<span id="cb36-905"><a href="#cb36-905" aria-hidden="true" tabindex="-1"></a>hv_species_major_tab <span class="ot">&lt;-</span> hv_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb36-906"><a href="#cb36-906" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-907"><a href="#cb36-907" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> <span class="fl">0.05</span>)</span>
+<span id="cb36-908"><a href="#cb36-908" aria-hidden="true" tabindex="-1"></a>hv_species_counts_major <span class="ot">&lt;-</span> hv_species_counts <span class="sc">%&gt;%</span></span>
+<span id="cb36-909"><a href="#cb36-909" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_species_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-910"><a href="#cb36-910" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb36-911"><a href="#cb36-911" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
+<span id="cb36-912"><a href="#cb36-912" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-913"><a href="#cb36-913" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb36-914"><a href="#cb36-914" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_species_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb36-915"><a href="#cb36-915" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-916"><a href="#cb36-916" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot species composition</span></span>
+<span id="cb36-917"><a href="#cb36-917" aria-hidden="true" tabindex="-1"></a>palette <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="st">"#AAAAAA"</span>)</span>
+<span id="cb36-918"><a href="#cb36-918" aria-hidden="true" tabindex="-1"></a>g_hv_species <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(hv_species_counts_major, </span>
+<span id="cb36-919"><a href="#cb36-919" aria-hidden="true" tabindex="-1"></a>                      <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>p_reads_hv, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
+<span id="cb36-920"><a href="#cb36-920" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb36-921"><a href="#cb36-921" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">name=</span><span class="st">"Viral</span><span class="sc">\n</span><span class="st">species"</span>, <span class="at">values=</span>palette) <span class="sc">+</span></span>
+<span id="cb36-922"><a href="#cb36-922" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>)) <span class="sc">+</span></span>
+<span id="cb36-923"><a href="#cb36-923" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>na_type) <span class="sc">+</span></span>
+<span id="cb36-924"><a href="#cb36-924" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
+<span id="cb36-925"><a href="#cb36-925" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb36-926"><a href="#cb36-926" aria-hidden="true" tabindex="-1"></a>g_hv_species</span>
+<span id="cb36-927"><a href="#cb36-927" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-928"><a href="#cb36-928" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-929"><a href="#cb36-929" aria-hidden="true" tabindex="-1"></a>By far the most common virus according to my pipeline (with over 91% of human-viral reads) is *Rotavirus A*; second (with 8%) is *Orthopicobirnavirus hominis*. Together, these two enteric RNA viruses dominate HV RNA reads in all samples. Both appear to be real; at least, BLASTN primarily maps these reads to the same or closely-related viruses to that identified by Bowtie2:</span>
+<span id="cb36-930"><a href="#cb36-930" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-933"><a href="#cb36-933" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-934"><a href="#cb36-934" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-rotavirus</span></span>
+<span id="cb36-935"><a href="#cb36-935" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-936"><a href="#cb36-936" aria-hidden="true" tabindex="-1"></a><span class="co"># Import names db</span></span>
+<span id="cb36-937"><a href="#cb36-937" aria-hidden="true" tabindex="-1"></a>tax_names_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"taxid-names.tsv.gz"</span>)</span>
+<span id="cb36-938"><a href="#cb36-938" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(tax_names_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb36-939"><a href="#cb36-939" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-940"><a href="#cb36-940" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing names</span></span>
+<span id="cb36-941"><a href="#cb36-941" aria-hidden="true" tabindex="-1"></a>tax_names_new <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
+<span id="cb36-942"><a href="#cb36-942" aria-hidden="true" tabindex="-1"></a>  <span class="at">staxid =</span> <span class="fu">c</span>(<span class="dv">557241</span>, <span class="dv">557245</span>, <span class="dv">557232</span>, <span class="dv">557247</span>, <span class="dv">557242</span>, <span class="dv">557245</span>),</span>
+<span id="cb36-943"><a href="#cb36-943" aria-hidden="true" tabindex="-1"></a>  <span class="at">name =</span> <span class="fu">c</span>(<span class="st">"Canine rotavirus A79-10/G3P[3]"</span>, <span class="st">"Human rotavirus Ro1845"</span>, <span class="st">"Canine rotavirus K9"</span>,</span>
+<span id="cb36-944"><a href="#cb36-944" aria-hidden="true" tabindex="-1"></a>           <span class="st">"Human rotavirus HCR3A"</span>, <span class="st">"Feline rotavirus Cat97/G3P[3]"</span>, <span class="st">"Feline rotavirus Cat2/G3P[9]"</span>)</span>
+<span id="cb36-945"><a href="#cb36-945" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb36-946"><a href="#cb36-946" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(tax_names, tax_names_new)</span>
+<span id="cb36-947"><a href="#cb36-947" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-948"><a href="#cb36-948" aria-hidden="true" tabindex="-1"></a>rota_ids <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid<span class="sc">==</span><span class="dv">28875</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(seq_id)</span>
+<span id="cb36-949"><a href="#cb36-949" aria-hidden="true" tabindex="-1"></a>rota_hits <span class="ot">&lt;-</span> blast_results_paired <span class="sc">%&gt;%</span></span>
+<span id="cb36-950"><a href="#cb36-950" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> rota_ids) <span class="sc">%&gt;%</span></span>
+<span id="cb36-951"><a href="#cb36-951" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">staxid =</span> <span class="fu">as.integer</span>(staxid))</span>
+<span id="cb36-952"><a href="#cb36-952" aria-hidden="true" tabindex="-1"></a>rota_hits_sorted <span class="ot">&lt;-</span> rota_hits <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(staxid) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(n)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-953"><a href="#cb36-953" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names, <span class="at">by=</span><span class="st">"staxid"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-954"><a href="#cb36-954" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">fct_inorder</span>(name))</span>
+<span id="cb36-955"><a href="#cb36-955" aria-hidden="true" tabindex="-1"></a>rota_hits_sorted_head <span class="ot">&lt;-</span> rota_hits_sorted <span class="sc">%&gt;%</span> <span class="fu">head</span>(<span class="dv">10</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-956"><a href="#cb36-956" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name=</span><span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(name)))</span>
+<span id="cb36-957"><a href="#cb36-957" aria-hidden="true" tabindex="-1"></a>g_rota <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(rota_hits_sorted_head,</span>
+<span id="cb36-958"><a href="#cb36-958" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">aes</span>(<span class="at">x=</span>n, <span class="at">y=</span><span class="fu">fct_inorder</span>(name))) <span class="sc">+</span> <span class="fu">geom_col</span>() <span class="sc">+</span></span>
+<span id="cb36-959"><a href="#cb36-959" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"# Mapped read pairs"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span></span>
+<span id="cb36-960"><a href="#cb36-960" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
+<span id="cb36-961"><a href="#cb36-961" aria-hidden="true" tabindex="-1"></a>g_rota</span>
+<span id="cb36-962"><a href="#cb36-962" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-963"><a href="#cb36-963" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-966"><a href="#cb36-966" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb36-967"><a href="#cb36-967" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-orthopicobirnavirus</span></span>
+<span id="cb36-968"><a href="#cb36-968" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-969"><a href="#cb36-969" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing names</span></span>
+<span id="cb36-970"><a href="#cb36-970" aria-hidden="true" tabindex="-1"></a>tax_names_new <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
+<span id="cb36-971"><a href="#cb36-971" aria-hidden="true" tabindex="-1"></a>  <span class="at">staxid =</span> <span class="fu">c</span>(<span class="dv">442302</span>, <span class="dv">3003954</span>, <span class="dv">585895</span>),</span>
+<span id="cb36-972"><a href="#cb36-972" aria-hidden="true" tabindex="-1"></a>  <span class="at">name =</span> <span class="fu">c</span>(<span class="st">"Porcine picobirnavirus"</span>, <span class="st">"ticpantry virus 7"</span>, <span class="st">"uncultured picobirnavirus"</span>)</span>
+<span id="cb36-973"><a href="#cb36-973" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb36-974"><a href="#cb36-974" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(tax_names, tax_names_new)</span>
+<span id="cb36-975"><a href="#cb36-975" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-976"><a href="#cb36-976" aria-hidden="true" tabindex="-1"></a>pico_ids <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid<span class="sc">==</span><span class="dv">2956252</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(seq_id)</span>
+<span id="cb36-977"><a href="#cb36-977" aria-hidden="true" tabindex="-1"></a>pico_hits <span class="ot">&lt;-</span> blast_results_paired <span class="sc">%&gt;%</span></span>
+<span id="cb36-978"><a href="#cb36-978" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(seq_id <span class="sc">%in%</span> pico_ids) <span class="sc">%&gt;%</span></span>
+<span id="cb36-979"><a href="#cb36-979" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">staxid =</span> <span class="fu">as.integer</span>(staxid))</span>
+<span id="cb36-980"><a href="#cb36-980" aria-hidden="true" tabindex="-1"></a>pico_hits_sorted <span class="ot">&lt;-</span> pico_hits <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(staxid) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(n)) <span class="sc">%&gt;%</span></span>
+<span id="cb36-981"><a href="#cb36-981" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names, <span class="at">by=</span><span class="st">"staxid"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-982"><a href="#cb36-982" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">fct_inorder</span>(name))</span>
+<span id="cb36-983"><a href="#cb36-983" aria-hidden="true" tabindex="-1"></a>pico_hits_sorted_head <span class="ot">&lt;-</span> pico_hits_sorted <span class="sc">%&gt;%</span> <span class="fu">head</span>(<span class="dv">10</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb36-984"><a href="#cb36-984" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name=</span><span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(name)))</span>
+<span id="cb36-985"><a href="#cb36-985" aria-hidden="true" tabindex="-1"></a>g_pico <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(pico_hits_sorted_head,</span>
+<span id="cb36-986"><a href="#cb36-986" aria-hidden="true" tabindex="-1"></a>                 <span class="fu">aes</span>(<span class="at">x=</span>n, <span class="at">y=</span><span class="fu">fct_inorder</span>(name))) <span class="sc">+</span> <span class="fu">geom_col</span>() <span class="sc">+</span></span>
+<span id="cb36-987"><a href="#cb36-987" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"# Mapped read pairs"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span></span>
+<span id="cb36-988"><a href="#cb36-988" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
+<span id="cb36-989"><a href="#cb36-989" aria-hidden="true" tabindex="-1"></a>g_pico</span>
+<span id="cb36-990"><a href="#cb36-990" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb36-991"><a href="#cb36-991" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-992"><a href="#cb36-992" aria-hidden="true" tabindex="-1"></a><span class="fu"># Conclusion</span></span>
+<span id="cb36-993"><a href="#cb36-993" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-994"><a href="#cb36-994" aria-hidden="true" tabindex="-1"></a>I'm quite happy with the quality of the human-viral selection process for this dataset, which ended up achieving very high precision and sensitivity. The most striking result coming out of this analysis is probably the drastic difference in total HV abundance between RNA and DNA reads, with the former <span class="sc">\&gt;</span>100x higher despite very similar processing methods. In the past we've attributed low HV abundance in the DNA datasets we've analyzed to the lack of viral enrichment in available DNA datasets; in this case, however, there is very little difference between the DNA and RNA processing methods, so this explanation doesn't really hold. This makes me more pessimistic about achieving good HV relative abundance with DNA sequencing, even with improved viral enrichment methods.</span>
 </code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div></div></div></div></div>
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+      <div class="quarto-title-block"><div><h1 class="title">Workflow analysis of Maritz et al.&nbsp;(2019)</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
+            <p class="subtitle lead">TODO: Subtitle.</p>
+                      </div>
+  </div>
+    
+  
+  <div class="quarto-title-meta">
+
+      <div>
+      <div class="quarto-title-meta-heading">Author</div>
+      <div class="quarto-title-meta-contents">
+               <p>Will Bradshaw </p>
+            </div>
+    </div>
+      
+      <div>
+      <div class="quarto-title-meta-heading">Published</div>
+      <div class="quarto-title-meta-contents">
+        <p class="date">March 17, 2024</p>
+      </div>
+    </div>
+    
+      
+    </div>
+    
+  
+  </header><div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
+<!-- sidebar -->
+<!-- margin-sidebar -->
+    
+<!-- main -->
+<main class="content quarto-banner-title-block" id="quarto-document-content"><p>Continuing my analysis of datasets from the <a href="https://doi.org/10.1101/2023.12.22.23300450">P2RA preprint</a>, I analyzed the data from <a href="https://doi.org/10.1038/s41396-019-0467-z">Maritz et al.&nbsp;(2019)</a>, a study that used DNA sequencing of wastewater samples to characterize protist diversity and temporal diversity in New York City. Samples for this study underwent direct DNA extraction without a dedicated concentration step, then underwent library prep and Illumina sequencing.</p>
+<section id="the-raw-data" class="level1"><h1>The raw data</h1>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Data input paths</span></span>
+<span><span class="va">data_dir</span> <span class="op">&lt;-</span> <span class="st">"../data/2024-04-08_maritz/"</span></span>
+<span><span class="va">libraries_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"sample-metadata.csv"</span><span class="op">)</span></span>
+<span><span class="va">basic_stats_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"qc_basic_stats.tsv"</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"qc_adapter_stats.tsv"</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"qc_quality_base_stats.tsv"</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"qc_quality_sequence_stats.tsv"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Import libraries and extract metadata from sample names</span></span>
+<span><span class="va">libraries</span> <span class="op">&lt;-</span> <span class="fu">read_csv</span><span class="op">(</span><span class="va">libraries_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Import QC data</span></span>
+<span><span class="va">stages</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"raw_concat"</span>, <span class="st">"cleaned"</span>, <span class="st">"dedup"</span>, <span class="st">"ribo_initial"</span>, <span class="st">"ribo_secondary"</span><span class="op">)</span></span>
+<span><span class="va">basic_stats</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">basic_stats_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>         sample <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">adapter_stats_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>sample <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html">sub</a></span><span class="op">(</span><span class="st">"_2$"</span>, <span class="st">""</span>, <span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>         read_pair <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">quality_base_stats_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>         read_pair <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">quality_seq_stats_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>         read_pair <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Filter to raw data</span></span>
+<span><span class="va">basic_stats_raw</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats_raw</span> <span class="op">&lt;-</span> <span class="va">adapter_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats_raw</span> <span class="op">&lt;-</span> <span class="va">quality_base_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats_raw</span> <span class="op">&lt;-</span> <span class="va">quality_seq_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>The Maritz data comprised 16 samples, with 8.6M to 18.3M read pairs per samples (mean 10.8M). Taken together, the samples totaled roughly 170M read pairs (84 gigabases of sequence). Read quality was high, adapter content was low (but present), duplicate levels were mostly minimal:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize basic stats</span></span>
+<span><span class="va">scale_fill_na</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_fill_brewer</span>, palette<span class="op">=</span><span class="st">"Set1"</span>, name<span class="op">=</span><span class="st">"Nucleic acid type"</span><span class="op">)</span></span>
+<span><span class="va">g_basic</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">basic_stats_raw</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="va">theme_kit</span></span>
+<span><span class="va">g_nreads_raw</span> <span class="op">&lt;-</span> <span class="va">g_basic</span> <span class="op">+</span> <span class="fu">geom_col</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">n_read_pairs</span><span class="op">)</span>, position <span class="op">=</span> <span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Read pairs"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_nreads_raw_2</span> <span class="op">&lt;-</span> <span class="va">g_nreads_raw</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
+<span><span class="va">legend_group</span> <span class="op">&lt;-</span> <span class="fu">get_legend</span><span class="op">(</span><span class="va">g_nreads_raw</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_nbases_raw</span> <span class="op">&lt;-</span> <span class="va">g_basic</span> <span class="op">+</span> <span class="fu">geom_col</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">n_bases_approx</span><span class="op">)</span>, position <span class="op">=</span> <span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Total base pairs (approx)"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">theme</span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
+<span><span class="va">g_ndup_raw</span> <span class="op">&lt;-</span> <span class="va">g_basic</span> <span class="op">+</span> <span class="fu">geom_col</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">percent_duplicates</span><span class="op">)</span>, position <span class="op">=</span> <span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Duplicates (FASTQC)"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">20</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">theme</span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_basic_raw</span> <span class="op">&lt;-</span> <span class="fu">plot_grid</span><span class="op">(</span><span class="va">g_nreads_raw_2</span> <span class="op">+</span> <span class="va">g_nbases_raw</span> <span class="op">+</span> <span class="va">g_ndup_raw</span>, <span class="va">legend_group</span>, ncol <span class="op">=</span> <span class="fl">1</span>, rel_heights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">0.1</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_basic_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-basic-stats-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb3"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">scale_color_na</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_color_brewer</span>,palette<span class="op">=</span><span class="st">"Set1"</span>,name<span class="op">=</span><span class="st">"Nucleic acid type"</span><span class="op">)</span></span>
+<span><span class="va">g_qual_raw</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span>mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>linetype<span class="op">=</span><span class="va">read_pair</span>, </span>
+<span>                         group<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/interaction.html">interaction</a></span><span class="op">(</span><span class="va">sample</span>,<span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_linetype_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Read Pair"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>color<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>,</span>
+<span>         linetype <span class="op">=</span> <span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span></span>
+<span><span class="co"># Visualize adapters</span></span>
+<span><span class="va">g_adapters_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">pc_adapters</span><span class="op">)</span>, data<span class="op">=</span><span class="va">adapter_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Adapters"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">5</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">adapter</span><span class="op">)</span></span>
+<span><span class="va">g_adapters_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-raw-quality-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb4"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize quality</span></span>
+<span><span class="va">g_quality_base_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">mean_phred_score</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_base_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10</span>,<span class="fl">45</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_quality_base_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-raw-quality-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb5"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_quality_seq_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">mean_phred_score</span>, y<span class="op">=</span><span class="va">n_sequences</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_seq_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Sequences"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_quality_seq_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-raw-quality-3.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="preprocessing" class="level1"><h1>Preprocessing</h1>
+<p>The average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. On average, cleaning &amp; deduplication together removed about 8% of total reads, with almost none being removed during subsequent ribodepletion.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb6"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">n_reads_rel</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">stage</span>, <span class="va">percent_duplicates</span>, <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="va">sample</span>, <span class="va">stage</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_retained <span class="op">=</span> <span class="va">n_read_pairs</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/stats/lag.html">lag</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>,</span>
+<span>         p_reads_lost <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">p_reads_retained</span>,</span>
+<span>         p_reads_retained_abs <span class="op">=</span> <span class="va">n_read_pairs</span> <span class="op">/</span> <span class="va">n_read_pairs</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
+<span>         p_reads_lost_abs <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_retained_abs</span>,</span>
+<span>         p_reads_lost_abs_marginal <span class="op">=</span> <span class="va">p_reads_lost_abs</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lag.html">lag</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">n_reads_rel_display</span> <span class="op">&lt;-</span> <span class="va">n_reads_rel</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>Stage<span class="op">=</span><span class="va">stage</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">Stage</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>`% Total Reads Lost (Cumulative)` <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">"-"</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">" (mean "</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">")"</span><span class="op">)</span>,</span>
+<span>            `% Total Reads Lost (Marginal)` <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">"-"</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">" (mean "</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">")"</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">Stage</span> <span class="op">!=</span> <span class="st">"raw_concat"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>Stage <span class="op">=</span> <span class="va">Stage</span> <span class="op">%&gt;%</span> <span class="va">as.numeric</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span>labels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Trimming &amp; filtering"</span>, <span class="st">"Deduplication"</span>, <span class="st">"Initial ribodepletion"</span>, <span class="st">"Secondary ribodepletion"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">n_reads_rel_display</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div data-pagedtable="false">
+  <script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["Stage"],"name":[1],"type":["fct"],"align":["left"]},{"label":["% Total Reads Lost (Cumulative)"],"name":[2],"type":["chr"],"align":["left"]},{"label":["% Total Reads Lost (Marginal)"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"Trimming & filtering","2":"5.6-7.9 (mean 6.2)","3":"5.6-7.9 (mean 6.2)"},{"1":"Deduplication","2":"6.9-21 (mean 8.4)","3":"1.2-13.1 (mean 2.2)"},{"1":"Initial ribodepletion","2":"7.1-21.1 (mean 8.7)","3":"0.1-0.4 (mean 0.3)"},{"1":"Secondary ribodepletion","2":"7.2-21.1 (mean 8.8)","3":"0-0.1 (mean 0.1)"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb7"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Plot reads over preprocessing</span></span>
+<span><span class="va">g_reads_stages</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">basic_stats</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">stage</span>, y<span class="op">=</span><span class="va">n_read_pairs</span> ,group<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position<span class="op">=</span><span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_fill_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"# Read pairs"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_reads_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/preproc-figures-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb8"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Plot relative read losses during preprocessing</span></span>
+<span><span class="va">g_reads_rel</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">n_reads_rel</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">stage</span>, y<span class="op">=</span><span class="va">p_reads_lost_abs_marginal</span>,group<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position<span class="op">=</span><span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_fill_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"% Total Reads Lost"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_reads_rel</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/preproc-figures-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>Data cleaning with FASTP was very successful at removing adapters, with no adapter sequences found by FASTQC at any stage after the raw data. FASTP also slightly increased (already high) read quality.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb9"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_qual</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span>mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>linetype<span class="op">=</span><span class="va">read_pair</span>, </span>
+<span>                         group<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/interaction.html">interaction</a></span><span class="op">(</span><span class="va">sample</span>,<span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_linetype_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Read Pair"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>color<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>,</span>
+<span>         linetype <span class="op">=</span> <span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span></span>
+<span><span class="co"># Visualize adapters</span></span>
+<span><span class="va">g_adapters</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">pc_adapters</span><span class="op">)</span>, data<span class="op">=</span><span class="va">adapter_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Adapters"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">20</span><span class="op">)</span>,</span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">50</span>,<span class="fl">10</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">adapter</span><span class="op">)</span></span>
+<span><span class="va">g_adapters</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-quality-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb10"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize quality</span></span>
+<span><span class="va">g_quality_base</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">mean_phred_score</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_base_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10</span>,<span class="fl">45</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">.</span><span class="op">)</span></span>
+<span><span class="va">g_quality_base</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-quality-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb11"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_quality_seq</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">mean_phred_score</span>, y<span class="op">=</span><span class="va">n_sequences</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_seq_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Sequences"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">.</span><span class="op">)</span></span>
+<span><span class="va">g_quality_seq</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-quality-3.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>According to FASTQC, those few duplicates that were present were mostly removed during deduplication, with mean and maximum FASTQC-measured duplicate levels falling from 4.2%/16.9% to 2.4%/6.9%:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb12"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_dup_stages</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">basic_stats</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">stage</span>, y<span class="op">=</span><span class="va">percent_duplicates</span>, group<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position<span class="op">=</span><span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_fill_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"% Duplicates"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_readlen_stages</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">basic_stats</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">stage</span>, y<span class="op">=</span><span class="va">mean_seq_len</span>, group<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position<span class="op">=</span><span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_fill_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"Mean read length (nt)"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">legend_loc</span> <span class="op">&lt;-</span> <span class="fu">get_legend</span><span class="op">(</span><span class="va">g_dup_stages</span><span class="op">)</span></span>
+<span><span class="va">g_dup_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/preproc-dedup-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb13"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_readlen_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/preproc-dedup-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="high-level-composition" class="level1"><h1>High-level composition</h1>
+<p>As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb14"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import Bracken data</span></span>
+<span><span class="va">bracken_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"bracken_counts.tsv"</span><span class="op">)</span></span>
+<span><span class="va">bracken</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">bracken_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span><span class="va">total_assigned</span> <span class="op">&lt;-</span> <span class="va">bracken</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">summarize</span><span class="op">(</span></span>
+<span>  name <span class="op">=</span> <span class="st">"Total"</span>,</span>
+<span>  kraken_assigned_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">kraken_assigned_reads</span><span class="op">)</span>,</span>
+<span>  added_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">added_reads</span><span class="op">)</span>,</span>
+<span>  new_est_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">new_est_reads</span><span class="op">)</span>,</span>
+<span>  fraction_total_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">fraction_total_reads</span><span class="op">)</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">bracken_spread</span> <span class="op">&lt;-</span> <span class="va">bracken</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">sample</span>, <span class="va">new_est_reads</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/chartr.html">tolower</a></span><span class="op">(</span><span class="va">name</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>id_cols <span class="op">=</span> <span class="st">"sample"</span>, names_from <span class="op">=</span> <span class="st">"name"</span>, values_from <span class="op">=</span> <span class="st">"new_est_reads"</span><span class="op">)</span></span>
+<span><span class="co"># Count reads</span></span>
+<span><span class="va">read_counts_preproc</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">stage</span>, <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>id_cols <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"sample"</span><span class="op">)</span>, names_from<span class="op">=</span><span class="st">"stage"</span>, values_from<span class="op">=</span><span class="st">"n_read_pairs"</span><span class="op">)</span></span>
+<span><span class="va">read_counts</span> <span class="op">&lt;-</span> <span class="va">read_counts_preproc</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">total_assigned</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">new_est_reads</span><span class="op">)</span>, by <span class="op">=</span> <span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>assigned <span class="op">=</span> <span class="va">new_est_reads</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">bracken_spread</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span></span>
+<span><span class="co"># Assess composition</span></span>
+<span><span class="va">read_comp</span> <span class="op">&lt;-</span> <span class="fu">transmute</span><span class="op">(</span><span class="va">read_counts</span>, sample<span class="op">=</span><span class="va">sample</span>,</span>
+<span>                       n_filtered <span class="op">=</span> <span class="va">raw_concat</span><span class="op">-</span><span class="va">cleaned</span>,</span>
+<span>                       n_duplicate <span class="op">=</span> <span class="va">cleaned</span><span class="op">-</span><span class="va">dedup</span>,</span>
+<span>                       n_ribosomal <span class="op">=</span> <span class="op">(</span><span class="va">dedup</span><span class="op">-</span><span class="va">ribo_initial</span><span class="op">)</span> <span class="op">+</span> <span class="op">(</span><span class="va">ribo_initial</span><span class="op">-</span><span class="va">ribo_secondary</span><span class="op">)</span>,</span>
+<span>                       n_unassigned <span class="op">=</span> <span class="va">ribo_secondary</span><span class="op">-</span><span class="va">assigned</span>,</span>
+<span>                       n_bacterial <span class="op">=</span> <span class="va">bacteria</span>,</span>
+<span>                       n_archaeal <span class="op">=</span> <span class="va">archaea</span>,</span>
+<span>                       n_viral <span class="op">=</span> <span class="va">viruses</span>,</span>
+<span>                       n_human <span class="op">=</span> <span class="va">eukaryota</span><span class="op">)</span></span>
+<span><span class="va">read_comp_long</span> <span class="op">&lt;-</span> <span class="fu">pivot_longer</span><span class="op">(</span><span class="va">read_comp</span>, <span class="op">-</span><span class="va">sample</span>, names_to <span class="op">=</span> <span class="st">"classification"</span>,</span>
+<span>                               names_prefix <span class="op">=</span> <span class="st">"n_"</span>, values_to <span class="op">=</span> <span class="st">"n_reads"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu">str_to_sentence</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_reads</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">read_comp_summ</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop_last"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>n_reads <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">n_reads</span>,<span class="fl">0</span><span class="op">)</span>,</span>
+<span>    p_reads <span class="op">=</span> <span class="va">n_reads</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span>,</span>
+<span>    pc_reads <span class="op">=</span> <span class="va">p_reads</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span></span>
+<span>  </span>
+<span><span class="co"># Plot overall composition</span></span>
+<span><span class="va">g_comp</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">read_comp_long</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">sample</span>, y<span class="op">=</span><span class="va">p_reads</span>, fill<span class="op">=</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"% Reads"</span>, limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Set1"</span>, name <span class="op">=</span> <span class="st">"Classification"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_comp</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/unnamed-chunk-9-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb15"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Plot composition of minor components</span></span>
+<span><span class="va">read_comp_minor</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">classification</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Archaeal"</span>, <span class="st">"Viral"</span>, <span class="st">"Human"</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">palette_minor</span> <span class="op">&lt;-</span> <span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">9</span>, <span class="st">"Set1"</span><span class="op">)</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">9</span><span class="op">]</span></span>
+<span><span class="va">g_comp_minor</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">read_comp_minor</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">sample</span>, y<span class="op">=</span><span class="va">p_reads</span>, fill<span class="op">=</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"% Reads"</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0.1</span>,<span class="fl">0.01</span><span class="op">)</span>,</span>
+<span>                     expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_minor</span>, name <span class="op">=</span> <span class="st">"Classification"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_comp_minor</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/unnamed-chunk-9-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb16"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">p_reads_summ_group</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">classification</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Filtered"</span>, <span class="st">"Duplicate"</span>, <span class="st">"Unassigned"</span><span class="op">)</span>, <span class="st">"Excluded"</span>, <span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>         classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">classification</span>, <span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>pc_min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, pc_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, </span>
+<span>            pc_mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ_prep</span> <span class="op">&lt;-</span> <span class="va">p_reads_summ_group</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span>,</span>
+<span>         pc_min <span class="op">=</span> <span class="va">pc_min</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         pc_max <span class="op">=</span> <span class="va">pc_max</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         pc_mean <span class="op">=</span> <span class="va">pc_mean</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">pc_min</span>, <span class="st">"-"</span>, <span class="va">pc_max</span>, <span class="st">"% (mean "</span>, <span class="va">pc_mean</span>, <span class="st">"%)"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ</span> <span class="op">&lt;-</span> <span class="va">p_reads_summ_prep</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span>Classification<span class="op">=</span><span class="va">classification</span>, `% Reads` <span class="op">=</span> <span class="va">display</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div data-pagedtable="false">
+  <script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["Classification"],"name":[1],"type":["fct"],"align":["left"]},{"label":["% Reads"],"name":[2],"type":["chr"],"align":["left"]}],"data":[{"1":"Excluded","2":"44-87% (mean 57%)"},{"1":"Ribosomal","2":"0.1-0.48% (mean 0.4%)"},{"1":"Bacterial","2":"11-55% (mean 42%)"},{"1":"Archaeal","2":"0.011-0.13% (mean 0.022%)"},{"1":"Viral","2":"0.048-0.98% (mean 0.13%)"},{"1":"Human","2":"0.13-0.7% (mean 0.32%)"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</div>
+</div>
+<p>Overall, between 44% and 87% of reads (mean 57%) were low-quality, duplicates, or unassigned. 0.1-0.5% (mean 0.4%) were identified as ribosomal, and 11-55% (mean 42%) were otherwise classified as bacterial. The fraction of human reads ranged from 0.1-0.7% (mean 0.3%); that of viral reads ranged from 0.05%-1% (mean 0.15%).</p>
+<p>This viral fraction is much lower than that seen for the RNA-seq studies I analyzed recently, I assume primarily due to the absence of highly abundant plant viruses. Looking more closely at the viral composition, we see that it is dominated by <em>Suoliviridae</em> and <em>Intestiviridae</em>, two families of <a href="https://en.wikipedia.org/wiki/CrAssphage">CrAss-like phage</a>, as well as <em>Straboviridae</em>, another group of DNA phages:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb17"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">viral_taxa_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"viral-taxids.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">viral_taxa</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">viral_taxa_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span><span class="va">samples</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">basic_stats_raw</span><span class="op">$</span><span class="va">sample</span><span class="op">)</span></span>
+<span><span class="va">col_names</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"pc_reads_total"</span>, <span class="st">"n_reads_clade"</span>, <span class="st">"n_reads_direct"</span>, <span class="st">"rank"</span>, <span class="st">"taxid"</span>, <span class="st">"name"</span><span class="op">)</span></span>
+<span><span class="va">report_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"kraken/"</span>, <span class="va">samples</span>, <span class="st">".report.gz"</span><span class="op">)</span></span>
+<span><span class="va">kraken_reports</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">samples</span><span class="op">)</span>, </span>
+<span>                         <span class="kw">function</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">report_paths</span><span class="op">[</span><span class="va">n</span><span class="op">]</span>, col_names <span class="op">=</span> <span class="va">col_names</span>,</span>
+<span>                                              show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>                           <span class="fu">mutate</span><span class="op">(</span>sample <span class="op">=</span> <span class="va">samples</span><span class="op">[</span><span class="va">n</span><span class="op">]</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span></span>
+<span><span class="va">kraken_reports_viral</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">kraken_reports</span>, <span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_viral <span class="op">=</span> <span class="va">n_reads_clade</span><span class="op">/</span><span class="va">n_reads_clade</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span><span class="va">viral_families</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"F"</span><span class="op">)</span></span>
+<span><span class="va">viral_families_major_list</span> <span class="op">&lt;-</span> <span class="va">viral_families</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_viral_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_viral_max</span> <span class="op">&gt;=</span> <span class="fl">0.01</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span><span class="va">viral_families_major</span> <span class="op">&lt;-</span> <span class="va">viral_families</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_families_major_list</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">p_reads_viral</span><span class="op">)</span></span>
+<span><span class="va">viral_families_minor</span> <span class="op">&lt;-</span> <span class="va">viral_families_major</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_viral_major <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Other"</span>, taxid<span class="op">=</span><span class="cn">NA</span>, p_reads_viral <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_viral_major</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">p_reads_viral</span><span class="op">)</span></span>
+<span><span class="va">viral_families_display</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">viral_families_major</span>, <span class="va">viral_families_minor</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name</span>, levels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">viral_families_major_list</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_families</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">viral_families_display</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">sample</span>, y<span class="op">=</span><span class="va">p_reads_viral</span>, fill<span class="op">=</span><span class="va">name</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position<span class="op">=</span><span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Set3"</span>, name <span class="op">=</span> <span class="st">"Viral family"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Viral Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_families</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/viral-families-all-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="human-infecting-virus-reads-validation" class="level1"><h1>Human-infecting virus reads: validation</h1>
+<p>Next, I investigated the human-infecting virus read content of these unenriched samples, using a pipeline identical to that described in my <a href="https://data.securebio.org/wills-public-notebook/notebooks/2024-02-27_rothman-1.html">entry on unenriched Rothman samples</a>. This process identified a total of 318 read pairs across all samples (0.0002% of surviving reads):</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb18"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">hv_reads_filtered_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"hv_hits_putative_filtered.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">hv_reads_filtered</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">hv_reads_filtered_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>sample <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">n_hv_filtered</span> <span class="op">&lt;-</span> <span class="va">hv_reads_filtered</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span> <span class="op">%&gt;%</span> <span class="fu">inner_join</span><span class="op">(</span><span class="va">basic_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"ribo_initial"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">n_read_pairs</span><span class="op">)</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>n_putative <span class="op">=</span> <span class="va">n</span>, n_total <span class="op">=</span> <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_putative</span><span class="op">/</span><span class="va">n_total</span>, pc_reads <span class="op">=</span> <span class="va">p_reads</span> <span class="op">*</span> <span class="fl">100</span><span class="op">)</span></span>
+<span><span class="va">n_hv_filtered_total</span> <span class="op">&lt;-</span> <span class="va">n_hv_filtered</span> <span class="op">%&gt;%</span> <span class="va">ungroup</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_putative <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_putative</span><span class="op">)</span>, n_total <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_total</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_putative</span><span class="op">/</span><span class="va">n_total</span>, pc_reads <span class="op">=</span> <span class="va">p_reads</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb19"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">mrg</span> <span class="op">&lt;-</span> <span class="va">hv_reads_filtered</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>kraken_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">assigned_hv</span>, <span class="st">"Kraken2 HV\nassignment"</span>,</span>
+<span>                               <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">hit_hv</span>, <span class="st">"Kraken2 HV\nhit"</span>,</span>
+<span>                                      <span class="st">"No hit or\nassignment"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">adj_score_fwd</span><span class="op">)</span>, <span class="fu">desc</span><span class="op">(</span><span class="va">adj_score_rev</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>seq_num <span class="op">=</span> <span class="fu">row_number</span><span class="op">(</span><span class="op">)</span>,</span>
+<span>         adj_score_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">pmax</a></span><span class="op">(</span><span class="va">adj_score_fwd</span>, <span class="va">adj_score_rev</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co"># Import Bowtie2/Kraken data and perform filtering steps</span></span>
+<span><span class="va">g_mrg</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">mrg</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_fwd</span>, y<span class="op">=</span><span class="va">adj_score_rev</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.5</span>, shape<span class="op">=</span><span class="fl">16</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(forward read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(reverse read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">kraken_label</span>, labeller <span class="op">=</span> <span class="fu">labeller</span><span class="op">(</span>kit <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span><span class="fl">20</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>aspect.ratio<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span><span class="va">g_mrg</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-hv-scores-1.png" class="img-fluid figure-img" width="768"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb20"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_hist_0</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">mrg</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_max</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_histogram</span><span class="op">(</span>binwidth<span class="op">=</span><span class="fl">5</span>,boundary<span class="op">=</span><span class="fl">0</span>,position<span class="op">=</span><span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">kraken_label</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Maximum adjusted alignment score"</span>,</span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">200</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Read pairs"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Dark2"</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">20</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span></span>
+<span><span class="va">g_hist_0</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-hv-scores-2.png" class="img-fluid figure-img" width="768"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>I ran these reads through BLASTN in a similar manner to past datasets. The results were surprisingly poor with a significant fraction of high-scoring false-positives:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb21"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">mrg_fasta</span> <span class="op">&lt;-</span>  <span class="va">mrg</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>seq_head <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">"&gt;"</span>, <span class="va">seq_id</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="va">ungroup</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span>header1<span class="op">=</span><span class="va">seq_head</span>, seq1<span class="op">=</span><span class="va">query_seq_fwd</span>, </span>
+<span>         header2<span class="op">=</span><span class="va">seq_head</span>, seq2<span class="op">=</span><span class="va">query_seq_rev</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>header1<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">header1</span>, <span class="st">"_1"</span><span class="op">)</span>, header2<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">header2</span>, <span class="st">"_2"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">mrg_fasta_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/do.call.html">do.call</a></span><span class="op">(</span><span class="va">paste</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">mrg_fasta</span>, sep<span class="op">=</span><span class="st">"\n"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste</a></span><span class="op">(</span>collapse<span class="op">=</span><span class="st">"\n"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/write.html">write</a></span><span class="op">(</span><span class="va">mrg_fasta_out</span>, <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"blast/maritz-putative-viral.fasta"</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb22"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import BLAST results</span></span>
+<span><span class="va">blast_results_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"blast/maritz-putative-viral.blast.gz"</span><span class="op">)</span></span>
+<span><span class="va">blast_cols</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"qseqid"</span>, <span class="st">"sseqid"</span>, <span class="st">"sgi"</span>, <span class="st">"staxid"</span>, <span class="st">"qlen"</span>, <span class="st">"evalue"</span>, <span class="st">"bitscore"</span>, <span class="st">"qcovs"</span>, <span class="st">"length"</span>, <span class="st">"pident"</span>, <span class="st">"mismatch"</span>, <span class="st">"gapopen"</span>, <span class="st">"sstrand"</span>, <span class="st">"qstart"</span>, <span class="st">"qend"</span>, <span class="st">"sstart"</span>, <span class="st">"send"</span><span class="op">)</span></span>
+<span><span class="va">blast_results</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">blast_results_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span>, </span>
+<span>                          col_names <span class="op">=</span> <span class="va">blast_cols</span>, col_types <span class="op">=</span> <span class="fu">cols</span><span class="op">(</span>.default<span class="op">=</span><span class="st">"c"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Filter for best hit for each query/subject</span></span>
+<span><span class="va">blast_results_best</span> <span class="op">&lt;-</span> <span class="va">blast_results</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">qseqid</span>, <span class="va">staxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">bitscore</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">length</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">length</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Rank hits for each query</span></span>
+<span><span class="va">blast_results_ranked</span> <span class="op">&lt;-</span> <span class="va">blast_results_best</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">qseqid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>rank <span class="op">=</span> <span class="fu">dense_rank</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">blast_results_highrank</span> <span class="op">&lt;-</span> <span class="va">blast_results_ranked</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">&lt;=</span> <span class="fl">5</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>read_pair <span class="op">=</span> <span class="fu">str_split</span><span class="op">(</span><span class="va">qseqid</span>, <span class="st">"_"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">nth</span>, n<span class="op">=</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span>,</span>
+<span>         seq_id <span class="op">=</span> <span class="fu">str_split</span><span class="op">(</span><span class="va">qseqid</span>, <span class="st">"_"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">nth</span>, n<span class="op">=</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>bitscore <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">seq_id</span>, <span class="va">read_pair</span>, <span class="fu">everything</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Summarize by read pair and taxid</span></span>
+<span><span class="va">blast_results_paired</span> <span class="op">&lt;-</span> <span class="va">blast_results_highrank</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">seq_id</span>, <span class="va">staxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>bitscore_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span>, bitscore_min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span>,</span>
+<span>            best_rank <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">rank</span><span class="op">)</span>,</span>
+<span>            n_reads <span class="op">=</span> <span class="fu">n</span><span class="op">(</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Add viral status</span></span>
+<span><span class="va">blast_results_viral</span> <span class="op">&lt;-</span> <span class="va">blast_results_paired</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>viral <span class="op">=</span> <span class="va">staxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span>,</span>
+<span>         viral_full <span class="op">=</span> <span class="va">viral</span> <span class="op">&amp;</span> <span class="va">n_reads</span> <span class="op">==</span> <span class="fl">2</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Compare to Kraken &amp; Bowtie assignments</span></span>
+<span><span class="va">mrg_assign</span> <span class="op">&lt;-</span> <span class="va">mrg</span> <span class="op">%&gt;%</span> <span class="va">ungroup</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">seq_id</span>, <span class="va">taxid</span>, <span class="va">assigned_taxid</span><span class="op">)</span></span>
+<span><span class="va">blast_results_assign</span> <span class="op">&lt;-</span> <span class="fu">full_join</span><span class="op">(</span><span class="va">blast_results_viral</span>, <span class="va">mrg_assign</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>taxid_match_bowtie <span class="op">=</span> <span class="op">(</span><span class="va">staxid</span> <span class="op">==</span> <span class="va">taxid</span><span class="op">)</span>,</span>
+<span>           taxid_match_kraken <span class="op">=</span> <span class="op">(</span><span class="va">staxid</span> <span class="op">==</span> <span class="va">assigned_taxid</span><span class="op">)</span>,</span>
+<span>           taxid_match_any <span class="op">=</span> <span class="va">taxid_match_bowtie</span> <span class="op">|</span> <span class="va">taxid_match_kraken</span><span class="op">)</span></span>
+<span><span class="va">blast_results_out</span> <span class="op">&lt;-</span> <span class="va">blast_results_assign</span> <span class="op">%&gt;%</span> <span class="va">ungroup</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>viral_status <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">viral_full</span><span class="op">)</span>, <span class="fl">2</span>,</span>
+<span>                                  <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">taxid_match_any</span><span class="op">)</span>, <span class="fl">2</span>,</span>
+<span>                                             <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">viral</span><span class="op">)</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>            .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>viral_status <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">viral_status</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb23"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Merge BLAST results with unenriched read data</span></span>
+<span><span class="va">mrg_blast</span> <span class="op">&lt;-</span> <span class="fu">full_join</span><span class="op">(</span><span class="va">mrg</span>, <span class="va">blast_results_out</span>, by<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"seq_id"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>viral_status <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">viral_status</span>, <span class="fl">0</span><span class="op">)</span>,</span>
+<span>         viral_status_out <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">viral_status</span> <span class="op">==</span> <span class="fl">0</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_mrg_blast</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">ggplot</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_fwd</span>, y<span class="op">=</span><span class="va">adj_score_rev</span>, color<span class="op">=</span><span class="va">viral_status_out</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.5</span>, shape<span class="op">=</span><span class="fl">16</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(forward read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(reverse read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Set1"</span>, name <span class="op">=</span> <span class="st">"Viral status"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">viral_status_out</span><span class="op">~</span><span class="va">kraken_label</span>, labeller <span class="op">=</span> <span class="fu">labeller</span><span class="op">(</span>kit <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span><span class="fl">20</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>aspect.ratio<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span><span class="va">g_mrg_blast</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/unnamed-chunk-16-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb24"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_hist_1</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">mrg_blast</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_max</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> <span class="fu">geom_histogram</span><span class="op">(</span>binwidth<span class="op">=</span><span class="fl">5</span>,boundary<span class="op">=</span><span class="fl">0</span>,position<span class="op">=</span><span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">viral_status_out</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Maximum adjusted alignment score"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Read pairs"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Dark2"</span><span class="op">)</span> <span class="op">+</span> <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">20</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span></span>
+<span><span class="va">g_hist_1</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/unnamed-chunk-17-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb25"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">test_sens_spec</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">tab</span>, <span class="va">score_threshold</span>, <span class="va">conjunctive</span>, <span class="va">include_special</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="va">include_special</span><span class="op">)</span> <span class="va">tab</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">tab</span>, <span class="va">viral_status_out</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"TRUE"</span>, <span class="st">"FALSE"</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="va">tab_retained</span> <span class="op">&lt;-</span> <span class="va">tab</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span></span>
+<span>    conjunctive <span class="op">=</span> <span class="va">conjunctive</span>,</span>
+<span>    retain_score_conjunctive <span class="op">=</span> <span class="op">(</span><span class="va">adj_score_fwd</span> <span class="op">&gt;</span> <span class="va">score_threshold</span> <span class="op">&amp;</span> <span class="va">adj_score_rev</span> <span class="op">&gt;</span> <span class="va">score_threshold</span><span class="op">)</span>, </span>
+<span>    retain_score_disjunctive <span class="op">=</span> <span class="op">(</span><span class="va">adj_score_fwd</span> <span class="op">&gt;</span> <span class="va">score_threshold</span> <span class="op">|</span> <span class="va">adj_score_rev</span> <span class="op">&gt;</span> <span class="va">score_threshold</span><span class="op">)</span>,</span>
+<span>    retain_score <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">conjunctive</span>, <span class="va">retain_score_conjunctive</span>, <span class="va">retain_score_disjunctive</span><span class="op">)</span>,</span>
+<span>    retain <span class="op">=</span> <span class="va">assigned_hv</span> <span class="op">|</span> <span class="va">hit_hv</span> <span class="op">|</span> <span class="va">retain_score</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span></span>
+<span>  <span class="va">pos_tru</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">==</span> <span class="st">"TRUE"</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">pos_fls</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">!=</span> <span class="st">"TRUE"</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">neg_tru</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">!=</span> <span class="st">"TRUE"</span>, <span class="op">!</span><span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">neg_fls</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">==</span> <span class="st">"TRUE"</span>, <span class="op">!</span><span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">sensitivity</span> <span class="op">&lt;-</span> <span class="va">pos_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">pos_tru</span> <span class="op">+</span> <span class="va">neg_fls</span><span class="op">)</span></span>
+<span>  <span class="va">specificity</span> <span class="op">&lt;-</span> <span class="va">neg_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">neg_tru</span> <span class="op">+</span> <span class="va">pos_fls</span><span class="op">)</span></span>
+<span>  <span class="va">precision</span>   <span class="op">&lt;-</span> <span class="va">pos_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">pos_tru</span> <span class="op">+</span> <span class="va">pos_fls</span><span class="op">)</span></span>
+<span>  <span class="va">f1</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> <span class="va">precision</span> <span class="op">*</span> <span class="va">sensitivity</span> <span class="op">/</span> <span class="op">(</span><span class="va">precision</span> <span class="op">+</span> <span class="va">sensitivity</span><span class="op">)</span></span>
+<span>  <span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="op">(</span>threshold<span class="op">=</span><span class="va">score_threshold</span>, include_special <span class="op">=</span> <span class="va">include_special</span>, </span>
+<span>                conjunctive <span class="op">=</span> <span class="va">conjunctive</span>, sensitivity<span class="op">=</span><span class="va">sensitivity</span>, </span>
+<span>                specificity<span class="op">=</span><span class="va">specificity</span>, precision<span class="op">=</span><span class="va">precision</span>, f1<span class="op">=</span><span class="va">f1</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">out</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">range_f1</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">intab</span>, <span class="va">inc_special</span>, <span class="va">inrange</span><span class="op">=</span><span class="fl">15</span><span class="op">:</span><span class="fl">45</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">tss</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">test_sens_spec</span>, tab<span class="op">=</span><span class="va">intab</span>, include_special<span class="op">=</span><span class="va">inc_special</span><span class="op">)</span></span>
+<span>  <span class="va">stats_conj</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">inrange</span>, <span class="va">tss</span>, conjunctive<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span></span>
+<span>  <span class="va">stats_disj</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">inrange</span>, <span class="va">tss</span>, conjunctive<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span></span>
+<span>  <span class="va">stats_all</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">stats_conj</span>, <span class="va">stats_disj</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">pivot_longer</span><span class="op">(</span><span class="op">!</span><span class="op">(</span><span class="va">threshold</span><span class="op">:</span><span class="va">conjunctive</span><span class="op">)</span>, names_to<span class="op">=</span><span class="st">"metric"</span>, values_to<span class="op">=</span><span class="st">"value"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>conj_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">conjunctive</span>, <span class="st">"Conjunctive"</span>, <span class="st">"Disjunctive"</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">stats_all</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">inc_special</span> <span class="op">&lt;-</span> <span class="cn">FALSE</span></span>
+<span><span class="va">stats_0</span> <span class="op">&lt;-</span> <span class="fu">range_f1</span><span class="op">(</span><span class="va">mrg_blast</span>, <span class="va">inc_special</span><span class="op">)</span></span>
+<span><span class="va">threshold_opt_0</span> <span class="op">&lt;-</span> <span class="va">stats_0</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">conj_label</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">metric</span> <span class="op">==</span> <span class="st">"f1"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">value</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">value</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">threshold</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">threshold</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_stats_0</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">stats_0</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">threshold</span>, y<span class="op">=</span><span class="va">value</span>, color<span class="op">=</span><span class="va">metric</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">threshold_opt_0</span>, mapping <span class="op">=</span> <span class="fu">aes</span><span class="op">(</span>xintercept<span class="op">=</span><span class="va">threshold</span><span class="op">)</span>,</span>
+<span>             color <span class="op">=</span> <span class="st">"red"</span>, linetype <span class="op">=</span> <span class="st">"dashed"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Value"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Threshold"</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_brewer</span><span class="op">(</span>palette<span class="op">=</span><span class="st">"Set3"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="op">~</span><span class="va">conj_label</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span><span class="va">g_stats_0</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/plot-f1-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>TODO: Describe results</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb26"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">major_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.05</span></span>
+<span><span class="va">fp</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, highscore <span class="op">=</span> <span class="va">adj_score_max</span> <span class="op">&gt;=</span> <span class="fl">20</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p<span class="op">=</span><span class="va">n</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">viral_taxa</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">fp_major_tab</span> <span class="op">&lt;-</span> <span class="va">fp</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p</span> <span class="op">&gt;</span> <span class="va">major_threshold</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">fp_major_list</span> <span class="op">&lt;-</span> <span class="va">fp_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sort</span> <span class="op">%&gt;%</span> <span class="va">unique</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">.</span>, <span class="st">"Other"</span><span class="op">)</span></span>
+<span><span class="va">fp_major</span> <span class="op">&lt;-</span> <span class="va">fp</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>major <span class="op">=</span> <span class="va">p</span> <span class="op">&gt;</span> <span class="va">major_threshold</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">major</span>, <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n</span><span class="op">)</span>, p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span>  <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, levels <span class="op">=</span> <span class="va">fp_major_list</span><span class="op">)</span>,</span>
+<span>         score_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">highscore</span>, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span><span class="op">)</span>,</span>
+<span>         status_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="st">"True positive"</span>, <span class="st">"False positive"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">fp_palette</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">12</span>, <span class="st">"Set3"</span><span class="op">)</span>, <span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">8</span>, <span class="st">"Set2"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_fp</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">fp_major</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">score_display</span>, y<span class="op">=</span><span class="va">p</span>, fill<span class="op">=</span><span class="va">name_display</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position<span class="op">=</span><span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"True positive?"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"% reads"</span>, limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">fp_palette</span>, name <span class="op">=</span> <span class="st">"Viral\ntaxon"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">status_display</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">6</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_fp</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/unnamed-chunk-19-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb27"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import names db</span></span>
+<span><span class="va">tax_names_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"taxid-names.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">tax_names</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">tax_names_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Add missing names</span></span>
+<span><span class="va">tax_names_new</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="op">(</span></span>
+<span>  staxid <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">263765</span>, <span class="fl">3082113</span>, <span class="fl">595494</span>, <span class="fl">240869</span>, <span class="fl">312017</span>, <span class="fl">3029053</span><span class="op">)</span>,</span>
+<span>  name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"uncultured Tolumonas sp."</span>,</span>
+<span>           <span class="st">"Rangifer tarandus platyrhynchus"</span>,</span>
+<span>           <span class="st">"Tolumonas auensis DSM 9187"</span>,</span>
+<span>           <span class="st">"Bombylius major"</span>,</span>
+<span>           <span class="st">"Tetrahymena thermophila SB210"</span>,</span>
+<span>           <span class="st">"Mucilaginibacter sp. SJ"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">tax_names</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">tax_names</span>, <span class="va">tax_names_new</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">fp_taxids_major</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="op">!</span><span class="va">viral_status_out</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">n</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">fp_staxid</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="op">!</span><span class="va">viral_status_out</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">taxid</span>, highscore <span class="op">=</span> <span class="va">adj_score_max</span> <span class="op">&gt;=</span> <span class="fl">20</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>n_seq <span class="op">=</span> <span class="fu">n</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">blast_results_paired</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>staxid <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html">as.integer</a></span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">tax_names</span>, by<span class="op">=</span><span class="st">"staxid"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>sname<span class="op">=</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">tax_names</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>taxid<span class="op">=</span><span class="va">staxid</span><span class="op">)</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span><span class="va">fp_staxid_count</span> <span class="op">&lt;-</span> <span class="va">fp_staxid</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">highscore</span>, <span class="va">taxid</span>, <span class="va">name</span>, <span class="va">staxid</span>, <span class="va">sname</span>, <span class="va">n_seq</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="va">count</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">highscore</span>, <span class="va">taxid</span>, <span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p<span class="op">=</span><span class="va">n</span><span class="op">/</span><span class="va">n_seq</span><span class="op">)</span></span>
+<span><span class="va">fp_staxid_count_display</span> <span class="op">&lt;-</span> <span class="va">fp_staxid_count</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">highscore</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">n</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">&lt;=</span> <span class="fl">10</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">fp_taxids_major</span><span class="op">$</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span> <span class="op">|</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">sname</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">fp_staxid_count_display</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">p</span>, y<span class="op">=</span><span class="va">sname</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">name</span><span class="op">~</span><span class="va">highscore</span>, scales<span class="op">=</span><span class="st">"free"</span>,</span>
+<span>             labeller <span class="op">=</span> <span class="fu">labeller</span><span class="op">(</span>name <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span><span class="fl">20</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span><span class="va">g</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-08_maritz_files/figure-html/hv-fp-blast-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb28"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># rota_ids &lt;- hv_reads_species %&gt;% filter(taxid==28875) %&gt;% pull(seq_id)</span></span>
+<span><span class="co"># rota_hits &lt;- blast_results_paired %&gt;%</span></span>
+<span><span class="co">#   filter(seq_id %in% rota_ids) %&gt;%</span></span>
+<span><span class="co">#   mutate(staxid = as.integer(staxid))</span></span>
+<span><span class="co"># rota_hits_sorted &lt;- rota_hits %&gt;% group_by(staxid) %&gt;% count %&gt;% arrange(desc(n)) %&gt;%</span></span>
+<span><span class="co">#   left_join(tax_names, by="staxid") %&gt;%</span></span>
+<span><span class="co">#   mutate(name = fct_inorder(name))</span></span>
+<span><span class="co"># rota_hits_sorted_head &lt;- rota_hits_sorted %&gt;% head(10) %&gt;%</span></span>
+<span><span class="co">#   mutate(name=fct_inorder(as.character(name)))</span></span>
+<span><span class="co"># g_rota &lt;- ggplot(rota_hits_sorted_head,</span></span>
+<span><span class="co">#                   aes(x=n, y=fct_inorder(name))) + geom_col() +</span></span>
+<span><span class="co">#   scale_x_continuous(name = "# Mapped read pairs") + theme_base +</span></span>
+<span><span class="co">#   theme(axis.title.y = element_blank())</span></span>
+<span><span class="co"># g_rota</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
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+</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
+<div class="sourceCode" id="cb29" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Workflow analysis of Maritz et al. (2019)"</span></span>
+<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "TODO: Subtitle."</span></span>
+<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span><span class="co"> "Will Bradshaw"</span></span>
+<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a><span class="an">date:</span><span class="co"> 2024-03-17</span></span>
+<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
+<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
+<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: true</span></span>
+<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
+<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a><span class="co">    code-link: true</span></span>
+<span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a><span class="co">    df-print: paged</span></span>
+<span id="cb29-12"><a href="#cb29-12" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
+<span id="cb29-13"><a href="#cb29-13" aria-hidden="true" tabindex="-1"></a><span class="an">title-block-banner:</span><span class="co"> black</span></span>
+<span id="cb29-14"><a href="#cb29-14" aria-hidden="true" tabindex="-1"></a><span class="an">draft:</span><span class="co"> true</span></span>
+<span id="cb29-15"><a href="#cb29-15" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb29-16"><a href="#cb29-16" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-19"><a href="#cb29-19" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-20"><a href="#cb29-20" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: load-packages</span></span>
+<span id="cb29-21"><a href="#cb29-21" aria-hidden="true" tabindex="-1"></a><span class="co">#| include: false</span></span>
+<span id="cb29-22"><a href="#cb29-22" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
+<span id="cb29-23"><a href="#cb29-23" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(cowplot)</span>
+<span id="cb29-24"><a href="#cb29-24" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(patchwork)</span>
+<span id="cb29-25"><a href="#cb29-25" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(fastqcr)</span>
+<span id="cb29-26"><a href="#cb29-26" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(RColorBrewer)</span>
+<span id="cb29-27"><a href="#cb29-27" aria-hidden="true" tabindex="-1"></a><span class="fu">source</span>(<span class="st">"../scripts/aux_plot-theme.R"</span>)</span>
+<span id="cb29-28"><a href="#cb29-28" aria-hidden="true" tabindex="-1"></a>theme_base <span class="ot">&lt;-</span> theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">aspect.ratio =</span> <span class="cn">NULL</span>)</span>
+<span id="cb29-29"><a href="#cb29-29" aria-hidden="true" tabindex="-1"></a>theme_kit <span class="ot">&lt;-</span> theme_base <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb29-30"><a href="#cb29-30" aria-hidden="true" tabindex="-1"></a>  <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="dv">1</span>, <span class="at">angle =</span> <span class="dv">45</span>),</span>
+<span id="cb29-31"><a href="#cb29-31" aria-hidden="true" tabindex="-1"></a>  <span class="at">axis.title.x =</span> <span class="fu">element_blank</span>(),</span>
+<span id="cb29-32"><a href="#cb29-32" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb29-33"><a href="#cb29-33" aria-hidden="true" tabindex="-1"></a>tnl <span class="ot">&lt;-</span> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
+<span id="cb29-34"><a href="#cb29-34" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-35"><a href="#cb29-35" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-36"><a href="#cb29-36" aria-hidden="true" tabindex="-1"></a>Continuing my analysis of datasets from the <span class="co">[</span><span class="ot">P2RA preprint</span><span class="co">](https://doi.org/10.1101/2023.12.22.23300450)</span>, I analyzed the data from <span class="co">[</span><span class="ot">Maritz et al. (2019)</span><span class="co">](https://doi.org/10.1038/s41396-019-0467-z)</span>, a study that used DNA sequencing of wastewater samples to characterize protist diversity and temporal diversity in New York City. Samples for this study underwent direct DNA extraction without a dedicated concentration step, then underwent library prep and Illumina sequencing.</span>
+<span id="cb29-37"><a href="#cb29-37" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-38"><a href="#cb29-38" aria-hidden="true" tabindex="-1"></a><span class="fu"># The raw data</span></span>
+<span id="cb29-39"><a href="#cb29-39" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-42"><a href="#cb29-42" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-43"><a href="#cb29-43" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: read-qc-data</span></span>
+<span id="cb29-44"><a href="#cb29-44" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-45"><a href="#cb29-45" aria-hidden="true" tabindex="-1"></a><span class="co"># Data input paths</span></span>
+<span id="cb29-46"><a href="#cb29-46" aria-hidden="true" tabindex="-1"></a>data_dir <span class="ot">&lt;-</span> <span class="st">"../data/2024-04-08_maritz/"</span></span>
+<span id="cb29-47"><a href="#cb29-47" aria-hidden="true" tabindex="-1"></a>libraries_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"sample-metadata.csv"</span>)</span>
+<span id="cb29-48"><a href="#cb29-48" aria-hidden="true" tabindex="-1"></a>basic_stats_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"qc_basic_stats.tsv"</span>)</span>
+<span id="cb29-49"><a href="#cb29-49" aria-hidden="true" tabindex="-1"></a>adapter_stats_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"qc_adapter_stats.tsv"</span>)</span>
+<span id="cb29-50"><a href="#cb29-50" aria-hidden="true" tabindex="-1"></a>quality_base_stats_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"qc_quality_base_stats.tsv"</span>)</span>
+<span id="cb29-51"><a href="#cb29-51" aria-hidden="true" tabindex="-1"></a>quality_seq_stats_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"qc_quality_sequence_stats.tsv"</span>)</span>
+<span id="cb29-52"><a href="#cb29-52" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-53"><a href="#cb29-53" aria-hidden="true" tabindex="-1"></a><span class="co"># Import libraries and extract metadata from sample names</span></span>
+<span id="cb29-54"><a href="#cb29-54" aria-hidden="true" tabindex="-1"></a>libraries <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(libraries_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb29-55"><a href="#cb29-55" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-56"><a href="#cb29-56" aria-hidden="true" tabindex="-1"></a><span class="co"># Import QC data</span></span>
+<span id="cb29-57"><a href="#cb29-57" aria-hidden="true" tabindex="-1"></a>stages <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"raw_concat"</span>, <span class="st">"cleaned"</span>, <span class="st">"dedup"</span>, <span class="st">"ribo_initial"</span>, <span class="st">"ribo_secondary"</span>)</span>
+<span id="cb29-58"><a href="#cb29-58" aria-hidden="true" tabindex="-1"></a>basic_stats <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(basic_stats_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-59"><a href="#cb29-59" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-60"><a href="#cb29-60" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb29-61"><a href="#cb29-61" aria-hidden="true" tabindex="-1"></a>         <span class="at">sample =</span> <span class="fu">fct_inorder</span>(sample))</span>
+<span id="cb29-62"><a href="#cb29-62" aria-hidden="true" tabindex="-1"></a>adapter_stats <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(adapter_stats_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-63"><a href="#cb29-63" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">sample =</span> <span class="fu">sub</span>(<span class="st">"_2$"</span>, <span class="st">""</span>, sample)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-64"><a href="#cb29-64" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-65"><a href="#cb29-65" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb29-66"><a href="#cb29-66" aria-hidden="true" tabindex="-1"></a>         <span class="at">read_pair =</span> <span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(read_pair)))</span>
+<span id="cb29-67"><a href="#cb29-67" aria-hidden="true" tabindex="-1"></a>quality_base_stats <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(quality_base_stats_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-68"><a href="#cb29-68" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-69"><a href="#cb29-69" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb29-70"><a href="#cb29-70" aria-hidden="true" tabindex="-1"></a>         <span class="at">read_pair =</span> <span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(read_pair)))</span>
+<span id="cb29-71"><a href="#cb29-71" aria-hidden="true" tabindex="-1"></a>quality_seq_stats <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(quality_seq_stats_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-72"><a href="#cb29-72" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-73"><a href="#cb29-73" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb29-74"><a href="#cb29-74" aria-hidden="true" tabindex="-1"></a>         <span class="at">read_pair =</span> <span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(read_pair)))</span>
+<span id="cb29-75"><a href="#cb29-75" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-76"><a href="#cb29-76" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter to raw data</span></span>
+<span id="cb29-77"><a href="#cb29-77" aria-hidden="true" tabindex="-1"></a>basic_stats_raw <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb29-78"><a href="#cb29-78" aria-hidden="true" tabindex="-1"></a>adapter_stats_raw <span class="ot">&lt;-</span> adapter_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb29-79"><a href="#cb29-79" aria-hidden="true" tabindex="-1"></a>quality_base_stats_raw <span class="ot">&lt;-</span> quality_base_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb29-80"><a href="#cb29-80" aria-hidden="true" tabindex="-1"></a>quality_seq_stats_raw <span class="ot">&lt;-</span> quality_seq_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb29-81"><a href="#cb29-81" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-82"><a href="#cb29-82" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-83"><a href="#cb29-83" aria-hidden="true" tabindex="-1"></a>The Maritz data comprised 16 samples, with 8.6M to 18.3M read pairs per samples (mean 10.8M). Taken together, the samples totaled roughly 170M read pairs (84 gigabases of sequence). Read quality was high, adapter content was low (but present), duplicate levels were mostly minimal:</span>
+<span id="cb29-84"><a href="#cb29-84" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-87"><a href="#cb29-87" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-88"><a href="#cb29-88" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 5</span></span>
+<span id="cb29-89"><a href="#cb29-89" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-basic-stats</span></span>
+<span id="cb29-90"><a href="#cb29-90" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-91"><a href="#cb29-91" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize basic stats</span></span>
+<span id="cb29-92"><a href="#cb29-92" aria-hidden="true" tabindex="-1"></a>scale_fill_na <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_fill_brewer, <span class="at">palette=</span><span class="st">"Set1"</span>, <span class="at">name=</span><span class="st">"Nucleic acid type"</span>)</span>
+<span id="cb29-93"><a href="#cb29-93" aria-hidden="true" tabindex="-1"></a>g_basic <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(basic_stats_raw, <span class="fu">aes</span>(<span class="at">x=</span>sample)) <span class="sc">+</span></span>
+<span id="cb29-94"><a href="#cb29-94" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_na</span>() <span class="sc">+</span> theme_kit</span>
+<span id="cb29-95"><a href="#cb29-95" aria-hidden="true" tabindex="-1"></a>g_nreads_raw <span class="ot">&lt;-</span> g_basic <span class="sc">+</span> <span class="fu">geom_col</span>(<span class="fu">aes</span>(<span class="at">y=</span>n_read_pairs), <span class="at">position =</span> <span class="st">"dodge"</span>) <span class="sc">+</span></span>
+<span id="cb29-96"><a href="#cb29-96" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))</span>
+<span id="cb29-97"><a href="#cb29-97" aria-hidden="true" tabindex="-1"></a>g_nreads_raw_2 <span class="ot">&lt;-</span> g_nreads_raw <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
+<span id="cb29-98"><a href="#cb29-98" aria-hidden="true" tabindex="-1"></a>legend_group <span class="ot">&lt;-</span> <span class="fu">get_legend</span>(g_nreads_raw)</span>
+<span id="cb29-99"><a href="#cb29-99" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-100"><a href="#cb29-100" aria-hidden="true" tabindex="-1"></a>g_nbases_raw <span class="ot">&lt;-</span> g_basic <span class="sc">+</span> <span class="fu">geom_col</span>(<span class="fu">aes</span>(<span class="at">y=</span>n_bases_approx), <span class="at">position =</span> <span class="st">"dodge"</span>) <span class="sc">+</span></span>
+<span id="cb29-101"><a href="#cb29-101" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"Total base pairs (approx)"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span> </span>
+<span id="cb29-102"><a href="#cb29-102" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
+<span id="cb29-103"><a href="#cb29-103" aria-hidden="true" tabindex="-1"></a>g_ndup_raw <span class="ot">&lt;-</span> g_basic <span class="sc">+</span> <span class="fu">geom_col</span>(<span class="fu">aes</span>(<span class="at">y=</span>percent_duplicates), <span class="at">position =</span> <span class="st">"dodge"</span>) <span class="sc">+</span></span>
+<span id="cb29-104"><a href="#cb29-104" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Duplicates (FASTQC)"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">100</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">20</span>)) <span class="sc">+</span></span>
+<span id="cb29-105"><a href="#cb29-105" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
+<span id="cb29-106"><a href="#cb29-106" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-107"><a href="#cb29-107" aria-hidden="true" tabindex="-1"></a>g_basic_raw <span class="ot">&lt;-</span> <span class="fu">plot_grid</span>(g_nreads_raw_2 <span class="sc">+</span> g_nbases_raw <span class="sc">+</span> g_ndup_raw, legend_group, <span class="at">ncol =</span> <span class="dv">1</span>, <span class="at">rel_heights =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="fl">0.1</span>))</span>
+<span id="cb29-108"><a href="#cb29-108" aria-hidden="true" tabindex="-1"></a>g_basic_raw</span>
+<span id="cb29-109"><a href="#cb29-109" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-110"><a href="#cb29-110" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-113"><a href="#cb29-113" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-114"><a href="#cb29-114" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-raw-quality</span></span>
+<span id="cb29-115"><a href="#cb29-115" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-116"><a href="#cb29-116" aria-hidden="true" tabindex="-1"></a>scale_color_na <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_color_brewer,<span class="at">palette=</span><span class="st">"Set1"</span>,<span class="at">name=</span><span class="st">"Nucleic acid type"</span>)</span>
+<span id="cb29-117"><a href="#cb29-117" aria-hidden="true" tabindex="-1"></a>g_qual_raw <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">linetype=</span>read_pair, </span>
+<span id="cb29-118"><a href="#cb29-118" aria-hidden="true" tabindex="-1"></a>                         <span class="at">group=</span><span class="fu">interaction</span>(sample,read_pair))) <span class="sc">+</span> </span>
+<span id="cb29-119"><a href="#cb29-119" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span> <span class="fu">scale_linetype_discrete</span>(<span class="at">name =</span> <span class="st">"Read Pair"</span>) <span class="sc">+</span></span>
+<span id="cb29-120"><a href="#cb29-120" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>),</span>
+<span id="cb29-121"><a href="#cb29-121" aria-hidden="true" tabindex="-1"></a>         <span class="at">linetype =</span> <span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>)) <span class="sc">+</span></span>
+<span id="cb29-122"><a href="#cb29-122" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb29-123"><a href="#cb29-123" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-124"><a href="#cb29-124" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize adapters</span></span>
+<span id="cb29-125"><a href="#cb29-125" aria-hidden="true" tabindex="-1"></a>g_adapters_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span> </span>
+<span id="cb29-126"><a href="#cb29-126" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>pc_adapters), <span class="at">data=</span>adapter_stats_raw) <span class="sc">+</span></span>
+<span id="cb29-127"><a href="#cb29-127" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Adapters"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb29-128"><a href="#cb29-128" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">5</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-129"><a href="#cb29-129" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb29-130"><a href="#cb29-130" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-131"><a href="#cb29-131" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>adapter)</span>
+<span id="cb29-132"><a href="#cb29-132" aria-hidden="true" tabindex="-1"></a>g_adapters_raw</span>
+<span id="cb29-133"><a href="#cb29-133" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-134"><a href="#cb29-134" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize quality</span></span>
+<span id="cb29-135"><a href="#cb29-135" aria-hidden="true" tabindex="-1"></a>g_quality_base_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span></span>
+<span id="cb29-136"><a href="#cb29-136" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb29-137"><a href="#cb29-137" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb29-138"><a href="#cb29-138" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>mean_phred_score), <span class="at">data=</span>quality_base_stats_raw) <span class="sc">+</span></span>
+<span id="cb29-139"><a href="#cb29-139" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">10</span>,<span class="dv">45</span>)) <span class="sc">+</span></span>
+<span id="cb29-140"><a href="#cb29-140" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb29-141"><a href="#cb29-141" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))</span>
+<span id="cb29-142"><a href="#cb29-142" aria-hidden="true" tabindex="-1"></a>g_quality_base_raw</span>
+<span id="cb29-143"><a href="#cb29-143" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-144"><a href="#cb29-144" aria-hidden="true" tabindex="-1"></a>g_quality_seq_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span></span>
+<span id="cb29-145"><a href="#cb29-145" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb29-146"><a href="#cb29-146" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb29-147"><a href="#cb29-147" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>mean_phred_score, <span class="at">y=</span>n_sequences), <span class="at">data=</span>quality_seq_stats_raw) <span class="sc">+</span></span>
+<span id="cb29-148"><a href="#cb29-148" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-149"><a href="#cb29-149" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Sequences"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))</span>
+<span id="cb29-150"><a href="#cb29-150" aria-hidden="true" tabindex="-1"></a>g_quality_seq_raw</span>
+<span id="cb29-151"><a href="#cb29-151" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-152"><a href="#cb29-152" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-153"><a href="#cb29-153" aria-hidden="true" tabindex="-1"></a><span class="fu"># Preprocessing</span></span>
+<span id="cb29-154"><a href="#cb29-154" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-155"><a href="#cb29-155" aria-hidden="true" tabindex="-1"></a>The average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. On average, cleaning &amp; deduplication together removed about 8% of total reads, with almost none being removed during subsequent ribodepletion.</span>
+<span id="cb29-156"><a href="#cb29-156" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-159"><a href="#cb29-159" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-160"><a href="#cb29-160" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-table</span></span>
+<span id="cb29-161"><a href="#cb29-161" aria-hidden="true" tabindex="-1"></a>n_reads_rel <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> </span>
+<span id="cb29-162"><a href="#cb29-162" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, stage, percent_duplicates, n_read_pairs) <span class="sc">%&gt;%</span></span>
+<span id="cb29-163"><a href="#cb29-163" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(sample, stage) <span class="sc">%&gt;%</span></span>
+<span id="cb29-164"><a href="#cb29-164" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_retained =</span> n_read_pairs <span class="sc">/</span> <span class="fu">lag</span>(n_read_pairs),</span>
+<span id="cb29-165"><a href="#cb29-165" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost =</span> <span class="dv">1</span> <span class="sc">-</span> p_reads_retained,</span>
+<span id="cb29-166"><a href="#cb29-166" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_retained_abs =</span> n_read_pairs <span class="sc">/</span> n_read_pairs[<span class="dv">1</span>],</span>
+<span id="cb29-167"><a href="#cb29-167" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost_abs =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_retained_abs,</span>
+<span id="cb29-168"><a href="#cb29-168" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost_abs_marginal =</span> p_reads_lost_abs <span class="sc">-</span> <span class="fu">lag</span>(p_reads_lost_abs))</span>
+<span id="cb29-169"><a href="#cb29-169" aria-hidden="true" tabindex="-1"></a>n_reads_rel_display <span class="ot">&lt;-</span> n_reads_rel <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">Stage=</span>stage) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-170"><a href="#cb29-170" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Stage) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-171"><a href="#cb29-171" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="st">`</span><span class="at">% Total Reads Lost (Cumulative)</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">paste0</span>(<span class="fu">round</span>(<span class="fu">min</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">"-"</span>, <span class="fu">round</span>(<span class="fu">max</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">" (mean "</span>, <span class="fu">round</span>(<span class="fu">mean</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">")"</span>),</span>
+<span id="cb29-172"><a href="#cb29-172" aria-hidden="true" tabindex="-1"></a>            <span class="st">`</span><span class="at">% Total Reads Lost (Marginal)</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">paste0</span>(<span class="fu">round</span>(<span class="fu">min</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">"-"</span>, <span class="fu">round</span>(<span class="fu">max</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">" (mean "</span>, <span class="fu">round</span>(<span class="fu">mean</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">")"</span>), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-173"><a href="#cb29-173" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(Stage <span class="sc">!=</span> <span class="st">"raw_concat"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-174"><a href="#cb29-174" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Stage =</span> Stage <span class="sc">%&gt;%</span> as.numeric <span class="sc">%&gt;%</span> <span class="fu">factor</span>(<span class="at">labels=</span><span class="fu">c</span>(<span class="st">"Trimming &amp; filtering"</span>, <span class="st">"Deduplication"</span>, <span class="st">"Initial ribodepletion"</span>, <span class="st">"Secondary ribodepletion"</span>)))</span>
+<span id="cb29-175"><a href="#cb29-175" aria-hidden="true" tabindex="-1"></a>n_reads_rel_display</span>
+<span id="cb29-176"><a href="#cb29-176" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-177"><a href="#cb29-177" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-180"><a href="#cb29-180" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-181"><a href="#cb29-181" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-figures</span></span>
+<span id="cb29-182"><a href="#cb29-182" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb29-183"><a href="#cb29-183" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot reads over preprocessing</span></span>
+<span id="cb29-184"><a href="#cb29-184" aria-hidden="true" tabindex="-1"></a>g_reads_stages <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(basic_stats, <span class="fu">aes</span>(<span class="at">x=</span>stage, <span class="at">y=</span>n_read_pairs ,<span class="at">group=</span>sample)) <span class="sc">+</span></span>
+<span id="cb29-185"><a href="#cb29-185" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">scale_fill_na</span>() <span class="sc">+</span></span>
+<span id="cb29-186"><a href="#cb29-186" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"# Read pairs"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-187"><a href="#cb29-187" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb29-188"><a href="#cb29-188" aria-hidden="true" tabindex="-1"></a>g_reads_stages</span>
+<span id="cb29-189"><a href="#cb29-189" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-190"><a href="#cb29-190" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot relative read losses during preprocessing</span></span>
+<span id="cb29-191"><a href="#cb29-191" aria-hidden="true" tabindex="-1"></a>g_reads_rel <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(n_reads_rel, <span class="fu">aes</span>(<span class="at">x=</span>stage, <span class="at">y=</span>p_reads_lost_abs_marginal,<span class="at">group=</span>sample)) <span class="sc">+</span></span>
+<span id="cb29-192"><a href="#cb29-192" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">scale_fill_na</span>() <span class="sc">+</span></span>
+<span id="cb29-193"><a href="#cb29-193" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"% Total Reads Lost"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(x) x<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb29-194"><a href="#cb29-194" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb29-195"><a href="#cb29-195" aria-hidden="true" tabindex="-1"></a>g_reads_rel</span>
+<span id="cb29-196"><a href="#cb29-196" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-197"><a href="#cb29-197" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-198"><a href="#cb29-198" aria-hidden="true" tabindex="-1"></a>Data cleaning with FASTP was very successful at removing adapters, with no adapter sequences found by FASTQC at any stage after the raw data. FASTP also slightly increased (already high) read quality.</span>
+<span id="cb29-199"><a href="#cb29-199" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-202"><a href="#cb29-202" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-203"><a href="#cb29-203" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb29-204"><a href="#cb29-204" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-quality</span></span>
+<span id="cb29-205"><a href="#cb29-205" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-206"><a href="#cb29-206" aria-hidden="true" tabindex="-1"></a>g_qual <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">linetype=</span>read_pair, </span>
+<span id="cb29-207"><a href="#cb29-207" aria-hidden="true" tabindex="-1"></a>                         <span class="at">group=</span><span class="fu">interaction</span>(sample,read_pair))) <span class="sc">+</span> </span>
+<span id="cb29-208"><a href="#cb29-208" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span> <span class="fu">scale_linetype_discrete</span>(<span class="at">name =</span> <span class="st">"Read Pair"</span>) <span class="sc">+</span></span>
+<span id="cb29-209"><a href="#cb29-209" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>),</span>
+<span id="cb29-210"><a href="#cb29-210" aria-hidden="true" tabindex="-1"></a>         <span class="at">linetype =</span> <span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>)) <span class="sc">+</span></span>
+<span id="cb29-211"><a href="#cb29-211" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb29-212"><a href="#cb29-212" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-213"><a href="#cb29-213" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize adapters</span></span>
+<span id="cb29-214"><a href="#cb29-214" aria-hidden="true" tabindex="-1"></a>g_adapters <span class="ot">&lt;-</span> g_qual <span class="sc">+</span> </span>
+<span id="cb29-215"><a href="#cb29-215" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>pc_adapters), <span class="at">data=</span>adapter_stats) <span class="sc">+</span></span>
+<span id="cb29-216"><a href="#cb29-216" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Adapters"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">20</span>),</span>
+<span id="cb29-217"><a href="#cb29-217" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">50</span>,<span class="dv">10</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-218"><a href="#cb29-218" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb29-219"><a href="#cb29-219" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-220"><a href="#cb29-220" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>adapter)</span>
+<span id="cb29-221"><a href="#cb29-221" aria-hidden="true" tabindex="-1"></a>g_adapters</span>
+<span id="cb29-222"><a href="#cb29-222" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-223"><a href="#cb29-223" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize quality</span></span>
+<span id="cb29-224"><a href="#cb29-224" aria-hidden="true" tabindex="-1"></a>g_quality_base <span class="ot">&lt;-</span> g_qual <span class="sc">+</span></span>
+<span id="cb29-225"><a href="#cb29-225" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb29-226"><a href="#cb29-226" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb29-227"><a href="#cb29-227" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>mean_phred_score), <span class="at">data=</span>quality_base_stats) <span class="sc">+</span></span>
+<span id="cb29-228"><a href="#cb29-228" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">10</span>,<span class="dv">45</span>)) <span class="sc">+</span></span>
+<span id="cb29-229"><a href="#cb29-229" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb29-230"><a href="#cb29-230" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-231"><a href="#cb29-231" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>.)</span>
+<span id="cb29-232"><a href="#cb29-232" aria-hidden="true" tabindex="-1"></a>g_quality_base</span>
+<span id="cb29-233"><a href="#cb29-233" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-234"><a href="#cb29-234" aria-hidden="true" tabindex="-1"></a>g_quality_seq <span class="ot">&lt;-</span> g_qual <span class="sc">+</span></span>
+<span id="cb29-235"><a href="#cb29-235" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb29-236"><a href="#cb29-236" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb29-237"><a href="#cb29-237" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>mean_phred_score, <span class="at">y=</span>n_sequences), <span class="at">data=</span>quality_seq_stats) <span class="sc">+</span></span>
+<span id="cb29-238"><a href="#cb29-238" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-239"><a href="#cb29-239" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Sequences"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-240"><a href="#cb29-240" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>.)</span>
+<span id="cb29-241"><a href="#cb29-241" aria-hidden="true" tabindex="-1"></a>g_quality_seq</span>
+<span id="cb29-242"><a href="#cb29-242" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-243"><a href="#cb29-243" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-244"><a href="#cb29-244" aria-hidden="true" tabindex="-1"></a>According to FASTQC, those few duplicates that were present were mostly removed during deduplication, with mean and maximum FASTQC-measured duplicate levels falling from 4.2%/16.9% to 2.4%/6.9%:</span>
+<span id="cb29-245"><a href="#cb29-245" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-248"><a href="#cb29-248" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-249"><a href="#cb29-249" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-dedup</span></span>
+<span id="cb29-250"><a href="#cb29-250" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-251"><a href="#cb29-251" aria-hidden="true" tabindex="-1"></a>g_dup_stages <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(basic_stats, <span class="fu">aes</span>(<span class="at">x=</span>stage, <span class="at">y=</span>percent_duplicates, <span class="at">group=</span>sample)) <span class="sc">+</span></span>
+<span id="cb29-252"><a href="#cb29-252" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">scale_fill_na</span>() <span class="sc">+</span></span>
+<span id="cb29-253"><a href="#cb29-253" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"% Duplicates"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">100</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-254"><a href="#cb29-254" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb29-255"><a href="#cb29-255" aria-hidden="true" tabindex="-1"></a>g_readlen_stages <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(basic_stats, <span class="fu">aes</span>(<span class="at">x=</span>stage, <span class="at">y=</span>mean_seq_len, <span class="at">group=</span>sample)) <span class="sc">+</span></span>
+<span id="cb29-256"><a href="#cb29-256" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">scale_fill_na</span>() <span class="sc">+</span></span>
+<span id="cb29-257"><a href="#cb29-257" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"Mean read length (nt)"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-258"><a href="#cb29-258" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb29-259"><a href="#cb29-259" aria-hidden="true" tabindex="-1"></a>legend_loc <span class="ot">&lt;-</span> <span class="fu">get_legend</span>(g_dup_stages)</span>
+<span id="cb29-260"><a href="#cb29-260" aria-hidden="true" tabindex="-1"></a>g_dup_stages</span>
+<span id="cb29-261"><a href="#cb29-261" aria-hidden="true" tabindex="-1"></a>g_readlen_stages</span>
+<span id="cb29-262"><a href="#cb29-262" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-263"><a href="#cb29-263" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-264"><a href="#cb29-264" aria-hidden="true" tabindex="-1"></a><span class="fu"># High-level composition</span></span>
+<span id="cb29-265"><a href="#cb29-265" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-266"><a href="#cb29-266" aria-hidden="true" tabindex="-1"></a>As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:</span>
+<span id="cb29-267"><a href="#cb29-267" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-270"><a href="#cb29-270" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-271"><a href="#cb29-271" aria-hidden="true" tabindex="-1"></a><span class="co"># Import Bracken data</span></span>
+<span id="cb29-272"><a href="#cb29-272" aria-hidden="true" tabindex="-1"></a>bracken_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"bracken_counts.tsv"</span>)</span>
+<span id="cb29-273"><a href="#cb29-273" aria-hidden="true" tabindex="-1"></a>bracken <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(bracken_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb29-274"><a href="#cb29-274" aria-hidden="true" tabindex="-1"></a>total_assigned <span class="ot">&lt;-</span> bracken <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">summarize</span>(</span>
+<span id="cb29-275"><a href="#cb29-275" aria-hidden="true" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">"Total"</span>,</span>
+<span id="cb29-276"><a href="#cb29-276" aria-hidden="true" tabindex="-1"></a>  <span class="at">kraken_assigned_reads =</span> <span class="fu">sum</span>(kraken_assigned_reads),</span>
+<span id="cb29-277"><a href="#cb29-277" aria-hidden="true" tabindex="-1"></a>  <span class="at">added_reads =</span> <span class="fu">sum</span>(added_reads),</span>
+<span id="cb29-278"><a href="#cb29-278" aria-hidden="true" tabindex="-1"></a>  <span class="at">new_est_reads =</span> <span class="fu">sum</span>(new_est_reads),</span>
+<span id="cb29-279"><a href="#cb29-279" aria-hidden="true" tabindex="-1"></a>  <span class="at">fraction_total_reads =</span> <span class="fu">sum</span>(fraction_total_reads)</span>
+<span id="cb29-280"><a href="#cb29-280" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb29-281"><a href="#cb29-281" aria-hidden="true" tabindex="-1"></a>bracken_spread <span class="ot">&lt;-</span> bracken <span class="sc">%&gt;%</span> <span class="fu">select</span>(name, sample, new_est_reads) <span class="sc">%&gt;%</span></span>
+<span id="cb29-282"><a href="#cb29-282" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">tolower</span>(name)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-283"><a href="#cb29-283" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">id_cols =</span> <span class="st">"sample"</span>, <span class="at">names_from =</span> <span class="st">"name"</span>, <span class="at">values_from =</span> <span class="st">"new_est_reads"</span>)</span>
+<span id="cb29-284"><a href="#cb29-284" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads</span></span>
+<span id="cb29-285"><a href="#cb29-285" aria-hidden="true" tabindex="-1"></a>read_counts_preproc <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> </span>
+<span id="cb29-286"><a href="#cb29-286" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, stage, n_read_pairs) <span class="sc">%&gt;%</span></span>
+<span id="cb29-287"><a href="#cb29-287" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">id_cols =</span> <span class="fu">c</span>(<span class="st">"sample"</span>), <span class="at">names_from=</span><span class="st">"stage"</span>, <span class="at">values_from=</span><span class="st">"n_read_pairs"</span>)</span>
+<span id="cb29-288"><a href="#cb29-288" aria-hidden="true" tabindex="-1"></a>read_counts <span class="ot">&lt;-</span> read_counts_preproc <span class="sc">%&gt;%</span></span>
+<span id="cb29-289"><a href="#cb29-289" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(total_assigned <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, new_est_reads), <span class="at">by =</span> <span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-290"><a href="#cb29-290" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">assigned =</span> new_est_reads) <span class="sc">%&gt;%</span></span>
+<span id="cb29-291"><a href="#cb29-291" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(bracken_spread, <span class="at">by=</span><span class="st">"sample"</span>)</span>
+<span id="cb29-292"><a href="#cb29-292" aria-hidden="true" tabindex="-1"></a><span class="co"># Assess composition</span></span>
+<span id="cb29-293"><a href="#cb29-293" aria-hidden="true" tabindex="-1"></a>read_comp <span class="ot">&lt;-</span> <span class="fu">transmute</span>(read_counts, <span class="at">sample=</span>sample,</span>
+<span id="cb29-294"><a href="#cb29-294" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_filtered =</span> raw_concat<span class="sc">-</span>cleaned,</span>
+<span id="cb29-295"><a href="#cb29-295" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_duplicate =</span> cleaned<span class="sc">-</span>dedup,</span>
+<span id="cb29-296"><a href="#cb29-296" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_ribosomal =</span> (dedup<span class="sc">-</span>ribo_initial) <span class="sc">+</span> (ribo_initial<span class="sc">-</span>ribo_secondary),</span>
+<span id="cb29-297"><a href="#cb29-297" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_unassigned =</span> ribo_secondary<span class="sc">-</span>assigned,</span>
+<span id="cb29-298"><a href="#cb29-298" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_bacterial =</span> bacteria,</span>
+<span id="cb29-299"><a href="#cb29-299" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_archaeal =</span> archaea,</span>
+<span id="cb29-300"><a href="#cb29-300" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_viral =</span> viruses,</span>
+<span id="cb29-301"><a href="#cb29-301" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_human =</span> eukaryota)</span>
+<span id="cb29-302"><a href="#cb29-302" aria-hidden="true" tabindex="-1"></a>read_comp_long <span class="ot">&lt;-</span> <span class="fu">pivot_longer</span>(read_comp, <span class="sc">-</span>sample, <span class="at">names_to =</span> <span class="st">"classification"</span>,</span>
+<span id="cb29-303"><a href="#cb29-303" aria-hidden="true" tabindex="-1"></a>                               <span class="at">names_prefix =</span> <span class="st">"n_"</span>, <span class="at">values_to =</span> <span class="st">"n_reads"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-304"><a href="#cb29-304" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">fct_inorder</span>(<span class="fu">str_to_sentence</span>(classification))) <span class="sc">%&gt;%</span></span>
+<span id="cb29-305"><a href="#cb29-305" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_reads<span class="sc">/</span><span class="fu">sum</span>(n_reads))</span>
+<span id="cb29-306"><a href="#cb29-306" aria-hidden="true" tabindex="-1"></a>read_comp_summ <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span> </span>
+<span id="cb29-307"><a href="#cb29-307" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(classification) <span class="sc">%&gt;%</span></span>
+<span id="cb29-308"><a href="#cb29-308" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads =</span> <span class="fu">sum</span>(n_reads), <span class="at">.groups =</span> <span class="st">"drop_last"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-309"><a href="#cb29-309" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads =</span> <span class="fu">replace_na</span>(n_reads,<span class="dv">0</span>),</span>
+<span id="cb29-310"><a href="#cb29-310" aria-hidden="true" tabindex="-1"></a>    <span class="at">p_reads =</span> n_reads<span class="sc">/</span><span class="fu">sum</span>(n_reads),</span>
+<span id="cb29-311"><a href="#cb29-311" aria-hidden="true" tabindex="-1"></a>    <span class="at">pc_reads =</span> p_reads<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb29-312"><a href="#cb29-312" aria-hidden="true" tabindex="-1"></a>  </span>
+<span id="cb29-313"><a href="#cb29-313" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot overall composition</span></span>
+<span id="cb29-314"><a href="#cb29-314" aria-hidden="true" tabindex="-1"></a>g_comp <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(read_comp_long, <span class="fu">aes</span>(<span class="at">x=</span>sample, <span class="at">y=</span>p_reads, <span class="at">fill=</span>classification)) <span class="sc">+</span></span>
+<span id="cb29-315"><a href="#cb29-315" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb29-316"><a href="#cb29-316" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"% Reads"</span>, <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb29-317"><a href="#cb29-317" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(x) x<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb29-318"><a href="#cb29-318" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Classification"</span>) <span class="sc">+</span></span>
+<span id="cb29-319"><a href="#cb29-319" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb29-320"><a href="#cb29-320" aria-hidden="true" tabindex="-1"></a>g_comp</span>
+<span id="cb29-321"><a href="#cb29-321" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-322"><a href="#cb29-322" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot composition of minor components</span></span>
+<span id="cb29-323"><a href="#cb29-323" aria-hidden="true" tabindex="-1"></a>read_comp_minor <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span> <span class="fu">filter</span>(classification <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"Archaeal"</span>, <span class="st">"Viral"</span>, <span class="st">"Human"</span>, <span class="st">"Other"</span>))</span>
+<span id="cb29-324"><a href="#cb29-324" aria-hidden="true" tabindex="-1"></a>palette_minor <span class="ot">&lt;-</span> <span class="fu">brewer.pal</span>(<span class="dv">9</span>, <span class="st">"Set1"</span>)[<span class="dv">6</span><span class="sc">:</span><span class="dv">9</span>]</span>
+<span id="cb29-325"><a href="#cb29-325" aria-hidden="true" tabindex="-1"></a>g_comp_minor <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(read_comp_minor, <span class="fu">aes</span>(<span class="at">x=</span>sample, <span class="at">y=</span>p_reads, <span class="at">fill=</span>classification)) <span class="sc">+</span></span>
+<span id="cb29-326"><a href="#cb29-326" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb29-327"><a href="#cb29-327" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"% Reads"</span>, <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="fl">0.1</span>,<span class="fl">0.01</span>),</span>
+<span id="cb29-328"><a href="#cb29-328" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(x) x<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb29-329"><a href="#cb29-329" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_minor, <span class="at">name =</span> <span class="st">"Classification"</span>) <span class="sc">+</span></span>
+<span id="cb29-330"><a href="#cb29-330" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb29-331"><a href="#cb29-331" aria-hidden="true" tabindex="-1"></a>g_comp_minor</span>
+<span id="cb29-332"><a href="#cb29-332" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-333"><a href="#cb29-333" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-336"><a href="#cb29-336" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-337"><a href="#cb29-337" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: composition-summary</span></span>
+<span id="cb29-338"><a href="#cb29-338" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-339"><a href="#cb29-339" aria-hidden="true" tabindex="-1"></a>p_reads_summ_group <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span></span>
+<span id="cb29-340"><a href="#cb29-340" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">ifelse</span>(classification <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"Filtered"</span>, <span class="st">"Duplicate"</span>, <span class="st">"Unassigned"</span>), <span class="st">"Excluded"</span>, <span class="fu">as.character</span>(classification)),</span>
+<span id="cb29-341"><a href="#cb29-341" aria-hidden="true" tabindex="-1"></a>         <span class="at">classification =</span> <span class="fu">fct_inorder</span>(classification)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-342"><a href="#cb29-342" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(classification, sample) <span class="sc">%&gt;%</span></span>
+<span id="cb29-343"><a href="#cb29-343" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads =</span> <span class="fu">sum</span>(p_reads), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-344"><a href="#cb29-344" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(classification) <span class="sc">%&gt;%</span></span>
+<span id="cb29-345"><a href="#cb29-345" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">pc_min =</span> <span class="fu">min</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, <span class="at">pc_max =</span> <span class="fu">max</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, </span>
+<span id="cb29-346"><a href="#cb29-346" aria-hidden="true" tabindex="-1"></a>            <span class="at">pc_mean =</span> <span class="fu">mean</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb29-347"><a href="#cb29-347" aria-hidden="true" tabindex="-1"></a>p_reads_summ_prep <span class="ot">&lt;-</span> p_reads_summ_group <span class="sc">%&gt;%</span></span>
+<span id="cb29-348"><a href="#cb29-348" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">fct_inorder</span>(classification),</span>
+<span id="cb29-349"><a href="#cb29-349" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_min =</span> pc_min <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb29-350"><a href="#cb29-350" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_max =</span> pc_max <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb29-351"><a href="#cb29-351" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_mean =</span> pc_mean <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb29-352"><a href="#cb29-352" aria-hidden="true" tabindex="-1"></a>         <span class="at">display =</span> <span class="fu">paste0</span>(pc_min, <span class="st">"-"</span>, pc_max, <span class="st">"% (mean "</span>, pc_mean, <span class="st">"%)"</span>))</span>
+<span id="cb29-353"><a href="#cb29-353" aria-hidden="true" tabindex="-1"></a>p_reads_summ <span class="ot">&lt;-</span> p_reads_summ_prep <span class="sc">%&gt;%</span></span>
+<span id="cb29-354"><a href="#cb29-354" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">Classification=</span>classification, <span class="st">`</span><span class="at">% Reads</span><span class="st">`</span> <span class="ot">=</span> display)</span>
+<span id="cb29-355"><a href="#cb29-355" aria-hidden="true" tabindex="-1"></a>p_reads_summ</span>
+<span id="cb29-356"><a href="#cb29-356" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-357"><a href="#cb29-357" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-358"><a href="#cb29-358" aria-hidden="true" tabindex="-1"></a>Overall, between 44% and 87% of reads (mean 57%) were low-quality, duplicates, or unassigned. 0.1-0.5% (mean 0.4%) were identified as ribosomal, and 11-55% (mean 42%) were otherwise classified as bacterial. The fraction of human reads ranged from 0.1-0.7% (mean 0.3%); that of viral reads ranged from 0.05%-1% (mean 0.15%).</span>
+<span id="cb29-359"><a href="#cb29-359" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-360"><a href="#cb29-360" aria-hidden="true" tabindex="-1"></a>This viral fraction is much lower than that seen for the RNA-seq studies I analyzed recently, I assume primarily due to the absence of highly abundant plant viruses. Looking more closely at the viral composition, we see that it is dominated by *Suoliviridae* and *Intestiviridae*, two families of [CrAss-like phage](https://en.wikipedia.org/wiki/CrAssphage), as well as *Straboviridae*, another group of DNA phages:</span>
+<span id="cb29-361"><a href="#cb29-361" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-364"><a href="#cb29-364" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-365"><a href="#cb29-365" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: viral-families-all</span></span>
+<span id="cb29-366"><a href="#cb29-366" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb29-367"><a href="#cb29-367" aria-hidden="true" tabindex="-1"></a>viral_taxa_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"viral-taxids.tsv.gz"</span>)</span>
+<span id="cb29-368"><a href="#cb29-368" aria-hidden="true" tabindex="-1"></a>viral_taxa <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(viral_taxa_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb29-369"><a href="#cb29-369" aria-hidden="true" tabindex="-1"></a>samples <span class="ot">&lt;-</span> <span class="fu">as.character</span>(basic_stats_raw<span class="sc">$</span>sample)</span>
+<span id="cb29-370"><a href="#cb29-370" aria-hidden="true" tabindex="-1"></a>col_names <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"pc_reads_total"</span>, <span class="st">"n_reads_clade"</span>, <span class="st">"n_reads_direct"</span>, <span class="st">"rank"</span>, <span class="st">"taxid"</span>, <span class="st">"name"</span>)</span>
+<span id="cb29-371"><a href="#cb29-371" aria-hidden="true" tabindex="-1"></a>report_paths <span class="ot">&lt;-</span> <span class="fu">paste0</span>(data_dir, <span class="st">"kraken/"</span>, samples, <span class="st">".report.gz"</span>)</span>
+<span id="cb29-372"><a href="#cb29-372" aria-hidden="true" tabindex="-1"></a>kraken_reports <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(samples), </span>
+<span id="cb29-373"><a href="#cb29-373" aria-hidden="true" tabindex="-1"></a>                         <span class="cf">function</span>(n) <span class="fu">read_tsv</span>(report_paths[n], <span class="at">col_names =</span> col_names,</span>
+<span id="cb29-374"><a href="#cb29-374" aria-hidden="true" tabindex="-1"></a>                                              <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-375"><a href="#cb29-375" aria-hidden="true" tabindex="-1"></a>                           <span class="fu">mutate</span>(<span class="at">sample =</span> samples[n])) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb29-376"><a href="#cb29-376" aria-hidden="true" tabindex="-1"></a>kraken_reports_viral <span class="ot">&lt;-</span> <span class="fu">filter</span>(kraken_reports, taxid <span class="sc">%in%</span> viral_taxa<span class="sc">$</span>taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb29-377"><a href="#cb29-377" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span></span>
+<span id="cb29-378"><a href="#cb29-378" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_viral =</span> n_reads_clade<span class="sc">/</span>n_reads_clade[<span class="dv">1</span>])</span>
+<span id="cb29-379"><a href="#cb29-379" aria-hidden="true" tabindex="-1"></a>viral_families <span class="ot">&lt;-</span> kraken_reports_viral <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"F"</span>)</span>
+<span id="cb29-380"><a href="#cb29-380" aria-hidden="true" tabindex="-1"></a>viral_families_major_list <span class="ot">&lt;-</span> viral_families <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb29-381"><a href="#cb29-381" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_viral_max =</span> <span class="fu">max</span>(p_reads_viral), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-382"><a href="#cb29-382" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_viral_max <span class="sc">&gt;=</span> <span class="fl">0.01</span>) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb29-383"><a href="#cb29-383" aria-hidden="true" tabindex="-1"></a>viral_families_major <span class="ot">&lt;-</span> viral_families <span class="sc">%&gt;%</span> <span class="fu">filter</span>(name <span class="sc">%in%</span> viral_families_major_list) <span class="sc">%&gt;%</span></span>
+<span id="cb29-384"><a href="#cb29-384" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, p_reads_viral)</span>
+<span id="cb29-385"><a href="#cb29-385" aria-hidden="true" tabindex="-1"></a>viral_families_minor <span class="ot">&lt;-</span> viral_families_major <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span></span>
+<span id="cb29-386"><a href="#cb29-386" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_viral_major =</span> <span class="fu">sum</span>(p_reads_viral)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-387"><a href="#cb29-387" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="st">"Other"</span>, <span class="at">taxid=</span><span class="cn">NA</span>, <span class="at">p_reads_viral =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_viral_major) <span class="sc">%&gt;%</span></span>
+<span id="cb29-388"><a href="#cb29-388" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, p_reads_viral)</span>
+<span id="cb29-389"><a href="#cb29-389" aria-hidden="true" tabindex="-1"></a>viral_families_display <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(viral_families_major, viral_families_minor) <span class="sc">%&gt;%</span></span>
+<span id="cb29-390"><a href="#cb29-390" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_viral)) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels=</span><span class="fu">c</span>(viral_families_major_list, <span class="st">"Other"</span>)))</span>
+<span id="cb29-391"><a href="#cb29-391" aria-hidden="true" tabindex="-1"></a>g_families <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(viral_families_display, <span class="fu">aes</span>(<span class="at">x=</span>sample, <span class="at">y=</span>p_reads_viral, <span class="at">fill=</span>name)) <span class="sc">+</span></span>
+<span id="cb29-392"><a href="#cb29-392" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb29-393"><a href="#cb29-393" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set3"</span>, <span class="at">name =</span> <span class="st">"Viral family"</span>) <span class="sc">+</span></span>
+<span id="cb29-394"><a href="#cb29-394" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Viral Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb29-395"><a href="#cb29-395" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb29-396"><a href="#cb29-396" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb29-397"><a href="#cb29-397" aria-hidden="true" tabindex="-1"></a>g_families</span>
+<span id="cb29-398"><a href="#cb29-398" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-399"><a href="#cb29-399" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-400"><a href="#cb29-400" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting virus reads: validation</span></span>
+<span id="cb29-401"><a href="#cb29-401" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-402"><a href="#cb29-402" aria-hidden="true" tabindex="-1"></a>Next, I investigated the human-infecting virus read content of these unenriched samples, using a pipeline identical to that described in my <span class="co">[</span><span class="ot">entry on unenriched Rothman samples</span><span class="co">](https://data.securebio.org/wills-public-notebook/notebooks/2024-02-27_rothman-1.html)</span>. This process identified a total of 318 read pairs across all samples (0.0002% of surviving reads):</span>
+<span id="cb29-403"><a href="#cb29-403" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-406"><a href="#cb29-406" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-407"><a href="#cb29-407" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-read-counts</span></span>
+<span id="cb29-408"><a href="#cb29-408" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"hv_hits_putative_filtered.tsv.gz"</span>)</span>
+<span id="cb29-409"><a href="#cb29-409" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(hv_reads_filtered_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-410"><a href="#cb29-410" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-411"><a href="#cb29-411" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(sample) <span class="sc">%&gt;%</span></span>
+<span id="cb29-412"><a href="#cb29-412" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> <span class="fu">fct_inorder</span>(sample))</span>
+<span id="cb29-413"><a href="#cb29-413" aria-hidden="true" tabindex="-1"></a>n_hv_filtered <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">inner_join</span>(basic_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"ribo_initial"</span>) <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, n_read_pairs), <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">n_putative =</span> n, <span class="at">n_total =</span> n_read_pairs) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads <span class="sc">*</span> <span class="dv">100</span>)</span>
+<span id="cb29-414"><a href="#cb29-414" aria-hidden="true" tabindex="-1"></a>n_hv_filtered_total <span class="ot">&lt;-</span> n_hv_filtered <span class="sc">%&gt;%</span> ungroup <span class="sc">%&gt;%</span></span>
+<span id="cb29-415"><a href="#cb29-415" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_putative =</span> <span class="fu">sum</span>(n_putative), <span class="at">n_total =</span> <span class="fu">sum</span>(n_total)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-416"><a href="#cb29-416" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb29-417"><a href="#cb29-417" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-418"><a href="#cb29-418" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-421"><a href="#cb29-421" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-422"><a href="#cb29-422" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-scores</span></span>
+<span id="cb29-423"><a href="#cb29-423" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb29-424"><a href="#cb29-424" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 8</span></span>
+<span id="cb29-425"><a href="#cb29-425" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-426"><a href="#cb29-426" aria-hidden="true" tabindex="-1"></a>mrg <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span></span>
+<span id="cb29-427"><a href="#cb29-427" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">kraken_label =</span> <span class="fu">ifelse</span>(assigned_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">assignment"</span>,</span>
+<span id="cb29-428"><a href="#cb29-428" aria-hidden="true" tabindex="-1"></a>                               <span class="fu">ifelse</span>(hit_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">hit"</span>,</span>
+<span id="cb29-429"><a href="#cb29-429" aria-hidden="true" tabindex="-1"></a>                                      <span class="st">"No hit or</span><span class="sc">\n</span><span class="st">assignment"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb29-430"><a href="#cb29-430" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(adj_score_fwd), <span class="fu">desc</span>(adj_score_rev)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-431"><a href="#cb29-431" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_num =</span> <span class="fu">row_number</span>(),</span>
+<span id="cb29-432"><a href="#cb29-432" aria-hidden="true" tabindex="-1"></a>         <span class="at">adj_score_max =</span> <span class="fu">pmax</span>(adj_score_fwd, adj_score_rev))</span>
+<span id="cb29-433"><a href="#cb29-433" aria-hidden="true" tabindex="-1"></a><span class="co"># Import Bowtie2/Kraken data and perform filtering steps</span></span>
+<span id="cb29-434"><a href="#cb29-434" aria-hidden="true" tabindex="-1"></a>g_mrg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev)) <span class="sc">+</span></span>
+<span id="cb29-435"><a href="#cb29-435" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span> </span>
+<span id="cb29-436"><a href="#cb29-436" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">100</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-437"><a href="#cb29-437" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">100</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-438"><a href="#cb29-438" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb29-439"><a href="#cb29-439" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>)</span>
+<span id="cb29-440"><a href="#cb29-440" aria-hidden="true" tabindex="-1"></a>g_mrg</span>
+<span id="cb29-441"><a href="#cb29-441" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-442"><a href="#cb29-442" aria-hidden="true" tabindex="-1"></a>g_hist_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> </span>
+<span id="cb29-443"><a href="#cb29-443" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>,<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> </span>
+<span id="cb29-444"><a href="#cb29-444" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>kraken_label) <span class="sc">+</span> </span>
+<span id="cb29-445"><a href="#cb29-445" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>,</span>
+<span id="cb29-446"><a href="#cb29-446" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">200</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span> </span>
+<span id="cb29-447"><a href="#cb29-447" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span> </span>
+<span id="cb29-448"><a href="#cb29-448" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Dark2"</span>) <span class="sc">+</span> </span>
+<span id="cb29-449"><a href="#cb29-449" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>)</span>
+<span id="cb29-450"><a href="#cb29-450" aria-hidden="true" tabindex="-1"></a>g_hist_0</span>
+<span id="cb29-451"><a href="#cb29-451" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-452"><a href="#cb29-452" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-453"><a href="#cb29-453" aria-hidden="true" tabindex="-1"></a>I ran these reads through BLASTN in a similar manner to past datasets. The results were surprisingly poor with a significant fraction of high-scoring false-positives:</span>
+<span id="cb29-454"><a href="#cb29-454" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-457"><a href="#cb29-457" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-458"><a href="#cb29-458" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: make-blast-fasta</span></span>
+<span id="cb29-459"><a href="#cb29-459" aria-hidden="true" tabindex="-1"></a>mrg_fasta <span class="ot">&lt;-</span>  mrg <span class="sc">%&gt;%</span></span>
+<span id="cb29-460"><a href="#cb29-460" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_head =</span> <span class="fu">paste0</span>(<span class="st">"&gt;"</span>, seq_id)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-461"><a href="#cb29-461" aria-hidden="true" tabindex="-1"></a>  ungroup <span class="sc">%&gt;%</span></span>
+<span id="cb29-462"><a href="#cb29-462" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">header1=</span>seq_head, <span class="at">seq1=</span>query_seq_fwd, </span>
+<span id="cb29-463"><a href="#cb29-463" aria-hidden="true" tabindex="-1"></a>         <span class="at">header2=</span>seq_head, <span class="at">seq2=</span>query_seq_rev) <span class="sc">%&gt;%</span></span>
+<span id="cb29-464"><a href="#cb29-464" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">header1=</span><span class="fu">paste0</span>(header1, <span class="st">"_1"</span>), <span class="at">header2=</span><span class="fu">paste0</span>(header2, <span class="st">"_2"</span>))</span>
+<span id="cb29-465"><a href="#cb29-465" aria-hidden="true" tabindex="-1"></a>mrg_fasta_out <span class="ot">&lt;-</span> <span class="fu">do.call</span>(paste, <span class="fu">c</span>(mrg_fasta, <span class="at">sep=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)) <span class="sc">%&gt;%</span> <span class="fu">paste</span>(<span class="at">collapse=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
+<span id="cb29-466"><a href="#cb29-466" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(mrg_fasta_out, <span class="fu">file.path</span>(data_dir, <span class="st">"blast/maritz-putative-viral.fasta"</span>))</span>
+<span id="cb29-467"><a href="#cb29-467" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-468"><a href="#cb29-468" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-471"><a href="#cb29-471" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-472"><a href="#cb29-472" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: process-blast-data</span></span>
+<span id="cb29-473"><a href="#cb29-473" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb29-474"><a href="#cb29-474" aria-hidden="true" tabindex="-1"></a><span class="co"># Import BLAST results</span></span>
+<span id="cb29-475"><a href="#cb29-475" aria-hidden="true" tabindex="-1"></a>blast_results_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"blast/maritz-putative-viral.blast.gz"</span>)</span>
+<span id="cb29-476"><a href="#cb29-476" aria-hidden="true" tabindex="-1"></a>blast_cols <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"qseqid"</span>, <span class="st">"sseqid"</span>, <span class="st">"sgi"</span>, <span class="st">"staxid"</span>, <span class="st">"qlen"</span>, <span class="st">"evalue"</span>, <span class="st">"bitscore"</span>, <span class="st">"qcovs"</span>, <span class="st">"length"</span>, <span class="st">"pident"</span>, <span class="st">"mismatch"</span>, <span class="st">"gapopen"</span>, <span class="st">"sstrand"</span>, <span class="st">"qstart"</span>, <span class="st">"qend"</span>, <span class="st">"sstart"</span>, <span class="st">"send"</span>)</span>
+<span id="cb29-477"><a href="#cb29-477" aria-hidden="true" tabindex="-1"></a>blast_results <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(blast_results_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>, </span>
+<span id="cb29-478"><a href="#cb29-478" aria-hidden="true" tabindex="-1"></a>                          <span class="at">col_names =</span> blast_cols, <span class="at">col_types =</span> <span class="fu">cols</span>(<span class="at">.default=</span><span class="st">"c"</span>))</span>
+<span id="cb29-479"><a href="#cb29-479" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-480"><a href="#cb29-480" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter for best hit for each query/subject</span></span>
+<span id="cb29-481"><a href="#cb29-481" aria-hidden="true" tabindex="-1"></a>blast_results_best <span class="ot">&lt;-</span> blast_results <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(qseqid, staxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-482"><a href="#cb29-482" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(bitscore <span class="sc">==</span> <span class="fu">max</span>(bitscore)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-483"><a href="#cb29-483" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(length <span class="sc">==</span> <span class="fu">max</span>(length)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>)</span>
+<span id="cb29-484"><a href="#cb29-484" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-485"><a href="#cb29-485" aria-hidden="true" tabindex="-1"></a><span class="co"># Rank hits for each query</span></span>
+<span id="cb29-486"><a href="#cb29-486" aria-hidden="true" tabindex="-1"></a>blast_results_ranked <span class="ot">&lt;-</span> blast_results_best <span class="sc">%&gt;%</span></span>
+<span id="cb29-487"><a href="#cb29-487" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(qseqid) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">rank =</span> <span class="fu">dense_rank</span>(<span class="fu">desc</span>(bitscore)))</span>
+<span id="cb29-488"><a href="#cb29-488" aria-hidden="true" tabindex="-1"></a>blast_results_highrank <span class="ot">&lt;-</span> blast_results_ranked <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">&lt;=</span> <span class="dv">5</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-489"><a href="#cb29-489" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">read_pair =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="sc">-</span><span class="dv">1</span>),</span>
+<span id="cb29-490"><a href="#cb29-490" aria-hidden="true" tabindex="-1"></a>         <span class="at">seq_id =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="dv">1</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-491"><a href="#cb29-491" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">bitscore =</span> <span class="fu">as.numeric</span>(bitscore)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-492"><a href="#cb29-492" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(seq_id, read_pair, <span class="fu">everything</span>())</span>
+<span id="cb29-493"><a href="#cb29-493" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-494"><a href="#cb29-494" aria-hidden="true" tabindex="-1"></a><span class="co"># Summarize by read pair and taxid</span></span>
+<span id="cb29-495"><a href="#cb29-495" aria-hidden="true" tabindex="-1"></a>blast_results_paired <span class="ot">&lt;-</span> blast_results_highrank <span class="sc">%&gt;%</span></span>
+<span id="cb29-496"><a href="#cb29-496" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id, staxid) <span class="sc">%&gt;%</span></span>
+<span id="cb29-497"><a href="#cb29-497" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">bitscore_max =</span> <span class="fu">max</span>(bitscore), <span class="at">bitscore_min =</span> <span class="fu">min</span>(bitscore),</span>
+<span id="cb29-498"><a href="#cb29-498" aria-hidden="true" tabindex="-1"></a>            <span class="at">best_rank =</span> <span class="fu">min</span>(rank),</span>
+<span id="cb29-499"><a href="#cb29-499" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads =</span> <span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb29-500"><a href="#cb29-500" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-501"><a href="#cb29-501" aria-hidden="true" tabindex="-1"></a><span class="co"># Add viral status</span></span>
+<span id="cb29-502"><a href="#cb29-502" aria-hidden="true" tabindex="-1"></a>blast_results_viral <span class="ot">&lt;-</span> blast_results_paired <span class="sc">%&gt;%</span></span>
+<span id="cb29-503"><a href="#cb29-503" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral =</span> staxid <span class="sc">%in%</span> viral_taxa<span class="sc">$</span>taxid,</span>
+<span id="cb29-504"><a href="#cb29-504" aria-hidden="true" tabindex="-1"></a>         <span class="at">viral_full =</span> viral <span class="sc">&amp;</span> n_reads <span class="sc">==</span> <span class="dv">2</span>)</span>
+<span id="cb29-505"><a href="#cb29-505" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-506"><a href="#cb29-506" aria-hidden="true" tabindex="-1"></a><span class="co"># Compare to Kraken &amp; Bowtie assignments</span></span>
+<span id="cb29-507"><a href="#cb29-507" aria-hidden="true" tabindex="-1"></a>mrg_assign <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> ungroup <span class="sc">%&gt;%</span> <span class="fu">select</span>(seq_id, taxid, assigned_taxid)</span>
+<span id="cb29-508"><a href="#cb29-508" aria-hidden="true" tabindex="-1"></a>blast_results_assign <span class="ot">&lt;-</span> <span class="fu">full_join</span>(blast_results_viral, mrg_assign, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-509"><a href="#cb29-509" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">taxid_match_bowtie =</span> (staxid <span class="sc">==</span> taxid),</span>
+<span id="cb29-510"><a href="#cb29-510" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_kraken =</span> (staxid <span class="sc">==</span> assigned_taxid),</span>
+<span id="cb29-511"><a href="#cb29-511" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_any =</span> taxid_match_bowtie <span class="sc">|</span> taxid_match_kraken)</span>
+<span id="cb29-512"><a href="#cb29-512" aria-hidden="true" tabindex="-1"></a>blast_results_out <span class="ot">&lt;-</span> blast_results_assign <span class="sc">%&gt;%</span> ungroup <span class="sc">%&gt;%</span></span>
+<span id="cb29-513"><a href="#cb29-513" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id) <span class="sc">%&gt;%</span></span>
+<span id="cb29-514"><a href="#cb29-514" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">viral_status =</span> <span class="fu">ifelse</span>(<span class="fu">any</span>(viral_full), <span class="dv">2</span>,</span>
+<span id="cb29-515"><a href="#cb29-515" aria-hidden="true" tabindex="-1"></a>                                  <span class="fu">ifelse</span>(<span class="fu">any</span>(taxid_match_any), <span class="dv">2</span>,</span>
+<span id="cb29-516"><a href="#cb29-516" aria-hidden="true" tabindex="-1"></a>                                             <span class="fu">ifelse</span>(<span class="fu">any</span>(viral), <span class="dv">1</span>, <span class="dv">0</span>))),</span>
+<span id="cb29-517"><a href="#cb29-517" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-518"><a href="#cb29-518" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_status =</span> <span class="fu">replace_na</span>(viral_status, <span class="dv">0</span>))</span>
+<span id="cb29-519"><a href="#cb29-519" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-520"><a href="#cb29-520" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-523"><a href="#cb29-523" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-524"><a href="#cb29-524" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 8</span></span>
+<span id="cb29-525"><a href="#cb29-525" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb29-526"><a href="#cb29-526" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-527"><a href="#cb29-527" aria-hidden="true" tabindex="-1"></a><span class="co"># Merge BLAST results with unenriched read data</span></span>
+<span id="cb29-528"><a href="#cb29-528" aria-hidden="true" tabindex="-1"></a>mrg_blast <span class="ot">&lt;-</span> <span class="fu">full_join</span>(mrg, blast_results_out, <span class="at">by=</span><span class="fu">c</span>(<span class="st">"seq_id"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-529"><a href="#cb29-529" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_status =</span> <span class="fu">replace_na</span>(viral_status, <span class="dv">0</span>),</span>
+<span id="cb29-530"><a href="#cb29-530" aria-hidden="true" tabindex="-1"></a>         <span class="at">viral_status_out =</span> <span class="fu">ifelse</span>(viral_status <span class="sc">==</span> <span class="dv">0</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>))</span>
+<span id="cb29-531"><a href="#cb29-531" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-532"><a href="#cb29-532" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb29-533"><a href="#cb29-533" aria-hidden="true" tabindex="-1"></a>g_mrg_blast <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span></span>
+<span id="cb29-534"><a href="#cb29-534" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev, <span class="at">color=</span>viral_status_out)) <span class="sc">+</span></span>
+<span id="cb29-535"><a href="#cb29-535" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span></span>
+<span id="cb29-536"><a href="#cb29-536" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">100</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-537"><a href="#cb29-537" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">100</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-538"><a href="#cb29-538" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Viral status"</span>) <span class="sc">+</span></span>
+<span id="cb29-539"><a href="#cb29-539" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(viral_status_out<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb29-540"><a href="#cb29-540" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>)</span>
+<span id="cb29-541"><a href="#cb29-541" aria-hidden="true" tabindex="-1"></a>g_mrg_blast</span>
+<span id="cb29-542"><a href="#cb29-542" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-543"><a href="#cb29-543" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-546"><a href="#cb29-546" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-547"><a href="#cb29-547" aria-hidden="true" tabindex="-1"></a>g_hist_1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg_blast, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>,<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">facet_wrap</span>(<span class="sc">~</span>viral_status_out) <span class="sc">+</span> <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Dark2"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>)</span>
+<span id="cb29-548"><a href="#cb29-548" aria-hidden="true" tabindex="-1"></a>g_hist_1</span>
+<span id="cb29-549"><a href="#cb29-549" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-550"><a href="#cb29-550" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-553"><a href="#cb29-553" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-554"><a href="#cb29-554" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-f1</span></span>
+<span id="cb29-555"><a href="#cb29-555" aria-hidden="true" tabindex="-1"></a>test_sens_spec <span class="ot">&lt;-</span> <span class="cf">function</span>(tab, score_threshold, conjunctive, include_special){</span>
+<span id="cb29-556"><a href="#cb29-556" aria-hidden="true" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span>include_special) tab <span class="ot">&lt;-</span> <span class="fu">filter</span>(tab, viral_status_out <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"TRUE"</span>, <span class="st">"FALSE"</span>))</span>
+<span id="cb29-557"><a href="#cb29-557" aria-hidden="true" tabindex="-1"></a>  tab_retained <span class="ot">&lt;-</span> tab <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(</span>
+<span id="cb29-558"><a href="#cb29-558" aria-hidden="true" tabindex="-1"></a>    <span class="at">conjunctive =</span> conjunctive,</span>
+<span id="cb29-559"><a href="#cb29-559" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score_conjunctive =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">&amp;</span> adj_score_rev <span class="sc">&gt;</span> score_threshold), </span>
+<span id="cb29-560"><a href="#cb29-560" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score_disjunctive =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">|</span> adj_score_rev <span class="sc">&gt;</span> score_threshold),</span>
+<span id="cb29-561"><a href="#cb29-561" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score =</span> <span class="fu">ifelse</span>(conjunctive, retain_score_conjunctive, retain_score_disjunctive),</span>
+<span id="cb29-562"><a href="#cb29-562" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> retain_score) <span class="sc">%&gt;%</span></span>
+<span id="cb29-563"><a href="#cb29-563" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(viral_status_out, retain) <span class="sc">%&gt;%</span> count</span>
+<span id="cb29-564"><a href="#cb29-564" aria-hidden="true" tabindex="-1"></a>  pos_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb29-565"><a href="#cb29-565" aria-hidden="true" tabindex="-1"></a>  pos_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb29-566"><a href="#cb29-566" aria-hidden="true" tabindex="-1"></a>  neg_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb29-567"><a href="#cb29-567" aria-hidden="true" tabindex="-1"></a>  neg_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb29-568"><a href="#cb29-568" aria-hidden="true" tabindex="-1"></a>  sensitivity <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> neg_fls)</span>
+<span id="cb29-569"><a href="#cb29-569" aria-hidden="true" tabindex="-1"></a>  specificity <span class="ot">&lt;-</span> neg_tru <span class="sc">/</span> (neg_tru <span class="sc">+</span> pos_fls)</span>
+<span id="cb29-570"><a href="#cb29-570" aria-hidden="true" tabindex="-1"></a>  precision   <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> pos_fls)</span>
+<span id="cb29-571"><a href="#cb29-571" aria-hidden="true" tabindex="-1"></a>  f1 <span class="ot">&lt;-</span> <span class="dv">2</span> <span class="sc">*</span> precision <span class="sc">*</span> sensitivity <span class="sc">/</span> (precision <span class="sc">+</span> sensitivity)</span>
+<span id="cb29-572"><a href="#cb29-572" aria-hidden="true" tabindex="-1"></a>  out <span class="ot">&lt;-</span> <span class="fu">tibble</span>(<span class="at">threshold=</span>score_threshold, <span class="at">include_special =</span> include_special, </span>
+<span id="cb29-573"><a href="#cb29-573" aria-hidden="true" tabindex="-1"></a>                <span class="at">conjunctive =</span> conjunctive, <span class="at">sensitivity=</span>sensitivity, </span>
+<span id="cb29-574"><a href="#cb29-574" aria-hidden="true" tabindex="-1"></a>                <span class="at">specificity=</span>specificity, <span class="at">precision=</span>precision, <span class="at">f1=</span>f1)</span>
+<span id="cb29-575"><a href="#cb29-575" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(out)</span>
+<span id="cb29-576"><a href="#cb29-576" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb29-577"><a href="#cb29-577" aria-hidden="true" tabindex="-1"></a>range_f1 <span class="ot">&lt;-</span> <span class="cf">function</span>(intab, inc_special, <span class="at">inrange=</span><span class="dv">15</span><span class="sc">:</span><span class="dv">45</span>){</span>
+<span id="cb29-578"><a href="#cb29-578" aria-hidden="true" tabindex="-1"></a>  tss <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(test_sens_spec, <span class="at">tab=</span>intab, <span class="at">include_special=</span>inc_special)</span>
+<span id="cb29-579"><a href="#cb29-579" aria-hidden="true" tabindex="-1"></a>  stats_conj <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss, <span class="at">conjunctive=</span><span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb29-580"><a href="#cb29-580" aria-hidden="true" tabindex="-1"></a>  stats_disj <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss, <span class="at">conjunctive=</span><span class="cn">FALSE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb29-581"><a href="#cb29-581" aria-hidden="true" tabindex="-1"></a>  stats_all <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(stats_conj, stats_disj) <span class="sc">%&gt;%</span></span>
+<span id="cb29-582"><a href="#cb29-582" aria-hidden="true" tabindex="-1"></a>    <span class="fu">pivot_longer</span>(<span class="sc">!</span>(threshold<span class="sc">:</span>conjunctive), <span class="at">names_to=</span><span class="st">"metric"</span>, <span class="at">values_to=</span><span class="st">"value"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-583"><a href="#cb29-583" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">conj_label =</span> <span class="fu">ifelse</span>(conjunctive, <span class="st">"Conjunctive"</span>, <span class="st">"Disjunctive"</span>))</span>
+<span id="cb29-584"><a href="#cb29-584" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(stats_all)</span>
+<span id="cb29-585"><a href="#cb29-585" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb29-586"><a href="#cb29-586" aria-hidden="true" tabindex="-1"></a>inc_special <span class="ot">&lt;-</span> <span class="cn">FALSE</span></span>
+<span id="cb29-587"><a href="#cb29-587" aria-hidden="true" tabindex="-1"></a>stats_0 <span class="ot">&lt;-</span> <span class="fu">range_f1</span>(mrg_blast, inc_special)</span>
+<span id="cb29-588"><a href="#cb29-588" aria-hidden="true" tabindex="-1"></a>threshold_opt_0 <span class="ot">&lt;-</span> stats_0 <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(conj_label) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-589"><a href="#cb29-589" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(metric <span class="sc">==</span> <span class="st">"f1"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-590"><a href="#cb29-590" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="fu">max</span>(value)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(threshold <span class="sc">==</span> <span class="fu">min</span>(threshold))</span>
+<span id="cb29-591"><a href="#cb29-591" aria-hidden="true" tabindex="-1"></a>g_stats_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(stats_0, <span class="fu">aes</span>(<span class="at">x=</span>threshold, <span class="at">y=</span>value, <span class="at">color=</span>metric)) <span class="sc">+</span></span>
+<span id="cb29-592"><a href="#cb29-592" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">data =</span> threshold_opt_0, <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">xintercept=</span>threshold),</span>
+<span id="cb29-593"><a href="#cb29-593" aria-hidden="true" tabindex="-1"></a>             <span class="at">color =</span> <span class="st">"red"</span>, <span class="at">linetype =</span> <span class="st">"dashed"</span>) <span class="sc">+</span></span>
+<span id="cb29-594"><a href="#cb29-594" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
+<span id="cb29-595"><a href="#cb29-595" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"Value"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-596"><a href="#cb29-596" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Threshold"</span>, <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-597"><a href="#cb29-597" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette=</span><span class="st">"Set3"</span>) <span class="sc">+</span></span>
+<span id="cb29-598"><a href="#cb29-598" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(<span class="sc">~</span>conj_label) <span class="sc">+</span></span>
+<span id="cb29-599"><a href="#cb29-599" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb29-600"><a href="#cb29-600" aria-hidden="true" tabindex="-1"></a>g_stats_0</span>
+<span id="cb29-601"><a href="#cb29-601" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-602"><a href="#cb29-602" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-603"><a href="#cb29-603" aria-hidden="true" tabindex="-1"></a>TODO: Describe results</span>
+<span id="cb29-604"><a href="#cb29-604" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-607"><a href="#cb29-607" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-608"><a href="#cb29-608" aria-hidden="true" tabindex="-1"></a>major_threshold <span class="ot">&lt;-</span> <span class="fl">0.05</span></span>
+<span id="cb29-609"><a href="#cb29-609" aria-hidden="true" tabindex="-1"></a>fp <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> </span>
+<span id="cb29-610"><a href="#cb29-610" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, <span class="at">highscore =</span> adj_score_max <span class="sc">&gt;=</span> <span class="dv">20</span>, taxid) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> </span>
+<span id="cb29-611"><a href="#cb29-611" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p=</span>n<span class="sc">/</span><span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-612"><a href="#cb29-612" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"taxid"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-613"><a href="#cb29-613" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p))</span>
+<span id="cb29-614"><a href="#cb29-614" aria-hidden="true" tabindex="-1"></a>fp_major_tab <span class="ot">&lt;-</span> fp <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p <span class="sc">&gt;</span> major_threshold) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(p))</span>
+<span id="cb29-615"><a href="#cb29-615" aria-hidden="true" tabindex="-1"></a>fp_major_list <span class="ot">&lt;-</span> fp_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name) <span class="sc">%&gt;%</span> sort <span class="sc">%&gt;%</span> unique <span class="sc">%&gt;%</span> <span class="fu">c</span>(., <span class="st">"Other"</span>)</span>
+<span id="cb29-616"><a href="#cb29-616" aria-hidden="true" tabindex="-1"></a>fp_major <span class="ot">&lt;-</span> fp <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">major =</span> p <span class="sc">&gt;</span> major_threshold) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-617"><a href="#cb29-617" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(major, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-618"><a href="#cb29-618" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore, name_display) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-619"><a href="#cb29-619" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n=</span><span class="fu">sum</span>(n), <span class="at">p=</span><span class="fu">sum</span>(p), <span class="at">.groups =</span> <span class="st">"drop"</span>)  <span class="sc">%&gt;%</span></span>
+<span id="cb29-620"><a href="#cb29-620" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, <span class="at">levels =</span> fp_major_list),</span>
+<span id="cb29-621"><a href="#cb29-621" aria-hidden="true" tabindex="-1"></a>         <span class="at">score_display =</span> <span class="fu">ifelse</span>(highscore, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span>),</span>
+<span id="cb29-622"><a href="#cb29-622" aria-hidden="true" tabindex="-1"></a>         <span class="at">status_display =</span> <span class="fu">ifelse</span>(viral_status_out, <span class="st">"True positive"</span>, <span class="st">"False positive"</span>))</span>
+<span id="cb29-623"><a href="#cb29-623" aria-hidden="true" tabindex="-1"></a>fp_palette <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="fu">brewer.pal</span>(<span class="dv">8</span>, <span class="st">"Set2"</span>))</span>
+<span id="cb29-624"><a href="#cb29-624" aria-hidden="true" tabindex="-1"></a>g_fp <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(fp_major, <span class="fu">aes</span>(<span class="at">x=</span>score_display, <span class="at">y=</span>p, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
+<span id="cb29-625"><a href="#cb29-625" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb29-626"><a href="#cb29-626" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">name =</span> <span class="st">"True positive?"</span>) <span class="sc">+</span></span>
+<span id="cb29-627"><a href="#cb29-627" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"% reads"</span>, <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb29-628"><a href="#cb29-628" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb29-629"><a href="#cb29-629" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>fp_palette, <span class="at">name =</span> <span class="st">"Viral</span><span class="sc">\n</span><span class="st">taxon"</span>) <span class="sc">+</span></span>
+<span id="cb29-630"><a href="#cb29-630" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>status_display) <span class="sc">+</span></span>
+<span id="cb29-631"><a href="#cb29-631" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">6</span>)) <span class="sc">+</span></span>
+<span id="cb29-632"><a href="#cb29-632" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb29-633"><a href="#cb29-633" aria-hidden="true" tabindex="-1"></a>g_fp</span>
+<span id="cb29-634"><a href="#cb29-634" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb29-635"><a href="#cb29-635" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-638"><a href="#cb29-638" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb29-639"><a href="#cb29-639" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-fp-blast</span></span>
+<span id="cb29-640"><a href="#cb29-640" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-641"><a href="#cb29-641" aria-hidden="true" tabindex="-1"></a><span class="co"># Import names db</span></span>
+<span id="cb29-642"><a href="#cb29-642" aria-hidden="true" tabindex="-1"></a>tax_names_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"taxid-names.tsv.gz"</span>)</span>
+<span id="cb29-643"><a href="#cb29-643" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(tax_names_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb29-644"><a href="#cb29-644" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-645"><a href="#cb29-645" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing names</span></span>
+<span id="cb29-646"><a href="#cb29-646" aria-hidden="true" tabindex="-1"></a>tax_names_new <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
+<span id="cb29-647"><a href="#cb29-647" aria-hidden="true" tabindex="-1"></a>  <span class="at">staxid =</span> <span class="fu">c</span>(<span class="dv">263765</span>, <span class="dv">3082113</span>, <span class="dv">595494</span>, <span class="dv">240869</span>, <span class="dv">312017</span>, <span class="dv">3029053</span>),</span>
+<span id="cb29-648"><a href="#cb29-648" aria-hidden="true" tabindex="-1"></a>  <span class="at">name =</span> <span class="fu">c</span>(<span class="st">"uncultured Tolumonas sp."</span>,</span>
+<span id="cb29-649"><a href="#cb29-649" aria-hidden="true" tabindex="-1"></a>           <span class="st">"Rangifer tarandus platyrhynchus"</span>,</span>
+<span id="cb29-650"><a href="#cb29-650" aria-hidden="true" tabindex="-1"></a>           <span class="st">"Tolumonas auensis DSM 9187"</span>,</span>
+<span id="cb29-651"><a href="#cb29-651" aria-hidden="true" tabindex="-1"></a>           <span class="st">"Bombylius major"</span>,</span>
+<span id="cb29-652"><a href="#cb29-652" aria-hidden="true" tabindex="-1"></a>           <span class="st">"Tetrahymena thermophila SB210"</span>,</span>
+<span id="cb29-653"><a href="#cb29-653" aria-hidden="true" tabindex="-1"></a>           <span class="st">"Mucilaginibacter sp. SJ"</span>)</span>
+<span id="cb29-654"><a href="#cb29-654" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb29-655"><a href="#cb29-655" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(tax_names, tax_names_new)</span>
+<span id="cb29-656"><a href="#cb29-656" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-657"><a href="#cb29-657" aria-hidden="true" tabindex="-1"></a>fp_taxids_major <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span>viral_status_out) <span class="sc">%&gt;%</span></span>
+<span id="cb29-658"><a href="#cb29-658" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(taxid) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(n)) <span class="sc">%&gt;%</span> <span class="fu">head</span>(<span class="dv">10</span>)</span>
+<span id="cb29-659"><a href="#cb29-659" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-660"><a href="#cb29-660" aria-hidden="true" tabindex="-1"></a>fp_staxid <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span>viral_status_out) <span class="sc">%&gt;%</span></span>
+<span id="cb29-661"><a href="#cb29-661" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(taxid, <span class="at">highscore =</span> adj_score_max <span class="sc">&gt;=</span> <span class="dv">20</span>) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">n_seq =</span> <span class="fu">n</span>()) <span class="sc">%&gt;%</span></span>
+<span id="cb29-662"><a href="#cb29-662" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(blast_results_paired, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb29-663"><a href="#cb29-663" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">staxid =</span> <span class="fu">as.integer</span>(staxid)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-664"><a href="#cb29-664" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names, <span class="at">by=</span><span class="st">"staxid"</span>) <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">sname=</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb29-665"><a href="#cb29-665" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">taxid=</span>staxid), <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb29-666"><a href="#cb29-666" aria-hidden="true" tabindex="-1"></a>fp_staxid_count <span class="ot">&lt;-</span> fp_staxid <span class="sc">%&gt;%</span></span>
+<span id="cb29-667"><a href="#cb29-667" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(highscore, taxid, name, staxid, sname, n_seq) <span class="sc">%&gt;%</span></span>
+<span id="cb29-668"><a href="#cb29-668" aria-hidden="true" tabindex="-1"></a>  count <span class="sc">%&gt;%</span></span>
+<span id="cb29-669"><a href="#cb29-669" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(highscore, taxid, name) <span class="sc">%&gt;%</span></span>
+<span id="cb29-670"><a href="#cb29-670" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p=</span>n<span class="sc">/</span>n_seq)</span>
+<span id="cb29-671"><a href="#cb29-671" aria-hidden="true" tabindex="-1"></a>fp_staxid_count_display <span class="ot">&lt;-</span> fp_staxid_count <span class="sc">%&gt;%</span></span>
+<span id="cb29-672"><a href="#cb29-672" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(highscore, taxid) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(n)) <span class="sc">%&gt;%</span></span>
+<span id="cb29-673"><a href="#cb29-673" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">&lt;=</span> <span class="dv">10</span>) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">%in%</span> fp_taxids_major<span class="sc">$</span>taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb29-674"><a href="#cb29-674" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="fu">is.na</span>(staxid) <span class="sc">|</span> <span class="sc">!</span><span class="fu">is.na</span>(sname))</span>
+<span id="cb29-675"><a href="#cb29-675" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-676"><a href="#cb29-676" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(fp_staxid_count_display, <span class="fu">aes</span>(<span class="at">x=</span>p, <span class="at">y=</span>sname)) <span class="sc">+</span> </span>
+<span id="cb29-677"><a href="#cb29-677" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>() <span class="sc">+</span> </span>
+<span id="cb29-678"><a href="#cb29-678" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(name<span class="sc">~</span>highscore, <span class="at">scales=</span><span class="st">"free"</span>,</span>
+<span id="cb29-679"><a href="#cb29-679" aria-hidden="true" tabindex="-1"></a>             <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">name =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb29-680"><a href="#cb29-680" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb29-681"><a href="#cb29-681" aria-hidden="true" tabindex="-1"></a>g</span>
+<span id="cb29-682"><a href="#cb29-682" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-683"><a href="#cb29-683" aria-hidden="true" tabindex="-1"></a><span class="co"># rota_ids &lt;- hv_reads_species %&gt;% filter(taxid==28875) %&gt;% pull(seq_id)</span></span>
+<span id="cb29-684"><a href="#cb29-684" aria-hidden="true" tabindex="-1"></a><span class="co"># rota_hits &lt;- blast_results_paired %&gt;%</span></span>
+<span id="cb29-685"><a href="#cb29-685" aria-hidden="true" tabindex="-1"></a><span class="co">#   filter(seq_id %in% rota_ids) %&gt;%</span></span>
+<span id="cb29-686"><a href="#cb29-686" aria-hidden="true" tabindex="-1"></a><span class="co">#   mutate(staxid = as.integer(staxid))</span></span>
+<span id="cb29-687"><a href="#cb29-687" aria-hidden="true" tabindex="-1"></a><span class="co"># rota_hits_sorted &lt;- rota_hits %&gt;% group_by(staxid) %&gt;% count %&gt;% arrange(desc(n)) %&gt;%</span></span>
+<span id="cb29-688"><a href="#cb29-688" aria-hidden="true" tabindex="-1"></a><span class="co">#   left_join(tax_names, by="staxid") %&gt;%</span></span>
+<span id="cb29-689"><a href="#cb29-689" aria-hidden="true" tabindex="-1"></a><span class="co">#   mutate(name = fct_inorder(name))</span></span>
+<span id="cb29-690"><a href="#cb29-690" aria-hidden="true" tabindex="-1"></a><span class="co"># rota_hits_sorted_head &lt;- rota_hits_sorted %&gt;% head(10) %&gt;%</span></span>
+<span id="cb29-691"><a href="#cb29-691" aria-hidden="true" tabindex="-1"></a><span class="co">#   mutate(name=fct_inorder(as.character(name)))</span></span>
+<span id="cb29-692"><a href="#cb29-692" aria-hidden="true" tabindex="-1"></a><span class="co"># g_rota &lt;- ggplot(rota_hits_sorted_head,</span></span>
+<span id="cb29-693"><a href="#cb29-693" aria-hidden="true" tabindex="-1"></a><span class="co">#                   aes(x=n, y=fct_inorder(name))) + geom_col() +</span></span>
+<span id="cb29-694"><a href="#cb29-694" aria-hidden="true" tabindex="-1"></a><span class="co">#   scale_x_continuous(name = "# Mapped read pairs") + theme_base +</span></span>
+<span id="cb29-695"><a href="#cb29-695" aria-hidden="true" tabindex="-1"></a><span class="co">#   theme(axis.title.y = element_blank())</span></span>
+<span id="cb29-696"><a href="#cb29-696" aria-hidden="true" tabindex="-1"></a><span class="co"># g_rota</span></span>
+<span id="cb29-697"><a href="#cb29-697" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div></div></div></div></div>
+</div> <!-- /content -->
+
+
+
+</body></html>
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+      <div class="quarto-title-block"><div><h1 class="title">Workflow analysis of Prussin et al.&nbsp;(2019)</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
+            <p class="subtitle lead">Wastewater from a manhole in Maryland.</p>
+                      </div>
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+    
+  
+  <div class="quarto-title-meta">
+
+      <div>
+      <div class="quarto-title-meta-heading">Author</div>
+      <div class="quarto-title-meta-contents">
+               <p>Will Bradshaw </p>
+            </div>
+    </div>
+      
+      <div>
+      <div class="quarto-title-meta-heading">Published</div>
+      <div class="quarto-title-meta-contents">
+        <p class="date">April 12, 2024</p>
+      </div>
+    </div>
+    
+      
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+    
+  
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+<!-- sidebar -->
+<!-- margin-sidebar -->
+    
+<!-- main -->
+<main class="content quarto-banner-title-block" id="quarto-document-content"><p>Taking a break from working on <a href="https://doi.org/10.1101/2023.12.22.23300450">P2RA datasets</a>, we’re also working on a review of air sampling for viral pathogen detection. For that study, we’re collecting and analyzing air MGS data that could give us a high-level idea of the likely viral composition of such samples.</p>
+<p>The first dataset I’m looking at for this work is <a href="https://doi.org/10.1186/s40168-019-0672-z">Prussin et al.&nbsp;(2019)</a>, a study of HVAC filter samples in a Virginia daycare center between 2014 and 2015. Samples were eluted from MERV-14 air filters collected every two weeks, with pairs of successive samples combined into four-week sampling periods. Like Brumfield et al, this study conducted both RNA and DNA sequencing; all samples were sequenced on an Illumina NextSeq500 with 2x150bp reads.</p>
+<section id="the-raw-data" class="level1"><h1>The raw data</h1>
+<p>The Prussin dataset comprised sequencing data from 14 timepoints spread across the year, from 20th January 2014 to 2nd February 2015. Each sample represents a four-week sampling period. In addition to the 14 on-site samples, there were also two control samples, a negative control (NC) and an “unexposed filter” control (UFC), which were collected on December 23rd 2014.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Data input paths</span></span>
+<span><span class="va">data_dir</span> <span class="op">&lt;-</span> <span class="st">"../data/2024-04-11_prussin/"</span></span>
+<span><span class="va">libraries_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"sample-metadata.csv"</span><span class="op">)</span></span>
+<span><span class="va">basic_stats_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"qc_basic_stats.tsv"</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"qc_adapter_stats.tsv"</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"qc_quality_base_stats.tsv"</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"qc_quality_sequence_stats.tsv"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Import libraries and extract metadata from sample names</span></span>
+<span><span class="va">libraries_raw</span> <span class="op">&lt;-</span> <span class="fu">read_csv</span><span class="op">(</span><span class="va">libraries_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span><span class="va">libraries</span> <span class="op">&lt;-</span> <span class="va">libraries_raw</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">na_type</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>na_type <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">na_type</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="va">date</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>start_date <span class="op">=</span> <span class="va">date</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>end_date <span class="op">=</span> <span class="va">start_date</span> <span class="op">+</span> <span class="fl">28</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>date <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">end_date</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>         ctrl <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/grep.html">grepl</a></span><span class="op">(</span><span class="st">"Negative_Control"</span>, <span class="va">sample_alias</span><span class="op">)</span>, <span class="st">"NC"</span>,</span>
+<span>                       <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/grep.html">grepl</a></span><span class="op">(</span><span class="st">"Unexposed_Filter"</span>, <span class="va">sample_alias</span><span class="op">)</span>,</span>
+<span>                              <span class="st">"UFC"</span>, <span class="st">"Non-Control"</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>         ctrl <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">ctrl</span>, levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Non-Control"</span>,</span>
+<span>                                        <span class="st">"NC"</span>,</span>
+<span>                                        <span class="st">"UFC"</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>         open <span class="op">=</span> <span class="op">(</span><span class="va">season</span> <span class="op">!=</span> <span class="st">"Closed"</span><span class="op">)</span> <span class="op">&amp;</span> <span class="op">(</span><span class="va">ctrl</span> <span class="op">==</span> <span class="st">"On-Site"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Import QC data</span></span>
+<span><span class="va">stages</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"raw_concat"</span>, <span class="st">"cleaned"</span>, <span class="st">"dedup"</span>, <span class="st">"ribo_initial"</span>, <span class="st">"ribo_secondary"</span><span class="op">)</span></span>
+<span><span class="va">basic_stats</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">basic_stats_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>         sample <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">adapter_stats_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>sample <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html">sub</a></span><span class="op">(</span><span class="st">"_2$"</span>, <span class="st">""</span>, <span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>         read_pair <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">quality_base_stats_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>         read_pair <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">quality_seq_stats_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>stage <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">stage</span>, levels <span class="op">=</span> <span class="va">stages</span><span class="op">)</span>,</span>
+<span>         read_pair <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Filter to raw data</span></span>
+<span><span class="va">basic_stats_raw</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">adapter_stats_raw</span> <span class="op">&lt;-</span> <span class="va">adapter_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">quality_base_stats_raw</span> <span class="op">&lt;-</span> <span class="va">quality_base_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span><span class="va">quality_seq_stats_raw</span> <span class="op">&lt;-</span> <span class="va">quality_seq_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"raw_concat"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get key values for readout</span></span>
+<span><span class="va">raw_read_counts</span> <span class="op">&lt;-</span> <span class="va">basic_stats_raw</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>rmin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>, rmax<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>,</span>
+<span>            rmean<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span><span class="va">raw_read_totals</span> <span class="op">&lt;-</span> <span class="va">basic_stats_raw</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_read_pairs <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>, </span>
+<span>            n_bases_approx <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_bases_approx</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span><span class="va">raw_dup</span> <span class="op">&lt;-</span> <span class="va">basic_stats_raw</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>dmin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>, dmax<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>,</span>
+<span>            dmean<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>The 14 positive samples from the dataset yielded 5M-20M (mean 11.3M) RNA-sequencing reads and 10M-42M (mean 21.0M) DNA-sequencing reads per sample, for a total of 159M RNA read pairs and 294M DNA read pairs (46.3 and 87.0 gigabases of sequence, respectively). Controls contributed an additional 1M RNA read pairs and 17.5M DNA read pairs.</p>
+<p>In positive samples, read qualities were mostly high but tailed off slightly at the 3’ end in some samples, suggesting the need for trimming. Adapter levels were high. Inferred duplication levels were low (2-4%) in DNA reads and moderate (21-44%) in RNA reads. Control sample reads were more problematic, with higher duplication and adapter levels and lower quality.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Prepare data</span></span>
+<span><span class="va">basic_stats_raw_metrics</span> <span class="op">&lt;-</span> <span class="va">basic_stats_raw</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">date</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>,</span>
+<span>         `# Read pairs` <span class="op">=</span> <span class="va">n_read_pairs</span>,</span>
+<span>         `Total base pairs\n(approx)` <span class="op">=</span> <span class="va">n_bases_approx</span>,</span>
+<span>         `% Duplicates\n(FASTQC)` <span class="op">=</span> <span class="va">percent_duplicates</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_longer</span><span class="op">(</span><span class="op">-</span><span class="op">(</span><span class="va">date</span><span class="op">:</span><span class="va">ctrl</span><span class="op">)</span>, names_to <span class="op">=</span> <span class="st">"metric"</span>, values_to <span class="op">=</span> <span class="st">"value"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>metric <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">metric</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Set up plot templates</span></span>
+<span><span class="va">scale_fill_na</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_fill_brewer</span>, palette<span class="op">=</span><span class="st">"Set1"</span>, </span>
+<span>                                name<span class="op">=</span><span class="st">"Nucleic acid type"</span><span class="op">)</span></span>
+<span><span class="va">g_basic</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">basic_stats_raw_metrics</span>, </span>
+<span>                  <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">date</span>, y<span class="op">=</span><span class="va">value</span>, fill<span class="op">=</span><span class="va">na_type</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position <span class="op">=</span> <span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_discrete</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Collection Date"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">expand_limits</span><span class="op">(</span>y<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">metric</span><span class="op">~</span><span class="va">ctrl</span>, scales <span class="op">=</span> <span class="st">"free"</span>, space<span class="op">=</span><span class="st">"free_x"</span>, switch<span class="op">=</span><span class="st">"y"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_rotate</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span></span>
+<span>    axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span>,</span>
+<span>    strip.text.y <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span></span>
+<span>  <span class="op">)</span></span>
+<span><span class="va">g_basic</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-basic-stats-1.png" class="img-fluid figure-img" width="864"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb3"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Set up plotting templates</span></span>
+<span><span class="va">scale_color_na</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_color_brewer</span>, palette<span class="op">=</span><span class="st">"Set1"</span>,</span>
+<span>                                 name<span class="op">=</span><span class="st">"Nucleic acid type"</span><span class="op">)</span></span>
+<span><span class="va">g_qual_raw</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span>mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>color<span class="op">=</span><span class="va">na_type</span>, linetype<span class="op">=</span><span class="va">read_pair</span>, </span>
+<span>                         group<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/interaction.html">interaction</a></span><span class="op">(</span><span class="va">sample</span>,<span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_linetype_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Read Pair"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>color<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>,</span>
+<span>         linetype <span class="op">=</span> <span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span></span>
+<span><span class="co"># Visualize adapters</span></span>
+<span><span class="va">g_adapters_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">pc_adapters</span><span class="op">)</span>, data<span class="op">=</span><span class="va">adapter_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Adapters"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">ctrl</span><span class="op">~</span><span class="va">adapter</span><span class="op">)</span></span>
+<span><span class="va">g_adapters_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-raw-quality-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb4"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize quality</span></span>
+<span><span class="va">g_quality_base_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">mean_phred_score</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_base_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10</span>,<span class="fl">45</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">ctrl</span><span class="op">~</span><span class="va">.</span><span class="op">)</span></span>
+<span><span class="va">g_quality_base_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-raw-quality-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb5"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_quality_seq_raw</span> <span class="op">&lt;-</span> <span class="va">g_qual_raw</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">mean_phred_score</span>, y<span class="op">=</span><span class="va">n_sequences</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_seq_stats_raw</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Sequences"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">ctrl</span><span class="op">~</span><span class="va">.</span>, scales <span class="op">=</span> <span class="st">"free_y"</span><span class="op">)</span></span>
+<span><span class="va">g_quality_seq_raw</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-raw-quality-3.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="preprocessing" class="level1"><h1>Preprocessing</h1>
+<p>The average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. For positive samples, on average, cleaning and deduplication removed about 19% of total read pairs from RNA libraries and about 7% from DNA libraries. Subsequent ribodepletion removed a further ~32% of total read pairs on average from RNA libraries but &lt;0.5% of total read pairs from DNA libraries.</p>
+<p>Control samples, meanwhile, lost an average of 63% of RNA read pairs and 21% of DNA read pairs during cleaning and deduplication, consistent with the lower qualities observed above. Subsequent ribodepletion removed an additional 9% of total RNA read pairs and 0.3% of total DNA read pairs.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb6"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">n_reads_rel</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">stage</span>, </span>
+<span>         <span class="va">percent_duplicates</span>, <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">na_type</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">stage</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_retained <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">n_read_pairs</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/stats/lag.html">lag</a></span><span class="op">(</span><span class="va">n_read_pairs</span><span class="op">)</span>, <span class="fl">0</span><span class="op">)</span>,</span>
+<span>         p_reads_lost <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">p_reads_retained</span>,</span>
+<span>         p_reads_retained_abs <span class="op">=</span> <span class="va">n_read_pairs</span> <span class="op">/</span> <span class="va">n_read_pairs</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>,</span>
+<span>         p_reads_lost_abs <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_retained_abs</span>,</span>
+<span>         p_reads_lost_abs_marginal <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">p_reads_lost_abs</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lag.html">lag</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">)</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">n_reads_rel_display</span> <span class="op">&lt;-</span> <span class="va">n_reads_rel</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">rename</span><span class="op">(</span>Stage<span class="op">=</span><span class="va">stage</span>, `NA Type`<span class="op">=</span><span class="va">na_type</span>, `Control?`<span class="op">=</span><span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">`Control?`</span>, <span class="va">`NA Type`</span>, <span class="va">Stage</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>`% Total Reads Lost (Cumulative)` <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">"-"</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">" (mean "</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_lost_abs</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">")"</span><span class="op">)</span>,</span>
+<span>            `% Total Reads Lost (Marginal)` <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">"-"</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">" (mean "</span>, <span class="fu"><a href="https://rdrr.io/r/base/Round.html">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span>,<span class="fl">1</span><span class="op">)</span>, <span class="st">")"</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">Stage</span> <span class="op">!=</span> <span class="st">"raw_concat"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>Stage <span class="op">=</span> <span class="va">Stage</span> <span class="op">%&gt;%</span> <span class="va">as.numeric</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span>labels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Trimming &amp; filtering"</span>, <span class="st">"Deduplication"</span>, <span class="st">"Initial ribodepletion"</span>, <span class="st">"Secondary ribodepletion"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">n_reads_rel_display</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div data-pagedtable="false">
+  <script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["Control?"],"name":[1],"type":["fct"],"align":["left"]},{"label":["NA Type"],"name":[2],"type":["fct"],"align":["left"]},{"label":["Stage"],"name":[3],"type":["fct"],"align":["left"]},{"label":["% Total Reads Lost (Cumulative)"],"name":[4],"type":["chr"],"align":["left"]},{"label":["% Total Reads Lost (Marginal)"],"name":[5],"type":["chr"],"align":["left"]}],"data":[{"1":"Non-Control","2":"RNA","3":"Trimming & filtering","4":"6.7-14.8 (mean 8.9)","5":"6.7-14.8 (mean 8.9)"},{"1":"Non-Control","2":"RNA","3":"Deduplication","4":"13-25.4 (mean 18.7)","5":"5.6-13.9 (mean 9.8)"},{"1":"Non-Control","2":"RNA","3":"Initial ribodepletion","4":"28.9-52.4 (mean 42.6)","5":"15.9-30.4 (mean 23.8)"},{"1":"Non-Control","2":"RNA","3":"Secondary ribodepletion","4":"35.3-63 (mean 50.8)","5":"5.9-10.6 (mean 8.2)"},{"1":"Non-Control","2":"DNA","3":"Trimming & filtering","4":"4.8-7.1 (mean 5.7)","5":"4.8-7.1 (mean 5.7)"},{"1":"Non-Control","2":"DNA","3":"Deduplication","4":"6.3-8.3 (mean 7)","5":"1-2.4 (mean 1.3)"},{"1":"Non-Control","2":"DNA","3":"Initial ribodepletion","4":"6.5-8.5 (mean 7.2)","5":"0.1-0.2 (mean 0.2)"},{"1":"Non-Control","2":"DNA","3":"Secondary ribodepletion","4":"6.6-8.6 (mean 7.3)","5":"0.1-0.1 (mean 0.1)"},{"1":"NC","2":"RNA","3":"Trimming & filtering","4":"35.4-35.4 (mean 35.4)","5":"35.4-35.4 (mean 35.4)"},{"1":"NC","2":"RNA","3":"Deduplication","4":"41.3-41.3 (mean 41.3)","5":"5.9-5.9 (mean 5.9)"},{"1":"NC","2":"RNA","3":"Initial ribodepletion","4":"51.2-51.2 (mean 51.2)","5":"9.9-9.9 (mean 9.9)"},{"1":"NC","2":"RNA","3":"Secondary ribodepletion","4":"54-54 (mean 54)","5":"2.7-2.7 (mean 2.7)"},{"1":"NC","2":"DNA","3":"Trimming & filtering","4":"6.5-6.5 (mean 6.5)","5":"6.5-6.5 (mean 6.5)"},{"1":"NC","2":"DNA","3":"Deduplication","4":"26.2-26.2 (mean 26.2)","5":"19.6-19.6 (mean 19.6)"},{"1":"NC","2":"DNA","3":"Initial ribodepletion","4":"26.3-26.3 (mean 26.3)","5":"0.1-0.1 (mean 0.1)"},{"1":"NC","2":"DNA","3":"Secondary ribodepletion","4":"26.4-26.4 (mean 26.4)","5":"0.1-0.1 (mean 0.1)"},{"1":"UFC","2":"RNA","3":"Trimming & filtering","4":"81.7-81.7 (mean 81.7)","5":"81.7-81.7 (mean 81.7)"},{"1":"UFC","2":"RNA","3":"Deduplication","4":"84.3-84.3 (mean 84.3)","5":"2.6-2.6 (mean 2.6)"},{"1":"UFC","2":"RNA","3":"Initial ribodepletion","4":"88.7-88.7 (mean 88.7)","5":"4.4-4.4 (mean 4.4)"},{"1":"UFC","2":"RNA","3":"Secondary ribodepletion","4":"89.9-89.9 (mean 89.9)","5":"1.2-1.2 (mean 1.2)"},{"1":"UFC","2":"DNA","3":"Trimming & filtering","4":"6.9-6.9 (mean 6.9)","5":"6.9-6.9 (mean 6.9)"},{"1":"UFC","2":"DNA","3":"Deduplication","4":"16-16 (mean 16)","5":"9.1-9.1 (mean 9.1)"},{"1":"UFC","2":"DNA","3":"Initial ribodepletion","4":"16.2-16.2 (mean 16.2)","5":"0.2-0.2 (mean 0.2)"},{"1":"UFC","2":"DNA","3":"Secondary ribodepletion","4":"16.2-16.2 (mean 16.2)","5":"0.1-0.1 (mean 0.1)"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb7"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_stage_trace</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">basic_stats</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">stage</span>, color<span class="op">=</span><span class="va">na_type</span>, group<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="va">ctrl</span><span class="op">~</span><span class="va">na_type</span>, scales<span class="op">=</span><span class="st">"free"</span>, ncol<span class="op">=</span><span class="fl">2</span>,</span>
+<span>             labeller <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span>multi_line<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span></span>
+<span><span class="co"># Plot reads over preprocessing</span></span>
+<span><span class="va">g_reads_stages</span> <span class="op">&lt;-</span> <span class="va">g_stage_trace</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">n_read_pairs</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"# Read pairs"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_reads_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/preproc-figures-1.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb8"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Plot relative read losses during preprocessing</span></span>
+<span><span class="va">g_reads_rel</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">n_reads_rel</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">stage</span>, color<span class="op">=</span><span class="va">na_type</span>, group<span class="op">=</span><span class="va">sample</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">p_reads_lost_abs_marginal</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"% Total Reads Lost"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, </span>
+<span>                     labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_wrap</span><span class="op">(</span><span class="va">ctrl</span><span class="op">~</span><span class="va">na_type</span>, scales<span class="op">=</span><span class="st">"free"</span>, ncol<span class="op">=</span><span class="fl">2</span>,</span>
+<span>             labeller <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span>multi_line<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_reads_rel</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/preproc-figures-2.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>In both positive and control samples, data cleaning with FASTP was very successful at removing adapters, with very few adapter sequences found by FASTQC at any stage after the raw data. FASTP was also successful at improving read quality.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb9"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_qual</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span>mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>color<span class="op">=</span><span class="va">na_type</span>, linetype<span class="op">=</span><span class="va">read_pair</span>, </span>
+<span>                         group<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/interaction.html">interaction</a></span><span class="op">(</span><span class="va">sample</span>,<span class="va">read_pair</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_linetype_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Read Pair"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>color<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>,</span>
+<span>         linetype <span class="op">=</span> <span class="fu">guide_legend</span><span class="op">(</span>nrow<span class="op">=</span><span class="fl">2</span>,byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span></span>
+<span><span class="co"># Visualize adapters</span></span>
+<span><span class="va">g_adapters</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">pc_adapters</span><span class="op">)</span>, data<span class="op">=</span><span class="va">adapter_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Adapters"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">20</span><span class="op">)</span>,</span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">50</span>,<span class="fl">10</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">adapter</span><span class="op">)</span></span>
+<span><span class="va">g_adapters</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-quality-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb10"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize quality</span></span>
+<span><span class="va">g_quality_base</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_hline</span><span class="op">(</span>yintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">position</span>, y<span class="op">=</span><span class="va">mean_phred_score</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_base_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10</span>,<span class="fl">45</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Position"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">140</span>,<span class="fl">20</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">.</span><span class="op">)</span></span>
+<span><span class="va">g_quality_base</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-quality-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb11"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_quality_seq</span> <span class="op">&lt;-</span> <span class="va">g_qual</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">25</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">30</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">mean_phred_score</span>, y<span class="op">=</span><span class="va">n_sequences</span><span class="op">)</span>, data<span class="op">=</span><span class="va">quality_seq_stats</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Mean Phred score"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Sequences"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">stage</span><span class="op">~</span><span class="va">.</span><span class="op">)</span></span>
+<span><span class="va">g_quality_seq</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-quality-3.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>According to FASTQC, deduplication was moderately effective at reducing measured duplicate levels in on-site samples, with FASTQC-measured levels falling from an average of 34% to 23% for RNA reads and from 2.7% to 2.2% for DNA reads.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb12"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">stage_dup</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">stage</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>dmin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>, dmax<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>,</span>
+<span>            dmean<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">percent_duplicates</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">g_dup_stages</span> <span class="op">&lt;-</span> <span class="va">g_stage_trace</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">percent_duplicates</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"% Duplicates"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_dup_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/preproc-dedup-1.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb13"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_readlen_stages</span> <span class="op">&lt;-</span> <span class="va">g_stage_trace</span> <span class="op">+</span> <span class="fu">geom_line</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">mean_seq_len</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span><span class="st">"Mean read length (nt)"</span>, expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="cn">NA</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_readlen_stages</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/preproc-dedup-2.png" class="img-fluid figure-img" width="576"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="high-level-composition" class="level1"><h1>High-level composition</h1>
+<p>As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb14"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import Bracken data</span></span>
+<span><span class="va">bracken_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"bracken_counts.tsv"</span><span class="op">)</span></span>
+<span><span class="va">bracken</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">bracken_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span><span class="va">total_assigned</span> <span class="op">&lt;-</span> <span class="va">bracken</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">summarize</span><span class="op">(</span></span>
+<span>  name <span class="op">=</span> <span class="st">"Total"</span>,</span>
+<span>  kraken_assigned_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">kraken_assigned_reads</span><span class="op">)</span>,</span>
+<span>  added_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">added_reads</span><span class="op">)</span>,</span>
+<span>  new_est_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">new_est_reads</span><span class="op">)</span>,</span>
+<span>  fraction_total_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">fraction_total_reads</span><span class="op">)</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">bracken_spread</span> <span class="op">&lt;-</span> <span class="va">bracken</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">sample</span>, <span class="va">new_est_reads</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/chartr.html">tolower</a></span><span class="op">(</span><span class="va">name</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>id_cols <span class="op">=</span> <span class="st">"sample"</span>, names_from <span class="op">=</span> <span class="st">"name"</span>, </span>
+<span>              values_from <span class="op">=</span> <span class="st">"new_est_reads"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count reads</span></span>
+<span><span class="va">read_counts_preproc</span> <span class="op">&lt;-</span> <span class="va">basic_stats</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span>, <span class="va">stage</span>, <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>id_cols <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"sample"</span>, <span class="st">"na_type"</span>, <span class="st">"ctrl"</span>, <span class="st">"date"</span><span class="op">)</span>,</span>
+<span>              names_from<span class="op">=</span><span class="st">"stage"</span>, values_from<span class="op">=</span><span class="st">"n_read_pairs"</span><span class="op">)</span></span>
+<span><span class="va">read_counts</span> <span class="op">&lt;-</span> <span class="va">read_counts_preproc</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">total_assigned</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">new_est_reads</span><span class="op">)</span>, by <span class="op">=</span> <span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>assigned <span class="op">=</span> <span class="va">new_est_reads</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">bracken_spread</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Assess composition</span></span>
+<span><span class="va">read_comp</span> <span class="op">&lt;-</span> <span class="fu">transmute</span><span class="op">(</span><span class="va">read_counts</span>, sample<span class="op">=</span><span class="va">sample</span>, na_type<span class="op">=</span><span class="va">na_type</span>,</span>
+<span>                       ctrl <span class="op">=</span> <span class="va">ctrl</span>, date <span class="op">=</span> <span class="va">date</span>,</span>
+<span>                       n_filtered <span class="op">=</span> <span class="va">raw_concat</span><span class="op">-</span><span class="va">cleaned</span>,</span>
+<span>                       n_duplicate <span class="op">=</span> <span class="va">cleaned</span><span class="op">-</span><span class="va">dedup</span>,</span>
+<span>                       n_ribosomal <span class="op">=</span> <span class="op">(</span><span class="va">dedup</span><span class="op">-</span><span class="va">ribo_initial</span><span class="op">)</span> <span class="op">+</span> <span class="op">(</span><span class="va">ribo_initial</span><span class="op">-</span><span class="va">ribo_secondary</span><span class="op">)</span>,</span>
+<span>                       n_unassigned <span class="op">=</span> <span class="va">ribo_secondary</span><span class="op">-</span><span class="va">assigned</span>,</span>
+<span>                       n_bacterial <span class="op">=</span> <span class="va">bacteria</span>,</span>
+<span>                       n_archaeal <span class="op">=</span> <span class="va">archaea</span>,</span>
+<span>                       n_viral <span class="op">=</span> <span class="va">viruses</span>,</span>
+<span>                       n_human <span class="op">=</span> <span class="va">eukaryota</span><span class="op">)</span></span>
+<span><span class="va">read_comp_long</span> <span class="op">&lt;-</span> <span class="fu">pivot_longer</span><span class="op">(</span><span class="va">read_comp</span>, <span class="op">-</span><span class="op">(</span><span class="va">sample</span><span class="op">:</span><span class="va">date</span><span class="op">)</span>, </span>
+<span>                               names_to <span class="op">=</span> <span class="st">"classification"</span>,</span>
+<span>                               names_prefix <span class="op">=</span> <span class="st">"n_"</span>, values_to <span class="op">=</span> <span class="st">"n_reads"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="fu">str_to_sentence</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_reads</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Summarize composition</span></span>
+<span><span class="va">read_comp_summ</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">classification</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop_last"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>n_reads <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">n_reads</span>,<span class="fl">0</span><span class="op">)</span>,</span>
+<span>    p_reads <span class="op">=</span> <span class="va">n_reads</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads</span><span class="op">)</span>,</span>
+<span>    pc_reads <span class="op">=</span> <span class="va">p_reads</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb15"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Prepare plotting templates</span></span>
+<span><span class="va">g_comp_base</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span>mapping<span class="op">=</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">date</span>, y<span class="op">=</span><span class="va">p_reads</span>, fill<span class="op">=</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_discrete</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Collection Date"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">na_type</span><span class="op">~</span><span class="va">ctrl</span>, scales <span class="op">=</span> <span class="st">"free"</span>, space <span class="op">=</span> <span class="st">"free_x"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_rotate</span></span>
+<span><span class="va">scale_y_pc_reads</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">scale_y_continuous</span>, name <span class="op">=</span> <span class="st">"% Reads"</span>,</span>
+<span>                                   expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot overall composition</span></span>
+<span><span class="va">g_comp</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> <span class="fu">geom_col</span><span class="op">(</span>data <span class="op">=</span> <span class="va">read_comp_long</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_pc_reads</span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Set1"</span>, name <span class="op">=</span> <span class="st">"Classification"</span><span class="op">)</span></span>
+<span><span class="va">g_comp</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-composition-all-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb16"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Plot composition of minor components</span></span>
+<span><span class="va">read_comp_minor</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">classification</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Archaeal"</span>, <span class="st">"Viral"</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">palette_minor</span> <span class="op">&lt;-</span> <span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">9</span>, <span class="st">"Set1"</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">6</span>,<span class="fl">7</span>,<span class="fl">9</span><span class="op">)</span><span class="op">]</span></span>
+<span><span class="va">g_comp_minor</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">read_comp_minor</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_pc_reads</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_minor</span>, name <span class="op">=</span> <span class="st">"Classification"</span><span class="op">)</span></span>
+<span><span class="va">g_comp_minor</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-composition-all-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb17"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">p_reads_summ_group</span> <span class="op">&lt;-</span> <span class="va">read_comp_long</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">classification</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Filtered"</span>, <span class="st">"Duplicate"</span>, <span class="st">"Unassigned"</span><span class="op">)</span>, <span class="st">"Excluded"</span>, <span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>         classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">classification</span>, <span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">classification</span>, <span class="va">na_type</span>, <span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>pc_min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, pc_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, </span>
+<span>            pc_mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">p_reads</span><span class="op">)</span><span class="op">*</span><span class="fl">100</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ_prep</span> <span class="op">&lt;-</span> <span class="va">p_reads_summ_group</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>classification <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">classification</span><span class="op">)</span>,</span>
+<span>         pc_min <span class="op">=</span> <span class="va">pc_min</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         pc_max <span class="op">=</span> <span class="va">pc_max</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         pc_mean <span class="op">=</span> <span class="va">pc_mean</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html">signif</a></span><span class="op">(</span>digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">format</span>, scientific<span class="op">=</span><span class="cn">FALSE</span>, trim<span class="op">=</span><span class="cn">TRUE</span>, digits<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>         display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">pc_min</span>, <span class="st">"-"</span>, <span class="va">pc_max</span>, <span class="st">"% (mean "</span>, <span class="va">pc_mean</span>, <span class="st">"%)"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ</span> <span class="op">&lt;-</span> <span class="va">p_reads_summ_prep</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">ctrl</span>, <span class="va">classification</span>, <span class="va">na_type</span>, <span class="va">display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>names_from<span class="op">=</span><span class="va">na_type</span>, values_from <span class="op">=</span> <span class="va">display</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="va">ctrl</span>, <span class="va">classification</span><span class="op">)</span></span>
+<span><span class="va">p_reads_summ</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div data-pagedtable="false">
+  <script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["ctrl"],"name":[1],"type":["fct"],"align":["left"]},{"label":["classification"],"name":[2],"type":["fct"],"align":["left"]},{"label":["RNA"],"name":[3],"type":["chr"],"align":["left"]},{"label":["DNA"],"name":[4],"type":["chr"],"align":["left"]}],"data":[{"1":"Non-Control","2":"Excluded","3":"32-45% (mean 37%)","4":"46-54% (mean 50%)"},{"1":"Non-Control","2":"Ribosomal","3":"22-41% (mean 32%)","4":"0.24-0.34% (mean 0.29%)"},{"1":"Non-Control","2":"Bacterial","3":"2.3-7.6% (mean 3.6%)","4":"11-33% (mean 16%)"},{"1":"Non-Control","2":"Archaeal","3":"0.008-0.016% (mean 0.012%)","4":"0.013-0.021% (mean 0.016%)"},{"1":"Non-Control","2":"Viral","3":"0.02-0.053% (mean 0.033%)","4":"0.011-0.033% (mean 0.019%)"},{"1":"Non-Control","2":"Human","3":"15-42% (mean 27%)","4":"13-42% (mean 34%)"},{"1":"NC","2":"Excluded","3":"71-71% (mean 71%)","4":"35-35% (mean 35%)"},{"1":"NC","2":"Ribosomal","3":"13-13% (mean 13%)","4":"0.21-0.21% (mean 0.21%)"},{"1":"NC","2":"Bacterial","3":"3.5-3.5% (mean 3.5%)","4":"61-61% (mean 61%)"},{"1":"NC","2":"Archaeal","3":"0.0055-0.0055% (mean 0.0055%)","4":"0.0014-0.0014% (mean 0.0014%)"},{"1":"NC","2":"Viral","3":"0.54-0.54% (mean 0.54%)","4":"0.00079-0.00079% (mean 0.00079%)"},{"1":"NC","2":"Human","3":"13-13% (mean 13%)","4":"3.8-3.8% (mean 3.8%)"},{"1":"UFC","2":"Excluded","3":"92-92% (mean 92%)","4":"27-27% (mean 27%)"},{"1":"UFC","2":"Ribosomal","3":"5.6-5.6% (mean 5.6%)","4":"0.25-0.25% (mean 0.25%)"},{"1":"UFC","2":"Bacterial","3":"0.77-0.77% (mean 0.77%)","4":"70-70% (mean 70%)"},{"1":"UFC","2":"Archaeal","3":"0.00059-0.00059% (mean 0.00059%)","4":"0.0016-0.0016% (mean 0.0016%)"},{"1":"UFC","2":"Viral","3":"0.08-0.08% (mean 0.08%)","4":"0.0016-0.0016% (mean 0.0016%)"},{"1":"UFC","2":"Human","3":"1.7-1.7% (mean 1.7%)","4":"2.3-2.3% (mean 2.3%)"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</div>
+</div>
+<p>What we see is a very different picture from the wastewater samples I’ve been analyzing so far. Most notably, the fraction of (non-ribosomal) human reads is <em>much</em> higher. Even the 2015-01-20 samples, which were taken when the daycare center was closed for the winter holidays, showed human read fractions (for both nucleic-acid types) of &gt;12%; non-control samples as a whole averaged 27% for RNA reads and 34% for DNA reads. Compare <a href="https://data.securebio.org/wills-public-notebook/notebooks/2024-04-08_brumfield.html">Brumfield</a> (average 0.08% for RNA and 0.02% for DNA), <a href="https://data.securebio.org/wills-public-notebook/notebooks/2024-03-16_yang.html">Yang</a> (mean 0.05% for RNA) or even <a href="https://data.securebio.org/wills-public-notebook/notebooks/2024-02-27_rothman-1.html">Rothman</a> (mean 1.8% for non-panel-enriched RNA samples).</p>
+<p>Conversely, total viral reads are very low: mean 0.033% for RNA reads and 0.019% for DNA reads. Wastewater RNA datasets have typically had much higher total viruses: mean 0.5% for Brumfield, about the same for Crits-Christoph, 5.5% for Yang, 4.5% for Rothman. Brumfield’s DNA data contained substantially fewer viruses than their RNA data, but still more than Prussin: about 0.08% on average.</p>
+<p>Looking at viral families…was less informative than usual, especially for DNA reads. It turns out that these samples contain a lot of viral reads that Kraken2 was only able to classify to the class level. In DNA reads, samples were dominated by <em>Caudoviricetes</em> phages, though <em>Herviviricetes</em> (which includes herpesviruses) and <em>Naldaviricetes</em> (a class of arthropod-infecting viruses) also put in a respectable showing. In RNA reads, <em>Caudoviricetes</em> was again a major presence, but <em>Alsuviricetes</em> (a family of primarily plant pathogens) was often as or more prevalent, and <em>Revtraviricetes</em> (a class that includes Hepatitis B virus and retroviruses) was also significant.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb18"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get viral taxonomy</span></span>
+<span><span class="va">viral_taxa_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"viral-taxids.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">viral_taxa</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">viral_taxa_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Import Kraken reports &amp; extract viral taxa</span></span>
+<span><span class="va">samples</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span><span class="op">(</span><span class="va">basic_stats_raw</span><span class="op">$</span><span class="va">sample</span><span class="op">)</span></span>
+<span><span class="va">col_names</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"pc_reads_total"</span>, <span class="st">"n_reads_clade"</span>, <span class="st">"n_reads_direct"</span>,</span>
+<span>               <span class="st">"rank"</span>, <span class="st">"taxid"</span>, <span class="st">"name"</span><span class="op">)</span></span>
+<span><span class="va">report_paths</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"kraken/"</span>, <span class="va">samples</span>, <span class="st">".report.gz"</span><span class="op">)</span></span>
+<span><span class="va">kraken_reports</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">samples</span><span class="op">)</span>, <span class="kw">function</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> </span>
+<span>  <span class="fu">read_tsv</span><span class="op">(</span><span class="va">report_paths</span><span class="op">[</span><span class="va">n</span><span class="op">]</span>, col_names <span class="op">=</span> <span class="va">col_names</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>sample <span class="op">=</span> <span class="va">samples</span><span class="op">[</span><span class="va">n</span><span class="op">]</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span></span>
+<span><span class="va">kraken_reports_viral</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">kraken_reports</span>, <span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_viral <span class="op">=</span> <span class="va">n_reads_clade</span><span class="op">/</span><span class="va">n_reads_clade</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span><span class="va">kraken_reports_viral_cleaned</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">pc_reads_total</span>, <span class="op">-</span><span class="va">n_reads_direct</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">p_reads_viral</span>, <span class="va">n_reads_clade</span>, <span class="fu">everything</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">viral_classes</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"C"</span><span class="op">)</span></span>
+<span><span class="va">viral_families</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"F"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb19"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Identify major viral classes</span></span>
+<span><span class="va">viral_classes_major_tab</span> <span class="op">&lt;-</span> <span class="va">viral_classes</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_viral_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_viral_max</span> <span class="op">&gt;=</span> <span class="fl">0.04</span><span class="op">)</span></span>
+<span><span class="va">viral_classes_major_list</span> <span class="op">&lt;-</span> <span class="va">viral_classes_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span><span class="va">viral_classes_major</span> <span class="op">&lt;-</span> <span class="va">viral_classes</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_classes_major_list</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span>, <span class="va">p_reads_viral</span><span class="op">)</span></span>
+<span><span class="va">viral_classes_minor</span> <span class="op">&lt;-</span> <span class="va">viral_classes_major</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_viral_major <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Other"</span>, taxid<span class="op">=</span><span class="cn">NA</span>, p_reads_viral <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_viral_major</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span>, <span class="va">p_reads_viral</span><span class="op">)</span></span>
+<span><span class="va">viral_classes_display</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">viral_classes_major</span>, <span class="va">viral_classes_minor</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_viral</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name</span>, levels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">viral_classes_major_list</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_viral</span>, classification<span class="op">=</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">palette_viral</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">12</span>, <span class="st">"Set3"</span><span class="op">)</span>, <span class="fu">brewer.pal</span><span class="op">(</span><span class="fl">8</span>, <span class="st">"Set2"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_classes</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">viral_classes_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Viral Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral class"</span><span class="op">)</span></span>
+<span><span class="va">g_classes</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/viral-class-composition-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>Of these, the most interesting are the strong presence of <em>Herviviricetes</em> in the DNA reads and <em>Revtraviricetes</em> in the RNA reads, as both of these are families that contain important human pathogens.</p>
+<p>Digging into the former, it turns out these reads are composed almost exclusively of <em>Herpesviridae</em> at the family level. Within non-control samples, these arise overwhelmingly from <em>Cytomegalovirus.</em> Digging in at the species level, these in turn are primarily attributed to a single CMV, Human betaherpesvirus 5, a.k.a. human cytomegalovirus (HCMV). I was excited to see this: this is the first time a single human pathogen, or even all human pathogens combined, have constituted a significant fraction of all viral reads in a sample I’ve analyzed with this pipeline.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb20"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get all read counts in class</span></span>
+<span><span class="va">hervi_taxid</span> <span class="op">&lt;-</span> <span class="fl">2731363</span></span>
+<span><span class="va">hervi_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">hervi_taxid</span></span>
+<span><span class="va">hervi_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hervi_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="kw">while</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">hervi_desc_taxids_new</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">hervi_desc_taxids_old</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">hervi_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">hervi_desc_taxids_new</span></span>
+<span>  <span class="va">hervi_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hervi_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">hervi_counts</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_desc_taxids_new</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_hervi <span class="op">=</span> <span class="va">n_reads_clade</span><span class="op">/</span><span class="va">n_reads_clade</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get genus composition</span></span>
+<span><span class="va">hervi_genera</span> <span class="op">&lt;-</span> <span class="va">hervi_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"S"</span>, <span class="va">na_type</span> <span class="op">==</span> <span class="st">"DNA"</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_major_tab</span> <span class="op">&lt;-</span> <span class="va">hervi_genera</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_hervi_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_hervi</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hervi_max</span> <span class="op">&gt;=</span> <span class="fl">0.04</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_major_list</span> <span class="op">&lt;-</span> <span class="va">hervi_genera_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_major</span> <span class="op">&lt;-</span> <span class="va">hervi_genera</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hervi_genera_major_list</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span>, <span class="va">p_reads_hervi</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_minor</span> <span class="op">&lt;-</span> <span class="va">hervi_genera_major</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_hervi_major <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_hervi</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Other"</span>, taxid<span class="op">=</span><span class="cn">NA</span>, p_reads_hervi <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_hervi_major</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span>, <span class="va">p_reads_hervi</span><span class="op">)</span></span>
+<span><span class="va">hervi_genera_display</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">hervi_genera_major</span>, <span class="va">hervi_genera_minor</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_hervi</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name</span>, levels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hervi_genera_major_list</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_hervi</span>, classification<span class="op">=</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_hervi_genera</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">hervi_genera_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Herviviricetes Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral species"</span><span class="op">)</span></span>
+<span><span class="va">g_hervi_genera</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/herviviricetes-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p><em>Revtraviricetes</em> reads are similarly dominated by a single viral genus, <em>Alpharetrovirus.</em> Digging in at the species level, we see contributions from a variety of avian oncoviruses. To my (admittedly non-expert) knowledge, none of these infect humans, and I think they are probably primarily arising from local birds (or possibly rats).</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb21"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get all read counts in class</span></span>
+<span><span class="va">revtra_taxid</span> <span class="op">&lt;-</span> <span class="fl">2732514</span></span>
+<span><span class="va">revtra_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">revtra_taxid</span></span>
+<span><span class="va">revtra_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">revtra_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="kw">while</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">revtra_desc_taxids_new</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">revtra_desc_taxids_old</span><span class="op">)</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">revtra_desc_taxids_old</span> <span class="op">&lt;-</span> <span class="va">revtra_desc_taxids_new</span></span>
+<span>  <span class="va">revtra_desc_taxids_new</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">revtra_desc_taxids_old</span>, <span class="va">viral_taxa</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">parent_taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_desc_taxids_old</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">revtra_counts</span> <span class="op">&lt;-</span> <span class="va">kraken_reports_viral_cleaned</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_desc_taxids_new</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_revtra <span class="op">=</span> <span class="va">n_reads_clade</span><span class="op">/</span><span class="va">n_reads_clade</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get genus composition</span></span>
+<span><span class="va">revtra_species</span> <span class="op">&lt;-</span> <span class="va">revtra_counts</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="st">"S"</span>, <span class="va">na_type</span> <span class="op">==</span> <span class="st">"RNA"</span><span class="op">)</span></span>
+<span><span class="va">revtra_species_major_tab</span> <span class="op">&lt;-</span> <span class="va">revtra_species</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_revtra_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_revtra</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_revtra_max</span> <span class="op">&gt;=</span> <span class="fl">0.04</span><span class="op">)</span></span>
+<span><span class="va">revtra_species_major_list</span> <span class="op">&lt;-</span> <span class="va">revtra_species_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span><span class="va">revtra_species_major</span> <span class="op">&lt;-</span> <span class="va">revtra_species</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">revtra_species_major_list</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span>, <span class="va">p_reads_revtra</span><span class="op">)</span></span>
+<span><span class="va">revtra_species_minor</span> <span class="op">&lt;-</span> <span class="va">revtra_species_major</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>p_reads_revtra_major <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_revtra</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Other"</span>, taxid<span class="op">=</span><span class="cn">NA</span>, p_reads_revtra <span class="op">=</span> <span class="fl">1</span><span class="op">-</span><span class="va">p_reads_revtra_major</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">name</span>, <span class="va">taxid</span>, <span class="va">sample</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">date</span>, <span class="va">p_reads_revtra</span><span class="op">)</span></span>
+<span><span class="va">revtra_species_display</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">revtra_species_major</span>, <span class="va">revtra_species_minor</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_revtra</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name</span>, levels<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">revtra_species_major_list</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_revtra</span>, classification<span class="op">=</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_revtra_species</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">revtra_species_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% Revtraviricetes Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral species"</span><span class="op">)</span></span>
+<span><span class="va">g_revtra_species</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/revtraviricetes-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="human-infecting-virus-reads-validation" class="level1"><h1>Human-infecting virus reads: validation</h1>
+<p>Next, I investigated the human-infecting virus read content of these unenriched samples. Using the same workflow I used for Brumfield et al, I identified 2811 RNA read pairs and 12792 DNA read pairs as putatively human viral: 0.003% and 0.005% of surviving reads, respectively.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb22"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">hv_reads_filtered_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"hv_hits_putative_filtered.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">hv_reads_filtered</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">hv_reads_filtered_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">libraries</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="va">date</span>, <span class="va">na_type</span><span class="op">)</span></span>
+<span><span class="va">n_hv_filtered</span> <span class="op">&lt;-</span> <span class="va">hv_reads_filtered</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">na_type</span>, <span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span> <span class="op">%&gt;%</span> <span class="fu">inner_join</span><span class="op">(</span><span class="va">basic_stats</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"ribo_initial"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">n_read_pairs</span><span class="op">)</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>n_putative <span class="op">=</span> <span class="va">n</span>, n_total <span class="op">=</span> <span class="va">n_read_pairs</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_putative</span><span class="op">/</span><span class="va">n_total</span>, pc_reads <span class="op">=</span> <span class="va">p_reads</span> <span class="op">*</span> <span class="fl">100</span><span class="op">)</span></span>
+<span><span class="va">n_hv_filtered_summ</span> <span class="op">&lt;-</span> <span class="va">n_hv_filtered</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">summarize</span><span class="op">(</span>n_putative <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_putative</span><span class="op">)</span>, n_total <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_total</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">n_putative</span><span class="op">/</span><span class="va">n_total</span>, pc_reads <span class="op">=</span> <span class="va">p_reads</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb23"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">mrg</span> <span class="op">&lt;-</span> <span class="va">hv_reads_filtered</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>kraken_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">assigned_hv</span>, <span class="st">"Kraken2 HV\nassignment"</span>,</span>
+<span>                               <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">hit_hv</span>, <span class="st">"Kraken2 HV\nhit"</span>,</span>
+<span>                                      <span class="st">"No hit or\nassignment"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">na_type</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">adj_score_fwd</span><span class="op">)</span>, <span class="fu">desc</span><span class="op">(</span><span class="va">adj_score_rev</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>seq_num <span class="op">=</span> <span class="fu">row_number</span><span class="op">(</span><span class="op">)</span>,</span>
+<span>         adj_score_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">pmax</a></span><span class="op">(</span><span class="va">adj_score_fwd</span>, <span class="va">adj_score_rev</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co"># Import Bowtie2/Kraken data and perform filtering steps</span></span>
+<span><span class="va">g_mrg</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">mrg</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_fwd</span>, y<span class="op">=</span><span class="va">adj_score_rev</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.5</span>, shape<span class="op">=</span><span class="fl">16</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(forward read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">65</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(reverse read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">65</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">na_type</span><span class="op">~</span><span class="va">kraken_label</span>, labeller <span class="op">=</span> <span class="fu">labeller</span><span class="op">(</span>kit <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span><span class="fl">20</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>aspect.ratio<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span><span class="va">g_mrg</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-hv-scores-1.png" class="img-fluid figure-img" width="768"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb24"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_hist_0</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">mrg</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_max</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> <span class="fu">geom_histogram</span><span class="op">(</span>binwidth<span class="op">=</span><span class="fl">5</span>,boundary<span class="op">=</span><span class="fl">0</span>,position<span class="op">=</span><span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">facet_grid</span><span class="op">(</span><span class="va">na_type</span><span class="op">~</span><span class="va">kraken_label</span>, scales<span class="op">=</span><span class="st">"free_y"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Maximum adjusted alignment score"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Read pairs"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Dark2"</span><span class="op">)</span> <span class="op">+</span> <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">20</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span></span>
+<span><span class="va">g_hist_0</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-hv-scores-2.png" class="img-fluid figure-img" width="768"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>As previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I’ve used for previous datasets.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb25"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">mrg_fasta</span> <span class="op">&lt;-</span>  <span class="va">mrg</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>seq_head <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">"&gt;"</span>, <span class="va">seq_id</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="va">ungroup</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span>header1<span class="op">=</span><span class="va">seq_head</span>, seq1<span class="op">=</span><span class="va">query_seq_fwd</span>, </span>
+<span>         header2<span class="op">=</span><span class="va">seq_head</span>, seq2<span class="op">=</span><span class="va">query_seq_rev</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>header1<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">header1</span>, <span class="st">"_1"</span><span class="op">)</span>, header2<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">header2</span>, <span class="st">"_2"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">mrg_fasta_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/do.call.html">do.call</a></span><span class="op">(</span><span class="va">paste</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">mrg_fasta</span>, sep<span class="op">=</span><span class="st">"\n"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste</a></span><span class="op">(</span>collapse<span class="op">=</span><span class="st">"\n"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/write.html">write</a></span><span class="op">(</span><span class="va">mrg_fasta_out</span>, <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"blast/putative-viral.fasta"</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb26"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Import BLAST results</span></span>
+<span><span class="co"># Pre-filtering BLAST results to save space</span></span>
+<span><span class="co"># blast_results_path &lt;- file.path(data_dir, "blast/putative-viral.blast.gz")</span></span>
+<span><span class="co"># blast_cols &lt;- c("qseqid", "sseqid", "sgi", "staxid", "qlen", "evalue", "bitscore", "qcovs", "length", "pident", "mismatch", "gapopen", "sstrand", "qstart", "qend", "sstart", "send")</span></span>
+<span><span class="co"># blast_results &lt;- read_tsv(blast_results_path, show_col_types = FALSE,</span></span>
+<span><span class="co">#                           col_names = blast_cols, col_types = cols(.default="c"))</span></span>
+<span><span class="va">blast_results_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"blast/putative-viral-best.blast.gz"</span><span class="op">)</span></span>
+<span><span class="va">blast_results</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">blast_results_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Filter for best hit for each query/subject combination</span></span>
+<span><span class="va">blast_results_best</span> <span class="op">&lt;-</span> <span class="va">blast_results</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">qseqid</span>, <span class="va">staxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">bitscore</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">length</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">length</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Rank hits for each query and filter for high-ranking hits</span></span>
+<span><span class="va">blast_results_ranked</span> <span class="op">&lt;-</span> <span class="va">blast_results_best</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">qseqid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>rank <span class="op">=</span> <span class="fu">dense_rank</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">blast_results_highrank</span> <span class="op">&lt;-</span> <span class="va">blast_results_ranked</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">&lt;=</span> <span class="fl">5</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>read_pair <span class="op">=</span> <span class="fu">str_split</span><span class="op">(</span><span class="va">qseqid</span>, <span class="st">"_"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">nth</span>, n<span class="op">=</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span>, </span>
+<span>         seq_id <span class="op">=</span> <span class="fu">str_split</span><span class="op">(</span><span class="va">qseqid</span>, <span class="st">"_"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">nth</span>, n<span class="op">=</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>bitscore <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Summarize by read pair and taxid</span></span>
+<span><span class="va">blast_results_paired</span> <span class="op">&lt;-</span> <span class="va">blast_results_highrank</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">seq_id</span>, <span class="va">staxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>bitscore_max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span>, bitscore_min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">bitscore</span><span class="op">)</span>,</span>
+<span>            n_reads <span class="op">=</span> <span class="fu">n</span><span class="op">(</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Add viral status</span></span>
+<span><span class="va">blast_results_viral</span> <span class="op">&lt;-</span> <span class="fu">mutate</span><span class="op">(</span><span class="va">blast_results_paired</span>, viral <span class="op">=</span> <span class="va">staxid</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>viral_full <span class="op">=</span> <span class="va">viral</span> <span class="op">&amp;</span> <span class="va">n_reads</span> <span class="op">==</span> <span class="fl">2</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Compare to Kraken &amp; Bowtie assignments</span></span>
+<span><span class="va">mrg_assign</span> <span class="op">&lt;-</span> <span class="va">mrg</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">seq_id</span>, <span class="va">taxid</span>, <span class="va">assigned_taxid</span>, <span class="va">adj_score_max</span>, <span class="va">na_type</span><span class="op">)</span></span>
+<span><span class="va">blast_results_assign</span> <span class="op">&lt;-</span> <span class="fu">left_join</span><span class="op">(</span><span class="va">blast_results_viral</span>, <span class="va">mrg_assign</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>taxid_match_bowtie <span class="op">=</span> <span class="op">(</span><span class="va">staxid</span> <span class="op">==</span> <span class="va">taxid</span><span class="op">)</span>,</span>
+<span>           taxid_match_kraken <span class="op">=</span> <span class="op">(</span><span class="va">staxid</span> <span class="op">==</span> <span class="va">assigned_taxid</span><span class="op">)</span>,</span>
+<span>           taxid_match_any <span class="op">=</span> <span class="va">taxid_match_bowtie</span> <span class="op">|</span> <span class="va">taxid_match_kraken</span><span class="op">)</span></span>
+<span><span class="va">blast_results_out</span> <span class="op">&lt;-</span> <span class="va">blast_results_assign</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">seq_id</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>viral_status <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">viral_full</span><span class="op">)</span>, <span class="fl">2</span>,</span>
+<span>                                  <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">taxid_match_any</span><span class="op">)</span>, <span class="fl">2</span>,</span>
+<span>                                             <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span><span class="op">(</span><span class="va">viral</span><span class="op">)</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>            .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb27"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Merge BLAST results with unenriched read data</span></span>
+<span><span class="va">mrg_blast</span> <span class="op">&lt;-</span> <span class="fu">full_join</span><span class="op">(</span><span class="va">mrg</span>, <span class="va">blast_results_out</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>viral_status <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">viral_status</span>, <span class="fl">0</span><span class="op">)</span>,</span>
+<span>         viral_status_out <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">viral_status</span> <span class="op">==</span> <span class="fl">0</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">mrg_blast_rna</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">na_type</span> <span class="op">==</span> <span class="st">"RNA"</span><span class="op">)</span></span>
+<span><span class="va">mrg_blast_dna</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">na_type</span> <span class="op">==</span> <span class="st">"DNA"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot RNA</span></span>
+<span><span class="va">g_mrg_blast_rna</span> <span class="op">&lt;-</span> <span class="va">mrg_blast_rna</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">ggplot</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_fwd</span>, y<span class="op">=</span><span class="va">adj_score_rev</span>, color<span class="op">=</span><span class="va">viral_status_out</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.5</span>, shape<span class="op">=</span><span class="fl">16</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(forward read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">65</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(reverse read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">65</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Set1"</span>, name <span class="op">=</span> <span class="st">"Viral status"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">viral_status_out</span><span class="op">~</span><span class="va">kraken_label</span>, labeller <span class="op">=</span> <span class="fu">labeller</span><span class="op">(</span>kit <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span><span class="fl">20</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="st">"RNA"</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">theme</span><span class="op">(</span>aspect.ratio<span class="op">=</span><span class="fl">1</span>, plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_mrg_blast_rna</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-blast-results-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb28"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Plot DNA</span></span>
+<span><span class="va">g_mrg_blast_dna</span> <span class="op">&lt;-</span> <span class="va">mrg_blast_dna</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">ggplot</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_fwd</span>, y<span class="op">=</span><span class="va">adj_score_rev</span>, color<span class="op">=</span><span class="va">viral_status_out</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.5</span>, shape<span class="op">=</span><span class="fl">16</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(forward read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">65</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"S(reverse read)"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">65</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span>,<span class="fl">10</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Set1"</span>, name <span class="op">=</span> <span class="st">"Viral status"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">viral_status_out</span><span class="op">~</span><span class="va">kraken_label</span>, labeller <span class="op">=</span> <span class="fu">labeller</span><span class="op">(</span>kit <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span><span class="fl">20</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="st">"DNA"</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">theme</span><span class="op">(</span>aspect.ratio<span class="op">=</span><span class="fl">1</span>, plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_mrg_blast_dna</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-blast-results-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb29"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">g_hist_1</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">mrg_blast</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">adj_score_max</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> <span class="fu">geom_histogram</span><span class="op">(</span>binwidth<span class="op">=</span><span class="fl">5</span>,boundary<span class="op">=</span><span class="fl">0</span>,position<span class="op">=</span><span class="st">"dodge"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">facet_grid</span><span class="op">(</span><span class="va">na_type</span><span class="op">~</span><span class="va">viral_status_out</span>, scales <span class="op">=</span> <span class="st">"free_y"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Maximum adjusted alignment score"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"# Read pairs"</span><span class="op">)</span> <span class="op">+</span> <span class="fu">scale_fill_brewer</span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Dark2"</span><span class="op">)</span> <span class="op">+</span> <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">geom_vline</span><span class="op">(</span>xintercept<span class="op">=</span><span class="fl">20</span>, linetype<span class="op">=</span><span class="st">"dashed"</span>, color<span class="op">=</span><span class="st">"red"</span><span class="op">)</span></span>
+<span><span class="va">g_hist_1</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-blast-histogram-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>These results look good on visual inspection, and indeed precision and sensitivity are both very high. For a disjunctive score threshold of 20, my updated workflow achieves an F1 score of 96.7% for RNA sequences and 98.2% for DNA sequences.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb30"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">test_sens_spec</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">tab</span>, <span class="va">score_threshold</span>, <span class="va">conjunctive</span>, <span class="va">include_special</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="va">include_special</span><span class="op">)</span> <span class="va">tab</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">tab</span>, <span class="va">viral_status_out</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"TRUE"</span>, <span class="st">"FALSE"</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="va">tab_retained</span> <span class="op">&lt;-</span> <span class="va">tab</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span></span>
+<span>    conjunctive <span class="op">=</span> <span class="va">conjunctive</span>,</span>
+<span>    retain_score_conjunctive <span class="op">=</span> <span class="op">(</span><span class="va">adj_score_fwd</span> <span class="op">&gt;</span> <span class="va">score_threshold</span> <span class="op">&amp;</span> <span class="va">adj_score_rev</span> <span class="op">&gt;</span> <span class="va">score_threshold</span><span class="op">)</span>, </span>
+<span>    retain_score_disjunctive <span class="op">=</span> <span class="op">(</span><span class="va">adj_score_fwd</span> <span class="op">&gt;</span> <span class="va">score_threshold</span> <span class="op">|</span> <span class="va">adj_score_rev</span> <span class="op">&gt;</span> <span class="va">score_threshold</span><span class="op">)</span>,</span>
+<span>    retain_score <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">conjunctive</span>, <span class="va">retain_score_conjunctive</span>, <span class="va">retain_score_disjunctive</span><span class="op">)</span>,</span>
+<span>    retain <span class="op">=</span> <span class="va">assigned_hv</span> <span class="op">|</span> <span class="va">hit_hv</span> <span class="op">|</span> <span class="va">retain_score</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span></span>
+<span>  <span class="va">pos_tru</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">==</span> <span class="st">"TRUE"</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">pos_fls</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">!=</span> <span class="st">"TRUE"</span>, <span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">neg_tru</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">!=</span> <span class="st">"TRUE"</span>, <span class="op">!</span><span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">neg_fls</span> <span class="op">&lt;-</span> <span class="va">tab_retained</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">viral_status_out</span> <span class="op">==</span> <span class="st">"TRUE"</span>, <span class="op">!</span><span class="va">retain</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">n</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sum</span></span>
+<span>  <span class="va">sensitivity</span> <span class="op">&lt;-</span> <span class="va">pos_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">pos_tru</span> <span class="op">+</span> <span class="va">neg_fls</span><span class="op">)</span></span>
+<span>  <span class="va">specificity</span> <span class="op">&lt;-</span> <span class="va">neg_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">neg_tru</span> <span class="op">+</span> <span class="va">pos_fls</span><span class="op">)</span></span>
+<span>  <span class="va">precision</span>   <span class="op">&lt;-</span> <span class="va">pos_tru</span> <span class="op">/</span> <span class="op">(</span><span class="va">pos_tru</span> <span class="op">+</span> <span class="va">pos_fls</span><span class="op">)</span></span>
+<span>  <span class="va">f1</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> <span class="va">precision</span> <span class="op">*</span> <span class="va">sensitivity</span> <span class="op">/</span> <span class="op">(</span><span class="va">precision</span> <span class="op">+</span> <span class="va">sensitivity</span><span class="op">)</span></span>
+<span>  <span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="op">(</span>threshold<span class="op">=</span><span class="va">score_threshold</span>, include_special <span class="op">=</span> <span class="va">include_special</span>, </span>
+<span>                conjunctive <span class="op">=</span> <span class="va">conjunctive</span>, sensitivity<span class="op">=</span><span class="va">sensitivity</span>, </span>
+<span>                specificity<span class="op">=</span><span class="va">specificity</span>, precision<span class="op">=</span><span class="va">precision</span>, f1<span class="op">=</span><span class="va">f1</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">out</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">range_f1</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">intab</span>, <span class="va">inc_special</span>, <span class="va">inrange</span><span class="op">=</span><span class="fl">15</span><span class="op">:</span><span class="fl">45</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">tss</span> <span class="op">&lt;-</span> <span class="fu">purrr</span><span class="fu">::</span><span class="fu"><a href="https://purrr.tidyverse.org/reference/partial.html">partial</a></span><span class="op">(</span><span class="va">test_sens_spec</span>, tab<span class="op">=</span><span class="va">intab</span>, include_special<span class="op">=</span><span class="va">inc_special</span><span class="op">)</span></span>
+<span>  <span class="va">stats_conj</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">inrange</span>, <span class="va">tss</span>, conjunctive<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span></span>
+<span>  <span class="va">stats_disj</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="va">inrange</span>, <span class="va">tss</span>, conjunctive<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">bind_rows</span></span>
+<span>  <span class="va">stats_all</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">stats_conj</span>, <span class="va">stats_disj</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">pivot_longer</span><span class="op">(</span><span class="op">!</span><span class="op">(</span><span class="va">threshold</span><span class="op">:</span><span class="va">conjunctive</span><span class="op">)</span>, names_to<span class="op">=</span><span class="st">"metric"</span>, values_to<span class="op">=</span><span class="st">"value"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">mutate</span><span class="op">(</span>conj_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">conjunctive</span>, <span class="st">"Conjunctive"</span>, <span class="st">"Disjunctive"</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">stats_all</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">inc_special</span> <span class="op">&lt;-</span> <span class="cn">FALSE</span></span>
+<span><span class="va">stats_rna</span> <span class="op">&lt;-</span> <span class="fu">range_f1</span><span class="op">(</span><span class="va">mrg_blast_rna</span>, <span class="va">inc_special</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>na_type <span class="op">=</span> <span class="st">"RNA"</span><span class="op">)</span></span>
+<span><span class="va">stats_dna</span> <span class="op">&lt;-</span> <span class="fu">range_f1</span><span class="op">(</span><span class="va">mrg_blast_dna</span>, <span class="va">inc_special</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>na_type <span class="op">=</span> <span class="st">"DNA"</span><span class="op">)</span></span>
+<span><span class="va">stats_0</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">stats_rna</span>, <span class="va">stats_dna</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>attempt<span class="op">=</span><span class="fl">0</span><span class="op">)</span></span>
+<span><span class="va">threshold_opt_0</span> <span class="op">&lt;-</span> <span class="va">stats_0</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">conj_label</span>,<span class="va">attempt</span>,<span class="va">na_type</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">metric</span> <span class="op">==</span> <span class="st">"f1"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">value</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">value</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">threshold</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">threshold</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_stats_0</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">stats_0</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">threshold</span>, y<span class="op">=</span><span class="va">value</span>, color<span class="op">=</span><span class="va">metric</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_vline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">threshold_opt_0</span>, mapping <span class="op">=</span> <span class="fu">aes</span><span class="op">(</span>xintercept<span class="op">=</span><span class="va">threshold</span><span class="op">)</span>,</span>
+<span>             color <span class="op">=</span> <span class="st">"red"</span>, linetype <span class="op">=</span> <span class="st">"dashed"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_line</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Value"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span><span class="op">)</span>, breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"Threshold"</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_brewer</span><span class="op">(</span>palette<span class="op">=</span><span class="st">"Set3"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">na_type</span><span class="op">~</span><span class="va">conj_label</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span></span>
+<span><span class="va">g_stats_0</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-f1-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>Looking into the composition of different read groups, nothing stands out except the predominance of HCMV (human betaherpesvirus 5), which is consistent with the Kraken results above and borne out by the BLASTN alignments:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb31"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">211787</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Human papillomavirus type 92"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">509154</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Porcine endogenous retrovirus C"</span></span>
+<span></span>
+<span><span class="va">major_threshold</span> <span class="op">&lt;-</span> <span class="fl">0.05</span></span>
+<span><span class="va">fp</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">viral_status_out</span>, </span>
+<span>           highscore <span class="op">=</span> <span class="va">adj_score_max</span> <span class="op">&gt;=</span> <span class="fl">20</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">count</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">viral_status_out</span>, <span class="va">highscore</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>p<span class="op">=</span><span class="va">n</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">viral_taxa</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">194958</span>, <span class="st">"Porcine endogenous retrovirus A"</span>, <span class="va">name</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">fp_major_tab</span> <span class="op">&lt;-</span> <span class="va">fp</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p</span> <span class="op">&gt;</span> <span class="va">major_threshold</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">fp_major_list</span> <span class="op">&lt;-</span> <span class="va">fp_major_tab</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="va">sort</span> <span class="op">%&gt;%</span> <span class="va">unique</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">.</span>, <span class="st">"Other"</span><span class="op">)</span></span>
+<span><span class="va">fp_major</span> <span class="op">&lt;-</span> <span class="va">fp</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>major <span class="op">=</span> <span class="va">p</span> <span class="op">&gt;</span> <span class="va">major_threshold</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">major</span>, <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">viral_status_out</span>, <span class="va">highscore</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n</span><span class="op">)</span>, p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span>  <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, levels <span class="op">=</span> <span class="va">fp_major_list</span><span class="op">)</span>,</span>
+<span>         score_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">highscore</span>, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span><span class="op">)</span>,</span>
+<span>         status_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="st">"True positive"</span>, <span class="st">"False positive"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_fp</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">fp_major</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">score_display</span>, y<span class="op">=</span><span class="va">p</span>, fill<span class="op">=</span><span class="va">name_display</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>position<span class="op">=</span><span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_discrete</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"True positive?"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"% reads"</span>, limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>, expand <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values <span class="op">=</span> <span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral\ntaxon"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">na_type</span><span class="op">~</span><span class="va">status_display</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">guides</span><span class="op">(</span>fill<span class="op">=</span><span class="fu">guide_legend</span><span class="op">(</span>ncol<span class="op">=</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_kit</span></span>
+<span><span class="va">g_fp</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/fp-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb32"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Configure</span></span>
+<span><span class="va">ref_taxid</span> <span class="op">&lt;-</span> <span class="fl">10359</span></span>
+<span></span>
+<span><span class="co"># Get taxon names</span></span>
+<span><span class="va">tax_names_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html">file.path</a></span><span class="op">(</span><span class="va">data_dir</span>, <span class="st">"taxid-names.tsv.gz"</span><span class="op">)</span></span>
+<span><span class="va">tax_names</span> <span class="op">&lt;-</span> <span class="fu">read_tsv</span><span class="op">(</span><span class="va">tax_names_path</span>, show_col_types <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Add missing names</span></span>
+<span><span class="va">tax_names_new</span> <span class="op">&lt;-</span> <span class="fu">tribble</span><span class="op">(</span><span class="op">~</span><span class="va">staxid</span>, <span class="op">~</span><span class="va">name</span>,</span>
+<span>                         <span class="fl">3050295</span>, <span class="st">"Cytomegalovirus humanbeta5"</span>,</span>
+<span>                         <span class="fl">459231</span>, <span class="st">"FLAG-tagging vector pFLAG97-TSR"</span><span class="op">)</span></span>
+<span><span class="va">tax_names</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">tax_names</span>, <span class="va">tax_names_new</span><span class="op">)</span></span>
+<span><span class="va">ref_name</span> <span class="op">&lt;-</span> <span class="va">tax_names</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">staxid</span> <span class="op">==</span> <span class="va">ref_taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">pull</span><span class="op">(</span><span class="va">name</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get major matches</span></span>
+<span><span class="va">fp_staxid</span> <span class="op">&lt;-</span> <span class="va">mrg_blast</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">taxid</span> <span class="op">==</span> <span class="va">ref_taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, highscore <span class="op">=</span> <span class="va">adj_score_max</span> <span class="op">&gt;=</span> <span class="fl">20</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>n_seq <span class="op">=</span> <span class="fu">n</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">blast_results_paired</span>, by<span class="op">=</span><span class="st">"seq_id"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>staxid <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html">as.integer</a></span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">tax_names</span>, by<span class="op">=</span><span class="st">"staxid"</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>sname<span class="op">=</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">tax_names</span> <span class="op">%&gt;%</span> <span class="fu">rename</span><span class="op">(</span>taxid<span class="op">=</span><span class="va">staxid</span><span class="op">)</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span><span class="va">fp_staxid_count</span> <span class="op">&lt;-</span> <span class="va">fp_staxid</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span>, <span class="va">na_type</span>, </span>
+<span>           <span class="va">taxid</span>, <span class="va">name</span>, <span class="va">staxid</span>, <span class="va">sname</span>, <span class="va">n_seq</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="va">count</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">viral_status_out</span>, <span class="va">highscore</span>, <span class="va">na_type</span>, <span class="va">taxid</span>, <span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p<span class="op">=</span><span class="va">n</span><span class="op">/</span><span class="va">n_seq</span><span class="op">)</span></span>
+<span><span class="va">fp_staxid_count_major</span> <span class="op">&lt;-</span> <span class="va">fp_staxid_count</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">n</span><span class="op">&gt;</span><span class="fl">1</span>, <span class="va">p</span><span class="op">&gt;</span><span class="fl">0.1</span>, <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">staxid</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>score_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">highscore</span>, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span><span class="op">)</span>,</span>
+<span>         status_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">viral_status_out</span>, </span>
+<span>                                 <span class="st">"True positive"</span>, <span class="st">"False positive"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">fp_staxid_count_major</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">p</span>, y<span class="op">=</span><span class="va">sname</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">na_type</span><span class="op">~</span><span class="va">status_display</span><span class="op">+</span><span class="va">score_display</span>, scales<span class="op">=</span><span class="st">"free"</span>,</span>
+<span>             labeller <span class="op">=</span> <span class="fu">label_wrap_gen</span><span class="op">(</span>multi_line <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% mapped reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span><span class="op">)</span>, breaks<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">labs</span><span class="op">(</span>title<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="va">ref_name</span>, <span class="st">" (taxid "</span>, <span class="va">ref_taxid</span>, <span class="st">")"</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span></span>
+<span>    axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span>,</span>
+<span>    plot.title <span class="op">=</span> <span class="fu">element_text</span><span class="op">(</span>size<span class="op">=</span><span class="fu">rel</span><span class="op">(</span><span class="fl">1.5</span><span class="op">)</span>, hjust<span class="op">=</span><span class="fl">0</span>, face<span class="op">=</span><span class="st">"plain"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/hcmv-blast-hits-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="human-infecting-viruses-overall-relative-abundance" class="level1"><h1>Human-infecting viruses: overall relative abundance</h1>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb33"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get raw read counts</span></span>
+<span><span class="va">read_counts_raw</span> <span class="op">&lt;-</span> <span class="va">basic_stats_raw</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, n_reads_raw <span class="op">=</span> <span class="va">n_read_pairs</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Get HV read counts</span></span>
+<span><span class="va">mrg_hv</span> <span class="op">&lt;-</span> <span class="va">mrg</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>hv_status <span class="op">=</span> <span class="va">assigned_hv</span> <span class="op">|</span> <span class="va">hit_hv</span> <span class="op">|</span> <span class="va">adj_score_max</span> <span class="op">&gt;=</span> <span class="fl">20</span><span class="op">)</span></span>
+<span><span class="va">read_counts_hv</span> <span class="op">&lt;-</span> <span class="va">mrg_hv</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">hv_status</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">count</span><span class="op">(</span>name<span class="op">=</span><span class="st">"n_reads_hv"</span><span class="op">)</span></span>
+<span><span class="va">read_counts</span> <span class="op">&lt;-</span> <span class="va">read_counts_raw</span> <span class="op">%&gt;%</span> <span class="fu">left_join</span><span class="op">(</span><span class="va">read_counts_hv</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>n_reads_hv <span class="op">=</span> <span class="fu">replace_na</span><span class="op">(</span><span class="va">n_reads_hv</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Aggregate</span></span>
+<span><span class="va">read_counts_total</span> <span class="op">&lt;-</span> <span class="va">read_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">ctrl</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_raw <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_raw</span><span class="op">)</span>,</span>
+<span>            n_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>sample<span class="op">=</span> <span class="st">"All samples"</span>, date <span class="op">=</span> <span class="st">"All dates"</span><span class="op">)</span></span>
+<span><span class="va">read_counts_agg</span> <span class="op">&lt;-</span> <span class="va">read_counts</span> <span class="op">%&gt;%</span> <span class="fu">arrange</span><span class="op">(</span><span class="va">date</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">bind_rows</span><span class="op">(</span><span class="va">read_counts_total</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>sample <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">sample</span><span class="op">)</span>,</span>
+<span>         p_reads_hv <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="va">n_reads_raw</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<p>In non-control samples, applying a disjunctive cutoff at S=20 identifies 2591 RNA reads and 12236 DNA reads as human-viral. This gives an overall relative HV abundance of <span class="math inline">\(1.63 \times 10^{-5}\)</span> for RNA reads and <span class="math inline">\(4.16 \times 10^{-5}\)</span> for DNA reads. Reassuringly, relative abundances in control samples are at least 10x lower. We also see a sharp drop in relative abundance for both nucleic-acid types for the period when the daycare was closed (2015-01-20):</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb34"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Visualize</span></span>
+<span><span class="va">g_phv_agg</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">read_counts_agg</span>, <span class="fu">aes</span><span class="op">(</span>x<span class="op">=</span><span class="va">date</span>, color<span class="op">=</span><span class="va">na_type</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_log10</span><span class="op">(</span><span class="st">"Relative abundance of human virus reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_discrete</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Collection Date"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">.</span><span class="op">~</span><span class="va">ctrl</span>, scales <span class="op">=</span> <span class="st">"free"</span>, space <span class="op">=</span> <span class="st">"free_x"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> <span class="va">theme_rotate</span></span>
+<span><span class="va">g_phv_agg</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/plot-hv-ra-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>These overall RA values are similar to those we’ve seen previously for non-panel-enriched wastewater RNA data. That said, it’s notable that the DNA read RA seen here is much higher than that seen in the only DNA wastewater dataset I’ve analyzed so far (Brumfield):</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb35"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Collate past RA values</span></span>
+<span><span class="va">ra_past</span> <span class="op">&lt;-</span> <span class="fu">tribble</span><span class="op">(</span><span class="op">~</span><span class="va">dataset</span>, <span class="op">~</span><span class="va">ra</span>, <span class="op">~</span><span class="va">na_type</span>, <span class="op">~</span><span class="va">panel_enriched</span>,</span>
+<span>                   <span class="st">"Brumfield"</span>, <span class="fl">5e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Brumfield"</span>, <span class="fl">3.66e-7</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Spurbeck"</span>, <span class="fl">5.44e-6</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Yang"</span>, <span class="fl">3.62e-4</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Rothman (unenriched)"</span>, <span class="fl">1.87e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Rothman (panel-enriched)"</span>, <span class="fl">3.3e-5</span>, <span class="st">"RNA"</span>, <span class="cn">TRUE</span>,</span>
+<span>                   <span class="st">"Crits-Christoph (unenriched)"</span>, <span class="fl">1.37e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Crits-Christoph (panel-enriched)"</span>, <span class="fl">1.26e-2</span>, <span class="st">"RNA"</span>, <span class="cn">TRUE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Collate new RA values</span></span>
+<span><span class="va">ra_new</span> <span class="op">&lt;-</span> <span class="fu">tribble</span><span class="op">(</span><span class="op">~</span><span class="va">dataset</span>, <span class="op">~</span><span class="va">ra</span>, <span class="op">~</span><span class="va">na_type</span>, <span class="op">~</span><span class="va">panel_enriched</span>,</span>
+<span>                   <span class="st">"Prussin (non-control)"</span>, <span class="fl">1.63e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span>                   <span class="st">"Prussin (non-control)"</span>, <span class="fl">4.16e-5</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">ra_comp</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">ra_past</span>, <span class="va">ra_new</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>dataset <span class="op">=</span> <span class="fu">fct_inorder</span><span class="op">(</span><span class="va">dataset</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_ra_comp</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">ra_comp</span>, <span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">dataset</span>, x<span class="op">=</span><span class="va">ra</span>, color<span class="op">=</span><span class="va">na_type</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_log10</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Relative abundance of human virus reads"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_ra_comp</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/ra-hv-past-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="human-infecting-viruses-taxonomy-and-composition" class="level1"><h1>Human-infecting viruses: taxonomy and composition</h1>
+<p>At the family level, we see that <em>Papillomaviridae</em>, <em>Herpesviridae</em>, and <em>Polyomaviridae</em> are the most abundant families in both DNA and RNA reads, with <em>Adenoviridae</em> and (in RNA reads) <em>Picornaviridae</em> also making a respectable showing. The <em>Herpesviridae</em> reads are, predictably, overwhelmingly from HCMV, while the <em>Papillomaviridae</em> and <em>Polyomaviridae</em> reads are split up among a larger number of related viruses:</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb36"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Get viral taxon names for putative HV reads</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">249588</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Mamastrovirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">194960</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Kobuvirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">688449</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Salivirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">585893</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Picobirnaviridae"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333922</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334207</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 3"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">369960</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Porcine type-C oncovirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333924</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 2"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">687329</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Anelloviridae"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">325455</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Gammapapillomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333750</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">694002</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betacoronavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334202</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Mupapillomavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">197911</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphainfluenzavirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">186938</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Respirovirus"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">333926</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Gammapapillomavirus 1"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">337051</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 1"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">337043</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Alphapapillomavirus 4"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">694003</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betacoronavirus 1"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334204</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Mupapillomavirus 2"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334208</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334208</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334208</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">name</span><span class="op">[</span><span class="va">viral_taxa</span><span class="op">$</span><span class="va">taxid</span> <span class="op">==</span> <span class="fl">334208</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span></span>
+<span></span>
+<span><span class="va">mrg_hv_named</span> <span class="op">&lt;-</span> <span class="va">mrg_hv</span> <span class="op">%&gt;%</span> <span class="fu">left_join</span><span class="op">(</span><span class="va">viral_taxa</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Discover viral species &amp; genera for HV reads</span></span>
+<span><span class="va">raise_rank</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">read_db</span>, <span class="va">taxid_db</span>, <span class="va">out_rank</span> <span class="op">=</span> <span class="st">"species"</span>, <span class="va">verbose</span> <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="co"># Get higher ranks than search rank</span></span>
+<span>  <span class="va">ranks</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"subspecies"</span>, <span class="st">"species"</span>, <span class="st">"subgenus"</span>, <span class="st">"genus"</span>, <span class="st">"subfamily"</span>, <span class="st">"family"</span>, <span class="st">"suborder"</span>, <span class="st">"order"</span>, <span class="st">"class"</span>, <span class="st">"subphylum"</span>, <span class="st">"phylum"</span>, <span class="st">"kingdom"</span>, <span class="st">"superkingdom"</span><span class="op">)</span></span>
+<span>  <span class="va">rank_match</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/which.min.html">which.max</a></span><span class="op">(</span><span class="va">ranks</span> <span class="op">==</span> <span class="va">out_rank</span><span class="op">)</span></span>
+<span>  <span class="va">high_ranks</span> <span class="op">&lt;-</span> <span class="va">ranks</span><span class="op">[</span><span class="va">rank_match</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">ranks</span><span class="op">)</span><span class="op">]</span></span>
+<span>  <span class="co"># Merge read DB and taxid DB</span></span>
+<span>  <span class="va">reads</span> <span class="op">&lt;-</span> <span class="va">read_db</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">parent_taxid</span>, <span class="op">-</span><span class="va">rank</span>, <span class="op">-</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">left_join</span><span class="op">(</span><span class="va">taxid_db</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span>  <span class="co"># Extract sequences that are already at appropriate rank</span></span>
+<span>  <span class="va">reads_rank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">reads</span>, <span class="va">rank</span> <span class="op">==</span> <span class="va">out_rank</span><span class="op">)</span></span>
+<span>  <span class="co"># Drop sequences at a higher rank and return unclassified sequences</span></span>
+<span>  <span class="va">reads_norank</span> <span class="op">&lt;-</span> <span class="va">reads</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">!=</span> <span class="va">out_rank</span>, <span class="op">!</span><span class="va">rank</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">high_ranks</span>, <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="kw">while</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html">nrow</a></span><span class="op">(</span><span class="va">reads_norank</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">0</span><span class="op">)</span><span class="op">{</span> <span class="co"># As long as there are unclassified sequences...</span></span>
+<span>    <span class="co"># Promote read taxids and re-merge with taxid DB, then re-classify and filter</span></span>
+<span>    <span class="va">reads_remaining</span> <span class="op">&lt;-</span> <span class="va">reads_norank</span> <span class="op">%&gt;%</span> <span class="fu">mutate</span><span class="op">(</span>taxid <span class="op">=</span> <span class="va">parent_taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>      <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">parent_taxid</span>, <span class="op">-</span><span class="va">rank</span>, <span class="op">-</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>      <span class="fu">left_join</span><span class="op">(</span><span class="va">taxid_db</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span>    <span class="va">reads_rank</span> <span class="op">&lt;-</span> <span class="va">reads_remaining</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">==</span> <span class="va">out_rank</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>      <span class="fu">bind_rows</span><span class="op">(</span><span class="va">reads_rank</span><span class="op">)</span></span>
+<span>    <span class="va">reads_norank</span> <span class="op">&lt;-</span> <span class="va">reads_remaining</span> <span class="op">%&gt;%</span></span>
+<span>      <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rank</span> <span class="op">!=</span> <span class="va">out_rank</span>, <span class="op">!</span><span class="va">rank</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">high_ranks</span>, <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">taxid</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="op">}</span></span>
+<span>  <span class="co"># Finally, extract and append reads that were excluded during the process</span></span>
+<span>  <span class="va">reads_dropped</span> <span class="op">&lt;-</span> <span class="va">reads</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="op">!</span><span class="va">seq_id</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">reads_rank</span><span class="op">$</span><span class="va">seq_id</span><span class="op">)</span></span>
+<span>  <span class="va">reads_out</span> <span class="op">&lt;-</span> <span class="va">reads_rank</span> <span class="op">%&gt;%</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">reads_dropped</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">select</span><span class="op">(</span><span class="op">-</span><span class="va">parent_taxid</span>, <span class="op">-</span><span class="va">rank</span>, <span class="op">-</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>    <span class="fu">left_join</span><span class="op">(</span><span class="va">taxid_db</span>, by<span class="op">=</span><span class="st">"taxid"</span><span class="op">)</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">reads_out</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span><span class="va">hv_reads_species</span> <span class="op">&lt;-</span> <span class="fu">raise_rank</span><span class="op">(</span><span class="va">mrg_hv_named</span>, <span class="va">viral_taxa</span>, <span class="st">"species"</span><span class="op">)</span></span>
+<span><span class="va">hv_reads_genus</span> <span class="op">&lt;-</span> <span class="fu">raise_rank</span><span class="op">(</span><span class="va">mrg_hv_named</span>, <span class="va">viral_taxa</span>, <span class="st">"genus"</span><span class="op">)</span></span>
+<span><span class="va">hv_reads_family</span> <span class="op">&lt;-</span> <span class="fu">raise_rank</span><span class="op">(</span><span class="va">mrg_hv_named</span>, <span class="va">viral_taxa</span>, <span class="st">"family"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb37"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_family</span> <span class="op">&lt;-</span> <span class="fl">0.06</span></span>
+<span></span>
+<span><span class="co"># Count reads for each human-viral family</span></span>
+<span><span class="va">hv_family_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_family</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_hv <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">hv_family_major_tab</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;</span> <span class="va">threshold_major_family</span><span class="op">)</span></span>
+<span><span class="va">hv_family_counts_major</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hv_family_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hv_family_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">hv_family_counts_display</span> <span class="op">&lt;-</span> <span class="va">hv_family_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_hv</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_hv_family</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">hv_family_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% HV Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral family"</span><span class="op">)</span></span>
+<span><span class="va">g_hv_family</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/hv-family-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb38"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_genus</span> <span class="op">&lt;-</span> <span class="fl">0.06</span></span>
+<span></span>
+<span><span class="co"># Count reads for each human-viral genus</span></span>
+<span><span class="va">hv_genus_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_genus</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_hv <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">hv_genus_major_tab</span> <span class="op">&lt;-</span> <span class="va">hv_genus_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;</span> <span class="va">threshold_major_genus</span><span class="op">)</span></span>
+<span><span class="va">hv_genus_counts_major</span> <span class="op">&lt;-</span> <span class="va">hv_genus_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hv_genus_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hv_genus_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">hv_genus_counts_display</span> <span class="op">&lt;-</span> <span class="va">hv_genus_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_hv</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_hv_genus</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">hv_genus_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% HV Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral genus"</span><span class="op">)</span></span>
+<span><span class="va">g_hv_genus</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/hv-genus-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb39"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">threshold_major_species</span> <span class="op">&lt;-</span> <span class="fl">0.10</span></span>
+<span></span>
+<span><span class="co"># Count reads for each human-viral species</span></span>
+<span><span class="va">hv_species_counts</span> <span class="op">&lt;-</span> <span class="va">hv_reads_species</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">count</span><span class="op">(</span>name <span class="op">=</span> <span class="st">"n_reads_hv"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_hv <span class="op">=</span> <span class="va">n_reads_hv</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Identify high-ranking families and group others</span></span>
+<span><span class="va">hv_species_major_tab</span> <span class="op">&lt;-</span> <span class="va">hv_species_counts</span> <span class="op">%&gt;%</span> <span class="fu">group_by</span><span class="op">(</span><span class="va">name</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">==</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">row_number</span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">arrange</span><span class="op">(</span><span class="fu">desc</span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">p_reads_hv</span> <span class="op">&gt;</span> <span class="va">threshold_major_species</span><span class="op">)</span></span>
+<span><span class="va">hv_species_counts_major</span> <span class="op">&lt;-</span> <span class="va">hv_species_counts</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">name</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span> <span class="va">hv_species_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">ctrl</span>, <span class="va">na_type</span>, <span class="va">name_display</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_hv</span><span class="op">)</span>, p_reads_hv <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">p_reads_hv</span><span class="op">)</span>, </span>
+<span>            .groups<span class="op">=</span><span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>name_display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="va">name_display</span>, </span>
+<span>                               levels <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">hv_species_major_tab</span><span class="op">$</span><span class="va">name</span>, <span class="st">"Other"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">hv_species_counts_display</span> <span class="op">&lt;-</span> <span class="va">hv_species_counts_major</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">rename</span><span class="op">(</span>p_reads <span class="op">=</span> <span class="va">p_reads_hv</span>, classification <span class="op">=</span> <span class="va">name_display</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_hv_species</span> <span class="op">&lt;-</span> <span class="va">g_comp_base</span> <span class="op">+</span> </span>
+<span>  <span class="fu">geom_col</span><span class="op">(</span>data<span class="op">=</span><span class="va">hv_species_counts_display</span>, position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_y_continuous</span><span class="op">(</span>name<span class="op">=</span><span class="st">"% HV Reads"</span>, limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1.01</span><span class="op">)</span>, </span>
+<span>                     breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">1</span>,<span class="fl">0.2</span><span class="op">)</span>,</span>
+<span>                     expand<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span>, labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="va">y</span><span class="op">*</span><span class="fl">100</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_fill_manual</span><span class="op">(</span>values<span class="op">=</span><span class="va">palette_viral</span>, name <span class="op">=</span> <span class="st">"Viral species"</span><span class="op">)</span></span>
+<span><span class="va">g_hv_species</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/hv-species-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+<p>Compared to the previous datasets I’ve analyzed, the most notable difference is the absence of enteric viruses: <em>Norovirus</em>, <em>Rotavirus</em>, and <em>Enterovirus</em> are all absent from the list of abundant human-viral taxa, as are ~all other gastrointestinal pathogens.</p>
+<p>Finally, here are the overall relative abundances of a set of specific viral genera picked out manually on the basis of scientific or medical interest. In the future, I’ll quantify the RA of these genera across all datasets analyzed with this pipeline to date, which should give us a better sense of how these data compare to others’ under this pipeline.</p>
+<div class="cell">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode" id="cb40"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Define reference genera</span></span>
+<span><span class="va">path_genera_rna</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Mamastrovirus"</span>, <span class="st">"Enterovirus"</span>, <span class="st">"Salivirus"</span>, <span class="st">"Kobuvirus"</span>, <span class="st">"Norovirus"</span>, <span class="st">"Sapovirus"</span>, <span class="st">"Rotavirus"</span>, <span class="st">"Alphacoronavirus"</span>, <span class="st">"Betacoronavirus"</span>,</span>
+<span>                 <span class="st">"Alphainfluenzavirus"</span>, <span class="st">"Betainfluenzavirus"</span>, <span class="st">"Lentivirus"</span><span class="op">)</span></span>
+<span><span class="va">path_genera_dna</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Mastadenovirus"</span>, <span class="st">"Alphapolyomavirus"</span>, <span class="st">"Betapolyomavirus"</span>, <span class="st">"Alphapapillomavirus"</span>, <span class="st">"Betapapillomavirus"</span>, <span class="st">"Orthopoxvirus"</span>, <span class="st">"Simplexvirus"</span>,</span>
+<span>                     <span class="st">"Lymphocryptovirus"</span>, <span class="st">"Cytomegalovirus"</span>, <span class="st">"Dependoparvovirus"</span><span class="op">)</span></span>
+<span><span class="va">path_genera</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="fu">tibble</span><span class="op">(</span>name<span class="op">=</span><span class="va">path_genera_rna</span>, genome_type<span class="op">=</span><span class="st">"RNA genome"</span><span class="op">)</span>,</span>
+<span>                         <span class="fu">tibble</span><span class="op">(</span>name<span class="op">=</span><span class="va">path_genera_dna</span>, genome_type<span class="op">=</span><span class="st">"DNA genome"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">viral_taxa</span>, by<span class="op">=</span><span class="st">"name"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Count in each sample</span></span>
+<span><span class="va">n_path_genera</span> <span class="op">&lt;-</span> <span class="va">hv_reads_genus</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">sample</span>, <span class="va">date</span>, <span class="va">na_type</span>, <span class="va">ctrl</span>, <span class="va">name</span>, <span class="va">taxid</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">count</span><span class="op">(</span>name<span class="op">=</span><span class="st">"n_reads_viral"</span><span class="op">)</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">inner_join</span><span class="op">(</span><span class="va">path_genera</span>, by<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"taxid"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">read_counts_raw</span>, by<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"sample"</span>, <span class="st">"date"</span>, <span class="st">"na_type"</span>, <span class="st">"ctrl"</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_viral <span class="op">=</span> <span class="va">n_reads_viral</span><span class="op">/</span><span class="va">n_reads_raw</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Pivot out and back to add zero lines</span></span>
+<span><span class="va">n_path_genera_out</span> <span class="op">&lt;-</span> <span class="va">n_path_genera</span> <span class="op">%&gt;%</span> <span class="va">ungroup</span> <span class="op">%&gt;%</span> <span class="fu">select</span><span class="op">(</span><span class="va">sample</span>, <span class="va">name</span>, <span class="va">n_reads_viral</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_wider</span><span class="op">(</span>names_from<span class="op">=</span><span class="st">"name"</span>, values_from<span class="op">=</span><span class="st">"n_reads_viral"</span>, values_fill<span class="op">=</span><span class="fl">0</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">pivot_longer</span><span class="op">(</span><span class="op">-</span><span class="va">sample</span>, names_to<span class="op">=</span><span class="st">"name"</span>, values_to<span class="op">=</span><span class="st">"n_reads_viral"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">read_counts_raw</span>, by<span class="op">=</span><span class="st">"sample"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">left_join</span><span class="op">(</span><span class="va">path_genera</span>, by<span class="op">=</span><span class="st">"name"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>p_reads_viral <span class="op">=</span> <span class="va">n_reads_viral</span><span class="op">/</span><span class="va">n_reads_raw</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">## Aggregate across dates</span></span>
+<span><span class="va">n_path_genera_stype</span> <span class="op">&lt;-</span> <span class="va">n_path_genera_out</span> <span class="op">%&gt;%</span> </span>
+<span>  <span class="fu">group_by</span><span class="op">(</span><span class="va">na_type</span>, <span class="va">ctrl</span>,</span>
+<span>           <span class="va">name</span>, <span class="va">taxid</span>, <span class="va">genome_type</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">summarize</span><span class="op">(</span>n_reads_raw <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_raw</span><span class="op">)</span>,</span>
+<span>            n_reads_viral <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">n_reads_viral</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op">%&gt;%</span></span>
+<span>  <span class="fu">mutate</span><span class="op">(</span>sample<span class="op">=</span><span class="st">"All samples"</span>, date<span class="op">=</span><span class="st">"All dates"</span>,</span>
+<span>         p_reads_viral <span class="op">=</span> <span class="va">n_reads_viral</span><span class="op">/</span><span class="va">n_reads_raw</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot</span></span>
+<span><span class="va">g_path_genera</span> <span class="op">&lt;-</span> <span class="fu">ggplot</span><span class="op">(</span><span class="va">n_path_genera_stype</span>,</span>
+<span>                        <span class="fu">aes</span><span class="op">(</span>y<span class="op">=</span><span class="va">name</span>, x<span class="op">=</span><span class="va">p_reads_viral</span>, color<span class="op">=</span><span class="va">na_type</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_x_log10</span><span class="op">(</span>name<span class="op">=</span><span class="st">"Relative abundance"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">scale_color_na</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">facet_grid</span><span class="op">(</span><span class="va">genome_type</span><span class="op">~</span><span class="va">ctrl</span>, scales<span class="op">=</span><span class="st">"free_y"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="va">theme_base</span> <span class="op">+</span> <span class="fu">theme</span><span class="op">(</span>axis.title.y <span class="op">=</span> <span class="fu">element_blank</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">g_path_genera</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details><div class="cell-output cell-output-stderr">
+<pre><code>Warning: Transformation introduced infinite values in continuous x-axis</code></pre>
+</div>
+<div class="cell-output-display">
+<div>
+<figure class="figure"><p><img src="2024-04-12_prussin_files/figure-html/ra-genera-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
+</div>
+</div>
+</section><section id="conclusion" class="level1"><h1>Conclusion</h1>
+<p>This is the first air-sampling dataset I’ve analyzed using this pipeline, and it was interesting to see the differences from the wastewater datasets I’ve been analyzing so far. Among the more striking differences were:</p>
+<ul>
+<li><p>A much higher total fraction of human reads;</p></li>
+<li><p>A lower total fraction of viral reads;</p></li>
+<li><p>Near-total absence of enteric viruses and <em>Tobamovirus</em>;</p></li>
+<li><p>Much higher relative abundance of herpesviruses, particularly HCMV, and papillomaviruses among human-infecting virus reads.</p></li>
+</ul>
+<p>Conversely, one thing that was not notably different, at least in RNA viruses, was the total relative abundance of human-infecting viruses as a whole. Given the lower fraction of reads made up of all viruses, this means that the fraction of total viruses arising from human-infecting viruses is much higher here than we’ve historically seen with wastewater data. In particular, HCMV represents a nontrivial fraction of total viruses for many DNA libraries, the first time I’ve seen a human pathogen show up significantly in the total virus composition data.</p>
+<p>Going forward, I have two more air sampling datasets to analyze, <a href="https://doi.org/10.1186/s40168-021-01044-7">Rosario et al.&nbsp;2018</a> and <a href="https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-021-01044-7">Leung et al.&nbsp;2021</a>. It will be interesting to see whether the findings from this dataset generalize to other air sampling contexts.</p>
+
+
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+<div class="sourceCode" id="cb42" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Workflow analysis of Prussin et al. (2019)"</span></span>
+<span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "Wastewater from a manhole in Maryland."</span></span>
+<span id="cb42-4"><a href="#cb42-4" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span><span class="co"> "Will Bradshaw"</span></span>
+<span id="cb42-5"><a href="#cb42-5" aria-hidden="true" tabindex="-1"></a><span class="an">date:</span><span class="co"> 2024-04-12</span></span>
+<span id="cb42-6"><a href="#cb42-6" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
+<span id="cb42-7"><a href="#cb42-7" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
+<span id="cb42-8"><a href="#cb42-8" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: true</span></span>
+<span id="cb42-9"><a href="#cb42-9" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
+<span id="cb42-10"><a href="#cb42-10" aria-hidden="true" tabindex="-1"></a><span class="co">    code-link: true</span></span>
+<span id="cb42-11"><a href="#cb42-11" aria-hidden="true" tabindex="-1"></a><span class="co">    df-print: paged</span></span>
+<span id="cb42-12"><a href="#cb42-12" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
+<span id="cb42-13"><a href="#cb42-13" aria-hidden="true" tabindex="-1"></a><span class="an">title-block-banner:</span><span class="co"> black</span></span>
+<span id="cb42-14"><a href="#cb42-14" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb42-15"><a href="#cb42-15" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-18"><a href="#cb42-18" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-19"><a href="#cb42-19" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: load-packages</span></span>
+<span id="cb42-20"><a href="#cb42-20" aria-hidden="true" tabindex="-1"></a><span class="co">#| include: false</span></span>
+<span id="cb42-21"><a href="#cb42-21" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
+<span id="cb42-22"><a href="#cb42-22" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(cowplot)</span>
+<span id="cb42-23"><a href="#cb42-23" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(patchwork)</span>
+<span id="cb42-24"><a href="#cb42-24" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(fastqcr)</span>
+<span id="cb42-25"><a href="#cb42-25" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(RColorBrewer)</span>
+<span id="cb42-26"><a href="#cb42-26" aria-hidden="true" tabindex="-1"></a><span class="fu">source</span>(<span class="st">"../scripts/aux_plot-theme.R"</span>)</span>
+<span id="cb42-27"><a href="#cb42-27" aria-hidden="true" tabindex="-1"></a>theme_base <span class="ot">&lt;-</span> theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">aspect.ratio =</span> <span class="cn">NULL</span>)</span>
+<span id="cb42-28"><a href="#cb42-28" aria-hidden="true" tabindex="-1"></a>theme_rotate <span class="ot">&lt;-</span> theme_base <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb42-29"><a href="#cb42-29" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="dv">1</span>, <span class="at">angle =</span> <span class="dv">45</span>),</span>
+<span id="cb42-30"><a href="#cb42-30" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb42-31"><a href="#cb42-31" aria-hidden="true" tabindex="-1"></a>theme_kit <span class="ot">&lt;-</span> theme_rotate <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb42-32"><a href="#cb42-32" aria-hidden="true" tabindex="-1"></a>  <span class="at">axis.title.x =</span> <span class="fu">element_blank</span>(),</span>
+<span id="cb42-33"><a href="#cb42-33" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb42-34"><a href="#cb42-34" aria-hidden="true" tabindex="-1"></a>tnl <span class="ot">&lt;-</span> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)</span>
+<span id="cb42-35"><a href="#cb42-35" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-36"><a href="#cb42-36" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-37"><a href="#cb42-37" aria-hidden="true" tabindex="-1"></a>Taking a break from working on <span class="co">[</span><span class="ot">P2RA datasets</span><span class="co">](https://doi.org/10.1101/2023.12.22.23300450)</span>, we're also working on a review of air sampling for viral pathogen detection. For that study, we're collecting and analyzing air MGS data that could give us a high-level idea of the likely viral composition of such samples.</span>
+<span id="cb42-38"><a href="#cb42-38" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-39"><a href="#cb42-39" aria-hidden="true" tabindex="-1"></a>The first dataset I'm looking at for this work is <span class="co">[</span><span class="ot">Prussin et al. (2019)</span><span class="co">](https://doi.org/10.1186/s40168-019-0672-z)</span>, a study of HVAC filter samples in a Virginia daycare center between 2014 and 2015. Samples were eluted from MERV-14 air filters collected every two weeks, with pairs of successive samples combined into four-week sampling periods. Like Brumfield et al, this study conducted both RNA and DNA sequencing; all samples were sequenced on an Illumina NextSeq500 with 2x150bp reads.</span>
+<span id="cb42-40"><a href="#cb42-40" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-41"><a href="#cb42-41" aria-hidden="true" tabindex="-1"></a><span class="fu"># The raw data</span></span>
+<span id="cb42-42"><a href="#cb42-42" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-43"><a href="#cb42-43" aria-hidden="true" tabindex="-1"></a>The Prussin dataset comprised sequencing data from 14 timepoints spread across the year, from 20th January 2014 to 2nd February 2015. Each sample represents a four-week sampling period. In addition to the 14 on-site samples, there were also two control samples, a negative control (NC) and an "unexposed filter" control (UFC), which were collected on December 23rd 2014.</span>
+<span id="cb42-44"><a href="#cb42-44" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-47"><a href="#cb42-47" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-48"><a href="#cb42-48" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb42-49"><a href="#cb42-49" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: read-qc-data</span></span>
+<span id="cb42-50"><a href="#cb42-50" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-51"><a href="#cb42-51" aria-hidden="true" tabindex="-1"></a><span class="co"># Data input paths</span></span>
+<span id="cb42-52"><a href="#cb42-52" aria-hidden="true" tabindex="-1"></a>data_dir <span class="ot">&lt;-</span> <span class="st">"../data/2024-04-11_prussin/"</span></span>
+<span id="cb42-53"><a href="#cb42-53" aria-hidden="true" tabindex="-1"></a>libraries_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"sample-metadata.csv"</span>)</span>
+<span id="cb42-54"><a href="#cb42-54" aria-hidden="true" tabindex="-1"></a>basic_stats_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"qc_basic_stats.tsv"</span>)</span>
+<span id="cb42-55"><a href="#cb42-55" aria-hidden="true" tabindex="-1"></a>adapter_stats_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"qc_adapter_stats.tsv"</span>)</span>
+<span id="cb42-56"><a href="#cb42-56" aria-hidden="true" tabindex="-1"></a>quality_base_stats_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"qc_quality_base_stats.tsv"</span>)</span>
+<span id="cb42-57"><a href="#cb42-57" aria-hidden="true" tabindex="-1"></a>quality_seq_stats_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"qc_quality_sequence_stats.tsv"</span>)</span>
+<span id="cb42-58"><a href="#cb42-58" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-59"><a href="#cb42-59" aria-hidden="true" tabindex="-1"></a><span class="co"># Import libraries and extract metadata from sample names</span></span>
+<span id="cb42-60"><a href="#cb42-60" aria-hidden="true" tabindex="-1"></a>libraries_raw <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(libraries_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb42-61"><a href="#cb42-61" aria-hidden="true" tabindex="-1"></a>libraries <span class="ot">&lt;-</span> libraries_raw <span class="sc">%&gt;%</span></span>
+<span id="cb42-62"><a href="#cb42-62" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(na_type)) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">na_type =</span> <span class="fu">fct_inorder</span>(na_type)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-63"><a href="#cb42-63" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(date) <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">start_date =</span> date) <span class="sc">%&gt;%</span></span>
+<span id="cb42-64"><a href="#cb42-64" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">end_date =</span> start_date <span class="sc">+</span> <span class="dv">28</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-65"><a href="#cb42-65" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">date =</span> <span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(end_date)),</span>
+<span id="cb42-66"><a href="#cb42-66" aria-hidden="true" tabindex="-1"></a>         <span class="at">ctrl =</span> <span class="fu">ifelse</span>(<span class="fu">grepl</span>(<span class="st">"Negative_Control"</span>, sample_alias), <span class="st">"NC"</span>,</span>
+<span id="cb42-67"><a href="#cb42-67" aria-hidden="true" tabindex="-1"></a>                       <span class="fu">ifelse</span>(<span class="fu">grepl</span>(<span class="st">"Unexposed_Filter"</span>, sample_alias),</span>
+<span id="cb42-68"><a href="#cb42-68" aria-hidden="true" tabindex="-1"></a>                              <span class="st">"UFC"</span>, <span class="st">"Non-Control"</span>)),</span>
+<span id="cb42-69"><a href="#cb42-69" aria-hidden="true" tabindex="-1"></a>         <span class="at">ctrl =</span> <span class="fu">factor</span>(ctrl, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">"Non-Control"</span>,</span>
+<span id="cb42-70"><a href="#cb42-70" aria-hidden="true" tabindex="-1"></a>                                        <span class="st">"NC"</span>,</span>
+<span id="cb42-71"><a href="#cb42-71" aria-hidden="true" tabindex="-1"></a>                                        <span class="st">"UFC"</span>)),</span>
+<span id="cb42-72"><a href="#cb42-72" aria-hidden="true" tabindex="-1"></a>         <span class="at">open =</span> (season <span class="sc">!=</span> <span class="st">"Closed"</span>) <span class="sc">&amp;</span> (ctrl <span class="sc">==</span> <span class="st">"On-Site"</span>))</span>
+<span id="cb42-73"><a href="#cb42-73" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-74"><a href="#cb42-74" aria-hidden="true" tabindex="-1"></a><span class="co"># Import QC data</span></span>
+<span id="cb42-75"><a href="#cb42-75" aria-hidden="true" tabindex="-1"></a>stages <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"raw_concat"</span>, <span class="st">"cleaned"</span>, <span class="st">"dedup"</span>, <span class="st">"ribo_initial"</span>, <span class="st">"ribo_secondary"</span>)</span>
+<span id="cb42-76"><a href="#cb42-76" aria-hidden="true" tabindex="-1"></a>basic_stats <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(basic_stats_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-77"><a href="#cb42-77" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-78"><a href="#cb42-78" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb42-79"><a href="#cb42-79" aria-hidden="true" tabindex="-1"></a>         <span class="at">sample =</span> <span class="fu">fct_inorder</span>(sample))</span>
+<span id="cb42-80"><a href="#cb42-80" aria-hidden="true" tabindex="-1"></a>adapter_stats <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(adapter_stats_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-81"><a href="#cb42-81" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">sample =</span> <span class="fu">sub</span>(<span class="st">"_2$"</span>, <span class="st">""</span>, sample)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-82"><a href="#cb42-82" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-83"><a href="#cb42-83" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb42-84"><a href="#cb42-84" aria-hidden="true" tabindex="-1"></a>         <span class="at">read_pair =</span> <span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(read_pair)))</span>
+<span id="cb42-85"><a href="#cb42-85" aria-hidden="true" tabindex="-1"></a>quality_base_stats <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(quality_base_stats_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-86"><a href="#cb42-86" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-87"><a href="#cb42-87" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb42-88"><a href="#cb42-88" aria-hidden="true" tabindex="-1"></a>         <span class="at">read_pair =</span> <span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(read_pair)))</span>
+<span id="cb42-89"><a href="#cb42-89" aria-hidden="true" tabindex="-1"></a>quality_seq_stats <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(quality_seq_stats_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-90"><a href="#cb42-90" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-91"><a href="#cb42-91" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">stage =</span> <span class="fu">factor</span>(stage, <span class="at">levels =</span> stages),</span>
+<span id="cb42-92"><a href="#cb42-92" aria-hidden="true" tabindex="-1"></a>         <span class="at">read_pair =</span> <span class="fu">fct_inorder</span>(<span class="fu">as.character</span>(read_pair)))</span>
+<span id="cb42-93"><a href="#cb42-93" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-94"><a href="#cb42-94" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter to raw data</span></span>
+<span id="cb42-95"><a href="#cb42-95" aria-hidden="true" tabindex="-1"></a>basic_stats_raw <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb42-96"><a href="#cb42-96" aria-hidden="true" tabindex="-1"></a>adapter_stats_raw <span class="ot">&lt;-</span> adapter_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb42-97"><a href="#cb42-97" aria-hidden="true" tabindex="-1"></a>quality_base_stats_raw <span class="ot">&lt;-</span> quality_base_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb42-98"><a href="#cb42-98" aria-hidden="true" tabindex="-1"></a>quality_seq_stats_raw <span class="ot">&lt;-</span> quality_seq_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"raw_concat"</span>)</span>
+<span id="cb42-99"><a href="#cb42-99" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-100"><a href="#cb42-100" aria-hidden="true" tabindex="-1"></a><span class="co"># Get key values for readout</span></span>
+<span id="cb42-101"><a href="#cb42-101" aria-hidden="true" tabindex="-1"></a>raw_read_counts <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type, ctrl) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-102"><a href="#cb42-102" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">rmin =</span> <span class="fu">min</span>(n_read_pairs), <span class="at">rmax=</span><span class="fu">max</span>(n_read_pairs),</span>
+<span id="cb42-103"><a href="#cb42-103" aria-hidden="true" tabindex="-1"></a>            <span class="at">rmean=</span><span class="fu">mean</span>(n_read_pairs), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb42-104"><a href="#cb42-104" aria-hidden="true" tabindex="-1"></a>raw_read_totals <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type, ctrl) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-105"><a href="#cb42-105" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_read_pairs =</span> <span class="fu">sum</span>(n_read_pairs), </span>
+<span id="cb42-106"><a href="#cb42-106" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_bases_approx =</span> <span class="fu">sum</span>(n_bases_approx), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb42-107"><a href="#cb42-107" aria-hidden="true" tabindex="-1"></a>raw_dup <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type, ctrl) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-108"><a href="#cb42-108" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">dmin =</span> <span class="fu">min</span>(percent_duplicates), <span class="at">dmax=</span><span class="fu">max</span>(percent_duplicates),</span>
+<span id="cb42-109"><a href="#cb42-109" aria-hidden="true" tabindex="-1"></a>            <span class="at">dmean=</span><span class="fu">mean</span>(percent_duplicates), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb42-110"><a href="#cb42-110" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-111"><a href="#cb42-111" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-112"><a href="#cb42-112" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-113"><a href="#cb42-113" aria-hidden="true" tabindex="-1"></a>The 14 positive samples from the dataset yielded 5M-20M (mean 11.3M) RNA-sequencing reads and 10M-42M (mean 21.0M) DNA-sequencing reads per sample, for a total of 159M RNA read pairs and 294M DNA read pairs (46.3 and 87.0 gigabases of sequence, respectively). Controls contributed an additional 1M RNA read pairs and 17.5M DNA read pairs.</span>
+<span id="cb42-114"><a href="#cb42-114" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-115"><a href="#cb42-115" aria-hidden="true" tabindex="-1"></a>In positive samples, read qualities were mostly high but tailed off slightly at the 3' end in some samples, suggesting the need for trimming. Adapter levels were high. Inferred duplication levels were low (2-4%) in DNA reads and moderate (21-44%) in RNA reads. Control sample reads were more problematic, with higher duplication and adapter levels and lower quality.</span>
+<span id="cb42-116"><a href="#cb42-116" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-119"><a href="#cb42-119" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-120"><a href="#cb42-120" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 9</span></span>
+<span id="cb42-121"><a href="#cb42-121" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb42-122"><a href="#cb42-122" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-basic-stats</span></span>
+<span id="cb42-123"><a href="#cb42-123" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-124"><a href="#cb42-124" aria-hidden="true" tabindex="-1"></a><span class="co"># Prepare data</span></span>
+<span id="cb42-125"><a href="#cb42-125" aria-hidden="true" tabindex="-1"></a>basic_stats_raw_metrics <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span></span>
+<span id="cb42-126"><a href="#cb42-126" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(date, na_type, ctrl,</span>
+<span id="cb42-127"><a href="#cb42-127" aria-hidden="true" tabindex="-1"></a>         <span class="st">`</span><span class="at"># Read pairs</span><span class="st">`</span> <span class="ot">=</span> n_read_pairs,</span>
+<span id="cb42-128"><a href="#cb42-128" aria-hidden="true" tabindex="-1"></a>         <span class="st">`</span><span class="at">Total base pairs</span><span class="sc">\n</span><span class="at">(approx)</span><span class="st">`</span> <span class="ot">=</span> n_bases_approx,</span>
+<span id="cb42-129"><a href="#cb42-129" aria-hidden="true" tabindex="-1"></a>         <span class="st">`</span><span class="at">% Duplicates</span><span class="sc">\n</span><span class="at">(FASTQC)</span><span class="st">`</span> <span class="ot">=</span> percent_duplicates) <span class="sc">%&gt;%</span></span>
+<span id="cb42-130"><a href="#cb42-130" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="sc">-</span>(date<span class="sc">:</span>ctrl), <span class="at">names_to =</span> <span class="st">"metric"</span>, <span class="at">values_to =</span> <span class="st">"value"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-131"><a href="#cb42-131" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">metric =</span> <span class="fu">fct_inorder</span>(metric))</span>
+<span id="cb42-132"><a href="#cb42-132" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-133"><a href="#cb42-133" aria-hidden="true" tabindex="-1"></a><span class="co"># Set up plot templates</span></span>
+<span id="cb42-134"><a href="#cb42-134" aria-hidden="true" tabindex="-1"></a>scale_fill_na <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_fill_brewer, <span class="at">palette=</span><span class="st">"Set1"</span>, </span>
+<span id="cb42-135"><a href="#cb42-135" aria-hidden="true" tabindex="-1"></a>                                <span class="at">name=</span><span class="st">"Nucleic acid type"</span>)</span>
+<span id="cb42-136"><a href="#cb42-136" aria-hidden="true" tabindex="-1"></a>g_basic <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(basic_stats_raw_metrics, </span>
+<span id="cb42-137"><a href="#cb42-137" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>value, <span class="at">fill=</span>na_type)) <span class="sc">+</span></span>
+<span id="cb42-138"><a href="#cb42-138" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">"dodge"</span>) <span class="sc">+</span></span>
+<span id="cb42-139"><a href="#cb42-139" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">name=</span><span class="st">"Collection Date"</span>) <span class="sc">+</span></span>
+<span id="cb42-140"><a href="#cb42-140" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-141"><a href="#cb42-141" aria-hidden="true" tabindex="-1"></a>  <span class="fu">expand_limits</span>(<span class="at">y=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">100</span>)) <span class="sc">+</span></span>
+<span id="cb42-142"><a href="#cb42-142" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_na</span>() <span class="sc">+</span> </span>
+<span id="cb42-143"><a href="#cb42-143" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(metric<span class="sc">~</span>ctrl, <span class="at">scales =</span> <span class="st">"free"</span>, <span class="at">space=</span><span class="st">"free_x"</span>, <span class="at">switch=</span><span class="st">"y"</span>) <span class="sc">+</span></span>
+<span id="cb42-144"><a href="#cb42-144" aria-hidden="true" tabindex="-1"></a>  theme_rotate <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb42-145"><a href="#cb42-145" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.title.y =</span> <span class="fu">element_blank</span>(),</span>
+<span id="cb42-146"><a href="#cb42-146" aria-hidden="true" tabindex="-1"></a>    <span class="at">strip.text.y =</span> <span class="fu">element_text</span>(<span class="at">face=</span><span class="st">"plain"</span>)</span>
+<span id="cb42-147"><a href="#cb42-147" aria-hidden="true" tabindex="-1"></a>  )</span>
+<span id="cb42-148"><a href="#cb42-148" aria-hidden="true" tabindex="-1"></a>g_basic</span>
+<span id="cb42-149"><a href="#cb42-149" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-150"><a href="#cb42-150" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-153"><a href="#cb42-153" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-154"><a href="#cb42-154" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-raw-quality</span></span>
+<span id="cb42-155"><a href="#cb42-155" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-156"><a href="#cb42-156" aria-hidden="true" tabindex="-1"></a><span class="co"># Set up plotting templates</span></span>
+<span id="cb42-157"><a href="#cb42-157" aria-hidden="true" tabindex="-1"></a>scale_color_na <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_color_brewer, <span class="at">palette=</span><span class="st">"Set1"</span>,</span>
+<span id="cb42-158"><a href="#cb42-158" aria-hidden="true" tabindex="-1"></a>                                 <span class="at">name=</span><span class="st">"Nucleic acid type"</span>)</span>
+<span id="cb42-159"><a href="#cb42-159" aria-hidden="true" tabindex="-1"></a>g_qual_raw <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">color=</span>na_type, <span class="at">linetype=</span>read_pair, </span>
+<span id="cb42-160"><a href="#cb42-160" aria-hidden="true" tabindex="-1"></a>                         <span class="at">group=</span><span class="fu">interaction</span>(sample,read_pair))) <span class="sc">+</span> </span>
+<span id="cb42-161"><a href="#cb42-161" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span> <span class="fu">scale_linetype_discrete</span>(<span class="at">name =</span> <span class="st">"Read Pair"</span>) <span class="sc">+</span></span>
+<span id="cb42-162"><a href="#cb42-162" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>),</span>
+<span id="cb42-163"><a href="#cb42-163" aria-hidden="true" tabindex="-1"></a>         <span class="at">linetype =</span> <span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>)) <span class="sc">+</span></span>
+<span id="cb42-164"><a href="#cb42-164" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb42-165"><a href="#cb42-165" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-166"><a href="#cb42-166" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize adapters</span></span>
+<span id="cb42-167"><a href="#cb42-167" aria-hidden="true" tabindex="-1"></a>g_adapters_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span> </span>
+<span id="cb42-168"><a href="#cb42-168" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>pc_adapters), <span class="at">data=</span>adapter_stats_raw) <span class="sc">+</span></span>
+<span id="cb42-169"><a href="#cb42-169" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Adapters"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb42-170"><a href="#cb42-170" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-171"><a href="#cb42-171" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb42-172"><a href="#cb42-172" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-173"><a href="#cb42-173" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(ctrl<span class="sc">~</span>adapter)</span>
+<span id="cb42-174"><a href="#cb42-174" aria-hidden="true" tabindex="-1"></a>g_adapters_raw</span>
+<span id="cb42-175"><a href="#cb42-175" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-176"><a href="#cb42-176" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize quality</span></span>
+<span id="cb42-177"><a href="#cb42-177" aria-hidden="true" tabindex="-1"></a>g_quality_base_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span></span>
+<span id="cb42-178"><a href="#cb42-178" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb42-179"><a href="#cb42-179" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb42-180"><a href="#cb42-180" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>mean_phred_score), <span class="at">data=</span>quality_base_stats_raw) <span class="sc">+</span></span>
+<span id="cb42-181"><a href="#cb42-181" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">10</span>,<span class="dv">45</span>)) <span class="sc">+</span></span>
+<span id="cb42-182"><a href="#cb42-182" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb42-183"><a href="#cb42-183" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-184"><a href="#cb42-184" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(ctrl<span class="sc">~</span>.)</span>
+<span id="cb42-185"><a href="#cb42-185" aria-hidden="true" tabindex="-1"></a>g_quality_base_raw</span>
+<span id="cb42-186"><a href="#cb42-186" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-187"><a href="#cb42-187" aria-hidden="true" tabindex="-1"></a>g_quality_seq_raw <span class="ot">&lt;-</span> g_qual_raw <span class="sc">+</span></span>
+<span id="cb42-188"><a href="#cb42-188" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb42-189"><a href="#cb42-189" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb42-190"><a href="#cb42-190" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>mean_phred_score, <span class="at">y=</span>n_sequences), <span class="at">data=</span>quality_seq_stats_raw) <span class="sc">+</span></span>
+<span id="cb42-191"><a href="#cb42-191" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-192"><a href="#cb42-192" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Sequences"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-193"><a href="#cb42-193" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(ctrl<span class="sc">~</span>., <span class="at">scales =</span> <span class="st">"free_y"</span>)</span>
+<span id="cb42-194"><a href="#cb42-194" aria-hidden="true" tabindex="-1"></a>g_quality_seq_raw</span>
+<span id="cb42-195"><a href="#cb42-195" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-196"><a href="#cb42-196" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-197"><a href="#cb42-197" aria-hidden="true" tabindex="-1"></a><span class="fu"># Preprocessing</span></span>
+<span id="cb42-198"><a href="#cb42-198" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-199"><a href="#cb42-199" aria-hidden="true" tabindex="-1"></a>The average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. For positive samples, on average, cleaning and deduplication removed about 19% of total read pairs from RNA libraries and about 7% from DNA libraries. Subsequent ribodepletion removed a further \~32% of total read pairs on average from RNA libraries but <span class="sc">\&lt;</span>0.5% of total read pairs from DNA libraries.</span>
+<span id="cb42-200"><a href="#cb42-200" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-201"><a href="#cb42-201" aria-hidden="true" tabindex="-1"></a>Control samples, meanwhile, lost an average of 63% of RNA read pairs and 21% of DNA read pairs during cleaning and deduplication, consistent with the lower qualities observed above. Subsequent ribodepletion removed an additional 9% of total RNA read pairs and 0.3% of total DNA read pairs.</span>
+<span id="cb42-202"><a href="#cb42-202" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-205"><a href="#cb42-205" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-206"><a href="#cb42-206" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-table</span></span>
+<span id="cb42-207"><a href="#cb42-207" aria-hidden="true" tabindex="-1"></a>n_reads_rel <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> </span>
+<span id="cb42-208"><a href="#cb42-208" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, date, na_type, ctrl, stage, </span>
+<span id="cb42-209"><a href="#cb42-209" aria-hidden="true" tabindex="-1"></a>         percent_duplicates, n_read_pairs) <span class="sc">%&gt;%</span></span>
+<span id="cb42-210"><a href="#cb42-210" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, na_type) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(sample, na_type, stage) <span class="sc">%&gt;%</span></span>
+<span id="cb42-211"><a href="#cb42-211" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_retained =</span> <span class="fu">replace_na</span>(n_read_pairs <span class="sc">/</span> <span class="fu">lag</span>(n_read_pairs), <span class="dv">0</span>),</span>
+<span id="cb42-212"><a href="#cb42-212" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost =</span> <span class="dv">1</span> <span class="sc">-</span> p_reads_retained,</span>
+<span id="cb42-213"><a href="#cb42-213" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_retained_abs =</span> n_read_pairs <span class="sc">/</span> n_read_pairs[<span class="dv">1</span>],</span>
+<span id="cb42-214"><a href="#cb42-214" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost_abs =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_retained_abs,</span>
+<span id="cb42-215"><a href="#cb42-215" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_lost_abs_marginal =</span> <span class="fu">replace_na</span>(p_reads_lost_abs <span class="sc">-</span> <span class="fu">lag</span>(p_reads_lost_abs), <span class="dv">0</span>))</span>
+<span id="cb42-216"><a href="#cb42-216" aria-hidden="true" tabindex="-1"></a>n_reads_rel_display <span class="ot">&lt;-</span> n_reads_rel <span class="sc">%&gt;%</span> </span>
+<span id="cb42-217"><a href="#cb42-217" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">Stage=</span>stage, <span class="st">`</span><span class="at">NA Type</span><span class="st">`</span><span class="ot">=</span>na_type, <span class="st">`</span><span class="at">Control?</span><span class="st">`</span><span class="ot">=</span>ctrl) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-218"><a href="#cb42-218" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(<span class="st">`</span><span class="at">Control?</span><span class="st">`</span>, <span class="st">`</span><span class="at">NA Type</span><span class="st">`</span>, Stage) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-219"><a href="#cb42-219" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="st">`</span><span class="at">% Total Reads Lost (Cumulative)</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">paste0</span>(<span class="fu">round</span>(<span class="fu">min</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">"-"</span>, <span class="fu">round</span>(<span class="fu">max</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">" (mean "</span>, <span class="fu">round</span>(<span class="fu">mean</span>(p_reads_lost_abs<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">")"</span>),</span>
+<span id="cb42-220"><a href="#cb42-220" aria-hidden="true" tabindex="-1"></a>            <span class="st">`</span><span class="at">% Total Reads Lost (Marginal)</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">paste0</span>(<span class="fu">round</span>(<span class="fu">min</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">"-"</span>, <span class="fu">round</span>(<span class="fu">max</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">" (mean "</span>, <span class="fu">round</span>(<span class="fu">mean</span>(p_reads_lost_abs_marginal<span class="sc">*</span><span class="dv">100</span>),<span class="dv">1</span>), <span class="st">")"</span>), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-221"><a href="#cb42-221" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(Stage <span class="sc">!=</span> <span class="st">"raw_concat"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-222"><a href="#cb42-222" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Stage =</span> Stage <span class="sc">%&gt;%</span> as.numeric <span class="sc">%&gt;%</span> <span class="fu">factor</span>(<span class="at">labels=</span><span class="fu">c</span>(<span class="st">"Trimming &amp; filtering"</span>, <span class="st">"Deduplication"</span>, <span class="st">"Initial ribodepletion"</span>, <span class="st">"Secondary ribodepletion"</span>)))</span>
+<span id="cb42-223"><a href="#cb42-223" aria-hidden="true" tabindex="-1"></a>n_reads_rel_display</span>
+<span id="cb42-224"><a href="#cb42-224" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-225"><a href="#cb42-225" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-228"><a href="#cb42-228" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-229"><a href="#cb42-229" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-figures</span></span>
+<span id="cb42-230"><a href="#cb42-230" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb42-231"><a href="#cb42-231" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 8</span></span>
+<span id="cb42-232"><a href="#cb42-232" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 6</span></span>
+<span id="cb42-233"><a href="#cb42-233" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-234"><a href="#cb42-234" aria-hidden="true" tabindex="-1"></a>g_stage_trace <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(basic_stats, <span class="fu">aes</span>(<span class="at">x=</span>stage, <span class="at">color=</span>na_type, <span class="at">group=</span>sample)) <span class="sc">+</span></span>
+<span id="cb42-235"><a href="#cb42-235" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span></span>
+<span id="cb42-236"><a href="#cb42-236" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(ctrl<span class="sc">~</span>na_type, <span class="at">scales=</span><span class="st">"free"</span>, <span class="at">ncol=</span><span class="dv">2</span>,</span>
+<span id="cb42-237"><a href="#cb42-237" aria-hidden="true" tabindex="-1"></a>             <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line=</span><span class="cn">FALSE</span>)) <span class="sc">+</span></span>
+<span id="cb42-238"><a href="#cb42-238" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb42-239"><a href="#cb42-239" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-240"><a href="#cb42-240" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot reads over preprocessing</span></span>
+<span id="cb42-241"><a href="#cb42-241" aria-hidden="true" tabindex="-1"></a>g_reads_stages <span class="ot">&lt;-</span> g_stage_trace <span class="sc">+</span></span>
+<span id="cb42-242"><a href="#cb42-242" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>n_read_pairs)) <span class="sc">+</span></span>
+<span id="cb42-243"><a href="#cb42-243" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"# Read pairs"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>))</span>
+<span id="cb42-244"><a href="#cb42-244" aria-hidden="true" tabindex="-1"></a>g_reads_stages</span>
+<span id="cb42-245"><a href="#cb42-245" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-246"><a href="#cb42-246" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot relative read losses during preprocessing</span></span>
+<span id="cb42-247"><a href="#cb42-247" aria-hidden="true" tabindex="-1"></a>g_reads_rel <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(n_reads_rel, <span class="fu">aes</span>(<span class="at">x=</span>stage, <span class="at">color=</span>na_type, <span class="at">group=</span>sample)) <span class="sc">+</span></span>
+<span id="cb42-248"><a href="#cb42-248" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>p_reads_lost_abs_marginal)) <span class="sc">+</span></span>
+<span id="cb42-249"><a href="#cb42-249" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"% Total Reads Lost"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), </span>
+<span id="cb42-250"><a href="#cb42-250" aria-hidden="true" tabindex="-1"></a>                     <span class="at">labels =</span> <span class="cf">function</span>(x) x<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb42-251"><a href="#cb42-251" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span></span>
+<span id="cb42-252"><a href="#cb42-252" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(ctrl<span class="sc">~</span>na_type, <span class="at">scales=</span><span class="st">"free"</span>, <span class="at">ncol=</span><span class="dv">2</span>,</span>
+<span id="cb42-253"><a href="#cb42-253" aria-hidden="true" tabindex="-1"></a>             <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line=</span><span class="cn">FALSE</span>)) <span class="sc">+</span></span>
+<span id="cb42-254"><a href="#cb42-254" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb42-255"><a href="#cb42-255" aria-hidden="true" tabindex="-1"></a>g_reads_rel</span>
+<span id="cb42-256"><a href="#cb42-256" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-257"><a href="#cb42-257" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-258"><a href="#cb42-258" aria-hidden="true" tabindex="-1"></a>In both positive and control samples, data cleaning with FASTP was very successful at removing adapters, with very few adapter sequences found by FASTQC at any stage after the raw data. FASTP was also successful at improving read quality.</span>
+<span id="cb42-259"><a href="#cb42-259" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-262"><a href="#cb42-262" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-263"><a href="#cb42-263" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb42-264"><a href="#cb42-264" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-quality</span></span>
+<span id="cb42-265"><a href="#cb42-265" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 7</span></span>
+<span id="cb42-266"><a href="#cb42-266" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-267"><a href="#cb42-267" aria-hidden="true" tabindex="-1"></a>g_qual <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">color=</span>na_type, <span class="at">linetype=</span>read_pair, </span>
+<span id="cb42-268"><a href="#cb42-268" aria-hidden="true" tabindex="-1"></a>                         <span class="at">group=</span><span class="fu">interaction</span>(sample,read_pair))) <span class="sc">+</span> </span>
+<span id="cb42-269"><a href="#cb42-269" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span> <span class="fu">scale_linetype_discrete</span>(<span class="at">name =</span> <span class="st">"Read Pair"</span>) <span class="sc">+</span></span>
+<span id="cb42-270"><a href="#cb42-270" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color=</span><span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>),</span>
+<span id="cb42-271"><a href="#cb42-271" aria-hidden="true" tabindex="-1"></a>         <span class="at">linetype =</span> <span class="fu">guide_legend</span>(<span class="at">nrow=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>)) <span class="sc">+</span></span>
+<span id="cb42-272"><a href="#cb42-272" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb42-273"><a href="#cb42-273" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-274"><a href="#cb42-274" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize adapters</span></span>
+<span id="cb42-275"><a href="#cb42-275" aria-hidden="true" tabindex="-1"></a>g_adapters <span class="ot">&lt;-</span> g_qual <span class="sc">+</span> </span>
+<span id="cb42-276"><a href="#cb42-276" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>pc_adapters), <span class="at">data=</span>adapter_stats) <span class="sc">+</span></span>
+<span id="cb42-277"><a href="#cb42-277" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Adapters"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">20</span>),</span>
+<span id="cb42-278"><a href="#cb42-278" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">50</span>,<span class="dv">10</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-279"><a href="#cb42-279" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb42-280"><a href="#cb42-280" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-281"><a href="#cb42-281" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>adapter)</span>
+<span id="cb42-282"><a href="#cb42-282" aria-hidden="true" tabindex="-1"></a>g_adapters</span>
+<span id="cb42-283"><a href="#cb42-283" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-284"><a href="#cb42-284" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize quality</span></span>
+<span id="cb42-285"><a href="#cb42-285" aria-hidden="true" tabindex="-1"></a>g_quality_base <span class="ot">&lt;-</span> g_qual <span class="sc">+</span></span>
+<span id="cb42-286"><a href="#cb42-286" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb42-287"><a href="#cb42-287" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb42-288"><a href="#cb42-288" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>position, <span class="at">y=</span>mean_phred_score), <span class="at">data=</span>quality_base_stats) <span class="sc">+</span></span>
+<span id="cb42-289"><a href="#cb42-289" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">10</span>,<span class="dv">45</span>)) <span class="sc">+</span></span>
+<span id="cb42-290"><a href="#cb42-290" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Position"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>),</span>
+<span id="cb42-291"><a href="#cb42-291" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">140</span>,<span class="dv">20</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-292"><a href="#cb42-292" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>.)</span>
+<span id="cb42-293"><a href="#cb42-293" aria-hidden="true" tabindex="-1"></a>g_quality_base</span>
+<span id="cb42-294"><a href="#cb42-294" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-295"><a href="#cb42-295" aria-hidden="true" tabindex="-1"></a>g_quality_seq <span class="ot">&lt;-</span> g_qual <span class="sc">+</span></span>
+<span id="cb42-296"><a href="#cb42-296" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">25</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb42-297"><a href="#cb42-297" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">30</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>) <span class="sc">+</span></span>
+<span id="cb42-298"><a href="#cb42-298" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>mean_phred_score, <span class="at">y=</span>n_sequences), <span class="at">data=</span>quality_seq_stats) <span class="sc">+</span></span>
+<span id="cb42-299"><a href="#cb42-299" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"Mean Phred score"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-300"><a href="#cb42-300" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Sequences"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-301"><a href="#cb42-301" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(stage<span class="sc">~</span>.)</span>
+<span id="cb42-302"><a href="#cb42-302" aria-hidden="true" tabindex="-1"></a>g_quality_seq</span>
+<span id="cb42-303"><a href="#cb42-303" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-304"><a href="#cb42-304" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-305"><a href="#cb42-305" aria-hidden="true" tabindex="-1"></a>According to FASTQC, deduplication was moderately effective at reducing measured duplicate levels in on-site samples, with FASTQC-measured levels falling from an average of 34% to 23% for RNA reads and from 2.7% to 2.2% for DNA reads.</span>
+<span id="cb42-306"><a href="#cb42-306" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-309"><a href="#cb42-309" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-310"><a href="#cb42-310" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: preproc-dedup</span></span>
+<span id="cb42-311"><a href="#cb42-311" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 8</span></span>
+<span id="cb42-312"><a href="#cb42-312" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 6</span></span>
+<span id="cb42-313"><a href="#cb42-313" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-314"><a href="#cb42-314" aria-hidden="true" tabindex="-1"></a>stage_dup <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type, ctrl, stage) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-315"><a href="#cb42-315" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">dmin =</span> <span class="fu">min</span>(percent_duplicates), <span class="at">dmax=</span><span class="fu">max</span>(percent_duplicates),</span>
+<span id="cb42-316"><a href="#cb42-316" aria-hidden="true" tabindex="-1"></a>            <span class="at">dmean=</span><span class="fu">mean</span>(percent_duplicates), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb42-317"><a href="#cb42-317" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-318"><a href="#cb42-318" aria-hidden="true" tabindex="-1"></a>g_dup_stages <span class="ot">&lt;-</span> g_stage_trace <span class="sc">+</span></span>
+<span id="cb42-319"><a href="#cb42-319" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>percent_duplicates)) <span class="sc">+</span></span>
+<span id="cb42-320"><a href="#cb42-320" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"% Duplicates"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>), <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))</span>
+<span id="cb42-321"><a href="#cb42-321" aria-hidden="true" tabindex="-1"></a>g_dup_stages</span>
+<span id="cb42-322"><a href="#cb42-322" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-323"><a href="#cb42-323" aria-hidden="true" tabindex="-1"></a>g_readlen_stages <span class="ot">&lt;-</span> g_stage_trace <span class="sc">+</span> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>mean_seq_len)) <span class="sc">+</span></span>
+<span id="cb42-324"><a href="#cb42-324" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="st">"Mean read length (nt)"</span>, <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="cn">NA</span>))</span>
+<span id="cb42-325"><a href="#cb42-325" aria-hidden="true" tabindex="-1"></a>g_readlen_stages</span>
+<span id="cb42-326"><a href="#cb42-326" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-327"><a href="#cb42-327" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-328"><a href="#cb42-328" aria-hidden="true" tabindex="-1"></a><span class="fu"># High-level composition</span></span>
+<span id="cb42-329"><a href="#cb42-329" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-330"><a href="#cb42-330" aria-hidden="true" tabindex="-1"></a>As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:</span>
+<span id="cb42-331"><a href="#cb42-331" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-334"><a href="#cb42-334" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-335"><a href="#cb42-335" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: prepare-composition</span></span>
+<span id="cb42-336"><a href="#cb42-336" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-337"><a href="#cb42-337" aria-hidden="true" tabindex="-1"></a><span class="co"># Import Bracken data</span></span>
+<span id="cb42-338"><a href="#cb42-338" aria-hidden="true" tabindex="-1"></a>bracken_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"bracken_counts.tsv"</span>)</span>
+<span id="cb42-339"><a href="#cb42-339" aria-hidden="true" tabindex="-1"></a>bracken <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(bracken_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb42-340"><a href="#cb42-340" aria-hidden="true" tabindex="-1"></a>total_assigned <span class="ot">&lt;-</span> bracken <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">summarize</span>(</span>
+<span id="cb42-341"><a href="#cb42-341" aria-hidden="true" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">"Total"</span>,</span>
+<span id="cb42-342"><a href="#cb42-342" aria-hidden="true" tabindex="-1"></a>  <span class="at">kraken_assigned_reads =</span> <span class="fu">sum</span>(kraken_assigned_reads),</span>
+<span id="cb42-343"><a href="#cb42-343" aria-hidden="true" tabindex="-1"></a>  <span class="at">added_reads =</span> <span class="fu">sum</span>(added_reads),</span>
+<span id="cb42-344"><a href="#cb42-344" aria-hidden="true" tabindex="-1"></a>  <span class="at">new_est_reads =</span> <span class="fu">sum</span>(new_est_reads),</span>
+<span id="cb42-345"><a href="#cb42-345" aria-hidden="true" tabindex="-1"></a>  <span class="at">fraction_total_reads =</span> <span class="fu">sum</span>(fraction_total_reads)</span>
+<span id="cb42-346"><a href="#cb42-346" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb42-347"><a href="#cb42-347" aria-hidden="true" tabindex="-1"></a>bracken_spread <span class="ot">&lt;-</span> bracken <span class="sc">%&gt;%</span> <span class="fu">select</span>(name, sample, new_est_reads) <span class="sc">%&gt;%</span></span>
+<span id="cb42-348"><a href="#cb42-348" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">tolower</span>(name)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-349"><a href="#cb42-349" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">id_cols =</span> <span class="st">"sample"</span>, <span class="at">names_from =</span> <span class="st">"name"</span>, </span>
+<span id="cb42-350"><a href="#cb42-350" aria-hidden="true" tabindex="-1"></a>              <span class="at">values_from =</span> <span class="st">"new_est_reads"</span>)</span>
+<span id="cb42-351"><a href="#cb42-351" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-352"><a href="#cb42-352" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads</span></span>
+<span id="cb42-353"><a href="#cb42-353" aria-hidden="true" tabindex="-1"></a>read_counts_preproc <span class="ot">&lt;-</span> basic_stats <span class="sc">%&gt;%</span> </span>
+<span id="cb42-354"><a href="#cb42-354" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, na_type, ctrl, date, stage, n_read_pairs) <span class="sc">%&gt;%</span></span>
+<span id="cb42-355"><a href="#cb42-355" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">id_cols =</span> <span class="fu">c</span>(<span class="st">"sample"</span>, <span class="st">"na_type"</span>, <span class="st">"ctrl"</span>, <span class="st">"date"</span>),</span>
+<span id="cb42-356"><a href="#cb42-356" aria-hidden="true" tabindex="-1"></a>              <span class="at">names_from=</span><span class="st">"stage"</span>, <span class="at">values_from=</span><span class="st">"n_read_pairs"</span>)</span>
+<span id="cb42-357"><a href="#cb42-357" aria-hidden="true" tabindex="-1"></a>read_counts <span class="ot">&lt;-</span> read_counts_preproc <span class="sc">%&gt;%</span></span>
+<span id="cb42-358"><a href="#cb42-358" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(total_assigned <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, new_est_reads), <span class="at">by =</span> <span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-359"><a href="#cb42-359" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">assigned =</span> new_est_reads) <span class="sc">%&gt;%</span></span>
+<span id="cb42-360"><a href="#cb42-360" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(bracken_spread, <span class="at">by=</span><span class="st">"sample"</span>)</span>
+<span id="cb42-361"><a href="#cb42-361" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-362"><a href="#cb42-362" aria-hidden="true" tabindex="-1"></a><span class="co"># Assess composition</span></span>
+<span id="cb42-363"><a href="#cb42-363" aria-hidden="true" tabindex="-1"></a>read_comp <span class="ot">&lt;-</span> <span class="fu">transmute</span>(read_counts, <span class="at">sample=</span>sample, <span class="at">na_type=</span>na_type,</span>
+<span id="cb42-364"><a href="#cb42-364" aria-hidden="true" tabindex="-1"></a>                       <span class="at">ctrl =</span> ctrl, <span class="at">date =</span> date,</span>
+<span id="cb42-365"><a href="#cb42-365" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_filtered =</span> raw_concat<span class="sc">-</span>cleaned,</span>
+<span id="cb42-366"><a href="#cb42-366" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_duplicate =</span> cleaned<span class="sc">-</span>dedup,</span>
+<span id="cb42-367"><a href="#cb42-367" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_ribosomal =</span> (dedup<span class="sc">-</span>ribo_initial) <span class="sc">+</span> (ribo_initial<span class="sc">-</span>ribo_secondary),</span>
+<span id="cb42-368"><a href="#cb42-368" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_unassigned =</span> ribo_secondary<span class="sc">-</span>assigned,</span>
+<span id="cb42-369"><a href="#cb42-369" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_bacterial =</span> bacteria,</span>
+<span id="cb42-370"><a href="#cb42-370" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_archaeal =</span> archaea,</span>
+<span id="cb42-371"><a href="#cb42-371" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_viral =</span> viruses,</span>
+<span id="cb42-372"><a href="#cb42-372" aria-hidden="true" tabindex="-1"></a>                       <span class="at">n_human =</span> eukaryota)</span>
+<span id="cb42-373"><a href="#cb42-373" aria-hidden="true" tabindex="-1"></a>read_comp_long <span class="ot">&lt;-</span> <span class="fu">pivot_longer</span>(read_comp, <span class="sc">-</span>(sample<span class="sc">:</span>date), </span>
+<span id="cb42-374"><a href="#cb42-374" aria-hidden="true" tabindex="-1"></a>                               <span class="at">names_to =</span> <span class="st">"classification"</span>,</span>
+<span id="cb42-375"><a href="#cb42-375" aria-hidden="true" tabindex="-1"></a>                               <span class="at">names_prefix =</span> <span class="st">"n_"</span>, <span class="at">values_to =</span> <span class="st">"n_reads"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-376"><a href="#cb42-376" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">fct_inorder</span>(<span class="fu">str_to_sentence</span>(classification))) <span class="sc">%&gt;%</span></span>
+<span id="cb42-377"><a href="#cb42-377" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_reads<span class="sc">/</span><span class="fu">sum</span>(n_reads))</span>
+<span id="cb42-378"><a href="#cb42-378" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-379"><a href="#cb42-379" aria-hidden="true" tabindex="-1"></a><span class="co"># Summarize composition</span></span>
+<span id="cb42-380"><a href="#cb42-380" aria-hidden="true" tabindex="-1"></a>read_comp_summ <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span> </span>
+<span id="cb42-381"><a href="#cb42-381" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(na_type, ctrl, classification) <span class="sc">%&gt;%</span></span>
+<span id="cb42-382"><a href="#cb42-382" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads =</span> <span class="fu">sum</span>(n_reads), <span class="at">.groups =</span> <span class="st">"drop_last"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-383"><a href="#cb42-383" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads =</span> <span class="fu">replace_na</span>(n_reads,<span class="dv">0</span>),</span>
+<span id="cb42-384"><a href="#cb42-384" aria-hidden="true" tabindex="-1"></a>    <span class="at">p_reads =</span> n_reads<span class="sc">/</span><span class="fu">sum</span>(n_reads),</span>
+<span id="cb42-385"><a href="#cb42-385" aria-hidden="true" tabindex="-1"></a>    <span class="at">pc_reads =</span> p_reads<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb42-386"><a href="#cb42-386" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-387"><a href="#cb42-387" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-390"><a href="#cb42-390" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-391"><a href="#cb42-391" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-composition-all</span></span>
+<span id="cb42-392"><a href="#cb42-392" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-393"><a href="#cb42-393" aria-hidden="true" tabindex="-1"></a><span class="co"># Prepare plotting templates</span></span>
+<span id="cb42-394"><a href="#cb42-394" aria-hidden="true" tabindex="-1"></a>g_comp_base <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">mapping=</span><span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">y=</span>p_reads, <span class="at">fill=</span>classification)) <span class="sc">+</span></span>
+<span id="cb42-395"><a href="#cb42-395" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">name=</span><span class="st">"Collection Date"</span>) <span class="sc">+</span></span>
+<span id="cb42-396"><a href="#cb42-396" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>ctrl, <span class="at">scales =</span> <span class="st">"free"</span>, <span class="at">space =</span> <span class="st">"free_x"</span>) <span class="sc">+</span></span>
+<span id="cb42-397"><a href="#cb42-397" aria-hidden="true" tabindex="-1"></a>  theme_rotate</span>
+<span id="cb42-398"><a href="#cb42-398" aria-hidden="true" tabindex="-1"></a>scale_y_pc_reads <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(scale_y_continuous, <span class="at">name =</span> <span class="st">"% Reads"</span>,</span>
+<span id="cb42-399"><a href="#cb42-399" aria-hidden="true" tabindex="-1"></a>                                   <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb42-400"><a href="#cb42-400" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-401"><a href="#cb42-401" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot overall composition</span></span>
+<span id="cb42-402"><a href="#cb42-402" aria-hidden="true" tabindex="-1"></a>g_comp <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> <span class="fu">geom_col</span>(<span class="at">data =</span> read_comp_long, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-403"><a href="#cb42-403" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_pc_reads</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>)) <span class="sc">+</span></span>
+<span id="cb42-404"><a href="#cb42-404" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Classification"</span>)</span>
+<span id="cb42-405"><a href="#cb42-405" aria-hidden="true" tabindex="-1"></a>g_comp</span>
+<span id="cb42-406"><a href="#cb42-406" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-407"><a href="#cb42-407" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot composition of minor components</span></span>
+<span id="cb42-408"><a href="#cb42-408" aria-hidden="true" tabindex="-1"></a>read_comp_minor <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span> </span>
+<span id="cb42-409"><a href="#cb42-409" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(classification <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"Archaeal"</span>, <span class="st">"Viral"</span>, <span class="st">"Other"</span>))</span>
+<span id="cb42-410"><a href="#cb42-410" aria-hidden="true" tabindex="-1"></a>palette_minor <span class="ot">&lt;-</span> <span class="fu">brewer.pal</span>(<span class="dv">9</span>, <span class="st">"Set1"</span>)[<span class="fu">c</span>(<span class="dv">6</span>,<span class="dv">7</span>,<span class="dv">9</span>)]</span>
+<span id="cb42-411"><a href="#cb42-411" aria-hidden="true" tabindex="-1"></a>g_comp_minor <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> <span class="fu">geom_col</span>(<span class="at">data=</span>read_comp_minor, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-412"><a href="#cb42-412" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_pc_reads</span>() <span class="sc">+</span></span>
+<span id="cb42-413"><a href="#cb42-413" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_minor, <span class="at">name =</span> <span class="st">"Classification"</span>)</span>
+<span id="cb42-414"><a href="#cb42-414" aria-hidden="true" tabindex="-1"></a>g_comp_minor</span>
+<span id="cb42-415"><a href="#cb42-415" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-416"><a href="#cb42-416" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-417"><a href="#cb42-417" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-420"><a href="#cb42-420" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-421"><a href="#cb42-421" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: composition-summary</span></span>
+<span id="cb42-422"><a href="#cb42-422" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-423"><a href="#cb42-423" aria-hidden="true" tabindex="-1"></a>p_reads_summ_group <span class="ot">&lt;-</span> read_comp_long <span class="sc">%&gt;%</span></span>
+<span id="cb42-424"><a href="#cb42-424" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">ifelse</span>(classification <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"Filtered"</span>, <span class="st">"Duplicate"</span>, <span class="st">"Unassigned"</span>), <span class="st">"Excluded"</span>, <span class="fu">as.character</span>(classification)),</span>
+<span id="cb42-425"><a href="#cb42-425" aria-hidden="true" tabindex="-1"></a>         <span class="at">classification =</span> <span class="fu">fct_inorder</span>(classification)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-426"><a href="#cb42-426" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(classification, sample, na_type, ctrl) <span class="sc">%&gt;%</span></span>
+<span id="cb42-427"><a href="#cb42-427" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads =</span> <span class="fu">sum</span>(p_reads), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-428"><a href="#cb42-428" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(classification, na_type, ctrl) <span class="sc">%&gt;%</span></span>
+<span id="cb42-429"><a href="#cb42-429" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">pc_min =</span> <span class="fu">min</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, <span class="at">pc_max =</span> <span class="fu">max</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, </span>
+<span id="cb42-430"><a href="#cb42-430" aria-hidden="true" tabindex="-1"></a>            <span class="at">pc_mean =</span> <span class="fu">mean</span>(p_reads)<span class="sc">*</span><span class="dv">100</span>, <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb42-431"><a href="#cb42-431" aria-hidden="true" tabindex="-1"></a>p_reads_summ_prep <span class="ot">&lt;-</span> p_reads_summ_group <span class="sc">%&gt;%</span></span>
+<span id="cb42-432"><a href="#cb42-432" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">classification =</span> <span class="fu">fct_inorder</span>(classification),</span>
+<span id="cb42-433"><a href="#cb42-433" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_min =</span> pc_min <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb42-434"><a href="#cb42-434" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_max =</span> pc_max <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb42-435"><a href="#cb42-435" aria-hidden="true" tabindex="-1"></a>         <span class="at">pc_mean =</span> pc_mean <span class="sc">%&gt;%</span> <span class="fu">signif</span>(<span class="at">digits=</span><span class="dv">2</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(format, <span class="at">scientific=</span><span class="cn">FALSE</span>, <span class="at">trim=</span><span class="cn">TRUE</span>, <span class="at">digits=</span><span class="dv">2</span>),</span>
+<span id="cb42-436"><a href="#cb42-436" aria-hidden="true" tabindex="-1"></a>         <span class="at">display =</span> <span class="fu">paste0</span>(pc_min, <span class="st">"-"</span>, pc_max, <span class="st">"% (mean "</span>, pc_mean, <span class="st">"%)"</span>))</span>
+<span id="cb42-437"><a href="#cb42-437" aria-hidden="true" tabindex="-1"></a>p_reads_summ <span class="ot">&lt;-</span> p_reads_summ_prep <span class="sc">%&gt;%</span></span>
+<span id="cb42-438"><a href="#cb42-438" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(ctrl, classification, na_type, display) <span class="sc">%&gt;%</span></span>
+<span id="cb42-439"><a href="#cb42-439" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">names_from=</span>na_type, <span class="at">values_from =</span> display) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-440"><a href="#cb42-440" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(ctrl, classification)</span>
+<span id="cb42-441"><a href="#cb42-441" aria-hidden="true" tabindex="-1"></a>p_reads_summ</span>
+<span id="cb42-442"><a href="#cb42-442" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-443"><a href="#cb42-443" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-444"><a href="#cb42-444" aria-hidden="true" tabindex="-1"></a>What we see is a very different picture from the wastewater samples I've been analyzing so far. Most notably, the fraction of (non-ribosomal) human reads is *much* higher. Even the 2015-01-20 samples, which were taken when the daycare center was closed for the winter holidays, showed human read fractions (for both nucleic-acid types) of <span class="sc">\&gt;</span>12%; non-control samples as a whole averaged 27% for RNA reads and 34% for DNA reads. Compare <span class="co">[</span><span class="ot">Brumfield</span><span class="co">](https://data.securebio.org/wills-public-notebook/notebooks/2024-04-08_brumfield.html)</span> (average 0.08% for RNA and 0.02% for DNA), <span class="co">[</span><span class="ot">Yang</span><span class="co">](https://data.securebio.org/wills-public-notebook/notebooks/2024-03-16_yang.html)</span> (mean 0.05% for RNA) or even <span class="co">[</span><span class="ot">Rothman</span><span class="co">](https://data.securebio.org/wills-public-notebook/notebooks/2024-02-27_rothman-1.html)</span> (mean 1.8% for non-panel-enriched RNA samples).</span>
+<span id="cb42-445"><a href="#cb42-445" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-446"><a href="#cb42-446" aria-hidden="true" tabindex="-1"></a>Conversely, total viral reads are very low: mean 0.033% for RNA reads and 0.019% for DNA reads. Wastewater RNA datasets have typically had much higher total viruses: mean 0.5% for Brumfield, about the same for Crits-Christoph, 5.5% for Yang, 4.5% for Rothman. Brumfield's DNA data contained substantially fewer viruses than their RNA data, but still more than Prussin: about 0.08% on average.</span>
+<span id="cb42-447"><a href="#cb42-447" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-448"><a href="#cb42-448" aria-hidden="true" tabindex="-1"></a>Looking at viral families...was less informative than usual, especially for DNA reads. It turns out that these samples contain a lot of viral reads that Kraken2 was only able to classify to the class level. In DNA reads, samples were dominated by *Caudoviricetes* phages, though *Herviviricetes* (which includes herpesviruses) and *Naldaviricetes* (a class of arthropod-infecting viruses) also put in a respectable showing. In RNA reads, *Caudoviricetes* was again a major presence, but *Alsuviricetes* (a family of primarily plant pathogens) was often as or more prevalent, and *Revtraviricetes* (a class that includes Hepatitis B virus and retroviruses) was also significant.</span>
+<span id="cb42-449"><a href="#cb42-449" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-452"><a href="#cb42-452" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-453"><a href="#cb42-453" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: extract-viral-taxa</span></span>
+<span id="cb42-454"><a href="#cb42-454" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-455"><a href="#cb42-455" aria-hidden="true" tabindex="-1"></a><span class="co"># Get viral taxonomy</span></span>
+<span id="cb42-456"><a href="#cb42-456" aria-hidden="true" tabindex="-1"></a>viral_taxa_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"viral-taxids.tsv.gz"</span>)</span>
+<span id="cb42-457"><a href="#cb42-457" aria-hidden="true" tabindex="-1"></a>viral_taxa <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(viral_taxa_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb42-458"><a href="#cb42-458" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-459"><a href="#cb42-459" aria-hidden="true" tabindex="-1"></a><span class="co"># Import Kraken reports &amp; extract viral taxa</span></span>
+<span id="cb42-460"><a href="#cb42-460" aria-hidden="true" tabindex="-1"></a>samples <span class="ot">&lt;-</span> <span class="fu">as.character</span>(basic_stats_raw<span class="sc">$</span>sample)</span>
+<span id="cb42-461"><a href="#cb42-461" aria-hidden="true" tabindex="-1"></a>col_names <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"pc_reads_total"</span>, <span class="st">"n_reads_clade"</span>, <span class="st">"n_reads_direct"</span>,</span>
+<span id="cb42-462"><a href="#cb42-462" aria-hidden="true" tabindex="-1"></a>               <span class="st">"rank"</span>, <span class="st">"taxid"</span>, <span class="st">"name"</span>)</span>
+<span id="cb42-463"><a href="#cb42-463" aria-hidden="true" tabindex="-1"></a>report_paths <span class="ot">&lt;-</span> <span class="fu">paste0</span>(data_dir, <span class="st">"kraken/"</span>, samples, <span class="st">".report.gz"</span>)</span>
+<span id="cb42-464"><a href="#cb42-464" aria-hidden="true" tabindex="-1"></a>kraken_reports <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(samples), <span class="cf">function</span>(n) </span>
+<span id="cb42-465"><a href="#cb42-465" aria-hidden="true" tabindex="-1"></a>  <span class="fu">read_tsv</span>(report_paths[n], <span class="at">col_names =</span> col_names, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-466"><a href="#cb42-466" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">sample =</span> samples[n])) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb42-467"><a href="#cb42-467" aria-hidden="true" tabindex="-1"></a>kraken_reports_viral <span class="ot">&lt;-</span> <span class="fu">filter</span>(kraken_reports, taxid <span class="sc">%in%</span> viral_taxa<span class="sc">$</span>taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-468"><a href="#cb42-468" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span></span>
+<span id="cb42-469"><a href="#cb42-469" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_viral =</span> n_reads_clade<span class="sc">/</span>n_reads_clade[<span class="dv">1</span>])</span>
+<span id="cb42-470"><a href="#cb42-470" aria-hidden="true" tabindex="-1"></a>kraken_reports_viral_cleaned <span class="ot">&lt;-</span> kraken_reports_viral <span class="sc">%&gt;%</span></span>
+<span id="cb42-471"><a href="#cb42-471" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-472"><a href="#cb42-472" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="sc">-</span>pc_reads_total, <span class="sc">-</span>n_reads_direct) <span class="sc">%&gt;%</span></span>
+<span id="cb42-473"><a href="#cb42-473" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, p_reads_viral, n_reads_clade, <span class="fu">everything</span>())</span>
+<span id="cb42-474"><a href="#cb42-474" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-475"><a href="#cb42-475" aria-hidden="true" tabindex="-1"></a>viral_classes <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"C"</span>)</span>
+<span id="cb42-476"><a href="#cb42-476" aria-hidden="true" tabindex="-1"></a>viral_families <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"F"</span>)</span>
+<span id="cb42-477"><a href="#cb42-477" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-478"><a href="#cb42-478" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-479"><a href="#cb42-479" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-482"><a href="#cb42-482" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-483"><a href="#cb42-483" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: viral-class-composition</span></span>
+<span id="cb42-484"><a href="#cb42-484" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-485"><a href="#cb42-485" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify major viral classes</span></span>
+<span id="cb42-486"><a href="#cb42-486" aria-hidden="true" tabindex="-1"></a>viral_classes_major_tab <span class="ot">&lt;-</span> viral_classes <span class="sc">%&gt;%</span> </span>
+<span id="cb42-487"><a href="#cb42-487" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-488"><a href="#cb42-488" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_viral_max =</span> <span class="fu">max</span>(p_reads_viral), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-489"><a href="#cb42-489" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_viral_max <span class="sc">&gt;=</span> <span class="fl">0.04</span>)</span>
+<span id="cb42-490"><a href="#cb42-490" aria-hidden="true" tabindex="-1"></a>viral_classes_major_list <span class="ot">&lt;-</span> viral_classes_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb42-491"><a href="#cb42-491" aria-hidden="true" tabindex="-1"></a>viral_classes_major <span class="ot">&lt;-</span> viral_classes <span class="sc">%&gt;%</span> </span>
+<span id="cb42-492"><a href="#cb42-492" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">%in%</span> viral_classes_major_list) <span class="sc">%&gt;%</span></span>
+<span id="cb42-493"><a href="#cb42-493" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, na_type, ctrl, date, p_reads_viral)</span>
+<span id="cb42-494"><a href="#cb42-494" aria-hidden="true" tabindex="-1"></a>viral_classes_minor <span class="ot">&lt;-</span> viral_classes_major <span class="sc">%&gt;%</span> </span>
+<span id="cb42-495"><a href="#cb42-495" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, na_type, ctrl, date) <span class="sc">%&gt;%</span></span>
+<span id="cb42-496"><a href="#cb42-496" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_viral_major =</span> <span class="fu">sum</span>(p_reads_viral), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-497"><a href="#cb42-497" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="st">"Other"</span>, <span class="at">taxid=</span><span class="cn">NA</span>, <span class="at">p_reads_viral =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_viral_major) <span class="sc">%&gt;%</span></span>
+<span id="cb42-498"><a href="#cb42-498" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, na_type, ctrl, date, p_reads_viral)</span>
+<span id="cb42-499"><a href="#cb42-499" aria-hidden="true" tabindex="-1"></a>viral_classes_display <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(viral_classes_major, viral_classes_minor) <span class="sc">%&gt;%</span></span>
+<span id="cb42-500"><a href="#cb42-500" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_viral)) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-501"><a href="#cb42-501" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels=</span><span class="fu">c</span>(viral_classes_major_list, <span class="st">"Other"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb42-502"><a href="#cb42-502" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_viral, <span class="at">classification=</span>name)</span>
+<span id="cb42-503"><a href="#cb42-503" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-504"><a href="#cb42-504" aria-hidden="true" tabindex="-1"></a>palette_viral <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">brewer.pal</span>(<span class="dv">12</span>, <span class="st">"Set3"</span>), <span class="fu">brewer.pal</span>(<span class="dv">8</span>, <span class="st">"Set2"</span>))</span>
+<span id="cb42-505"><a href="#cb42-505" aria-hidden="true" tabindex="-1"></a>g_classes <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb42-506"><a href="#cb42-506" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>viral_classes_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-507"><a href="#cb42-507" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Viral Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb42-508"><a href="#cb42-508" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb42-509"><a href="#cb42-509" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral class"</span>)</span>
+<span id="cb42-510"><a href="#cb42-510" aria-hidden="true" tabindex="-1"></a>g_classes</span>
+<span id="cb42-511"><a href="#cb42-511" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-512"><a href="#cb42-512" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-513"><a href="#cb42-513" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-514"><a href="#cb42-514" aria-hidden="true" tabindex="-1"></a>Of these, the most interesting are the strong presence of *Herviviricetes* in the DNA reads and *Revtraviricetes* in the RNA reads, as both of these are families that contain important human pathogens.</span>
+<span id="cb42-515"><a href="#cb42-515" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-516"><a href="#cb42-516" aria-hidden="true" tabindex="-1"></a>Digging into the former, it turns out these reads are composed almost exclusively of *Herpesviridae* at the family level. Within non-control samples, these arise overwhelmingly from *Cytomegalovirus.* Digging in at the species level, these in turn are primarily attributed to a single CMV, Human betaherpesvirus 5, a.k.a. human cytomegalovirus (HCMV). I was excited to see this: this is the first time a single human pathogen, or even all human pathogens combined, have constituted a significant fraction of all viral reads in a sample I've analyzed with this pipeline.</span>
+<span id="cb42-517"><a href="#cb42-517" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-520"><a href="#cb42-520" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-521"><a href="#cb42-521" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: herviviricetes</span></span>
+<span id="cb42-522"><a href="#cb42-522" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-523"><a href="#cb42-523" aria-hidden="true" tabindex="-1"></a><span class="co"># Get all read counts in class</span></span>
+<span id="cb42-524"><a href="#cb42-524" aria-hidden="true" tabindex="-1"></a>hervi_taxid <span class="ot">&lt;-</span> <span class="dv">2731363</span></span>
+<span id="cb42-525"><a href="#cb42-525" aria-hidden="true" tabindex="-1"></a>hervi_desc_taxids_old <span class="ot">&lt;-</span> hervi_taxid</span>
+<span id="cb42-526"><a href="#cb42-526" aria-hidden="true" tabindex="-1"></a>hervi_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(hervi_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> hervi_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb42-527"><a href="#cb42-527" aria-hidden="true" tabindex="-1"></a><span class="cf">while</span> (<span class="fu">length</span>(hervi_desc_taxids_new) <span class="sc">&gt;</span> <span class="fu">length</span>(hervi_desc_taxids_old)){</span>
+<span id="cb42-528"><a href="#cb42-528" aria-hidden="true" tabindex="-1"></a>  hervi_desc_taxids_old <span class="ot">&lt;-</span> hervi_desc_taxids_new</span>
+<span id="cb42-529"><a href="#cb42-529" aria-hidden="true" tabindex="-1"></a>  hervi_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(hervi_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> hervi_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb42-530"><a href="#cb42-530" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb42-531"><a href="#cb42-531" aria-hidden="true" tabindex="-1"></a>hervi_counts <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span></span>
+<span id="cb42-532"><a href="#cb42-532" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">%in%</span> hervi_desc_taxids_new) <span class="sc">%&gt;%</span></span>
+<span id="cb42-533"><a href="#cb42-533" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hervi =</span> n_reads_clade<span class="sc">/</span>n_reads_clade[<span class="dv">1</span>])</span>
+<span id="cb42-534"><a href="#cb42-534" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-535"><a href="#cb42-535" aria-hidden="true" tabindex="-1"></a><span class="co"># Get genus composition</span></span>
+<span id="cb42-536"><a href="#cb42-536" aria-hidden="true" tabindex="-1"></a>hervi_genera <span class="ot">&lt;-</span> hervi_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"S"</span>, na_type <span class="sc">==</span> <span class="st">"DNA"</span>)</span>
+<span id="cb42-537"><a href="#cb42-537" aria-hidden="true" tabindex="-1"></a>hervi_genera_major_tab <span class="ot">&lt;-</span> hervi_genera <span class="sc">%&gt;%</span> </span>
+<span id="cb42-538"><a href="#cb42-538" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-539"><a href="#cb42-539" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_hervi_max =</span> <span class="fu">max</span>(p_reads_hervi), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-540"><a href="#cb42-540" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hervi_max <span class="sc">&gt;=</span> <span class="fl">0.04</span>)</span>
+<span id="cb42-541"><a href="#cb42-541" aria-hidden="true" tabindex="-1"></a>hervi_genera_major_list <span class="ot">&lt;-</span> hervi_genera_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb42-542"><a href="#cb42-542" aria-hidden="true" tabindex="-1"></a>hervi_genera_major <span class="ot">&lt;-</span> hervi_genera <span class="sc">%&gt;%</span> </span>
+<span id="cb42-543"><a href="#cb42-543" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">%in%</span> hervi_genera_major_list) <span class="sc">%&gt;%</span></span>
+<span id="cb42-544"><a href="#cb42-544" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, na_type, ctrl, date, p_reads_hervi)</span>
+<span id="cb42-545"><a href="#cb42-545" aria-hidden="true" tabindex="-1"></a>hervi_genera_minor <span class="ot">&lt;-</span> hervi_genera_major <span class="sc">%&gt;%</span> </span>
+<span id="cb42-546"><a href="#cb42-546" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, na_type, ctrl, date) <span class="sc">%&gt;%</span></span>
+<span id="cb42-547"><a href="#cb42-547" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_hervi_major =</span> <span class="fu">sum</span>(p_reads_hervi), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-548"><a href="#cb42-548" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="st">"Other"</span>, <span class="at">taxid=</span><span class="cn">NA</span>, <span class="at">p_reads_hervi =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_hervi_major) <span class="sc">%&gt;%</span></span>
+<span id="cb42-549"><a href="#cb42-549" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, na_type, ctrl, date, p_reads_hervi)</span>
+<span id="cb42-550"><a href="#cb42-550" aria-hidden="true" tabindex="-1"></a>hervi_genera_display <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(hervi_genera_major, hervi_genera_minor) <span class="sc">%&gt;%</span></span>
+<span id="cb42-551"><a href="#cb42-551" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hervi)) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-552"><a href="#cb42-552" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels=</span><span class="fu">c</span>(hervi_genera_major_list, <span class="st">"Other"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb42-553"><a href="#cb42-553" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_hervi, <span class="at">classification=</span>name)</span>
+<span id="cb42-554"><a href="#cb42-554" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-555"><a href="#cb42-555" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb42-556"><a href="#cb42-556" aria-hidden="true" tabindex="-1"></a>g_hervi_genera <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb42-557"><a href="#cb42-557" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>hervi_genera_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-558"><a href="#cb42-558" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Herviviricetes Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb42-559"><a href="#cb42-559" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb42-560"><a href="#cb42-560" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb42-561"><a href="#cb42-561" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral species"</span>)</span>
+<span id="cb42-562"><a href="#cb42-562" aria-hidden="true" tabindex="-1"></a>g_hervi_genera</span>
+<span id="cb42-563"><a href="#cb42-563" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-564"><a href="#cb42-564" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-565"><a href="#cb42-565" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-566"><a href="#cb42-566" aria-hidden="true" tabindex="-1"></a>*Revtraviricetes* reads are similarly dominated by a single viral genus, *Alpharetrovirus.* Digging in at the species level, we see contributions from a variety of avian oncoviruses. To my (admittedly non-expert) knowledge, none of these infect humans, and I think they are probably primarily arising from local birds (or possibly rats).</span>
+<span id="cb42-567"><a href="#cb42-567" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-570"><a href="#cb42-570" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-571"><a href="#cb42-571" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: revtraviricetes</span></span>
+<span id="cb42-572"><a href="#cb42-572" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-573"><a href="#cb42-573" aria-hidden="true" tabindex="-1"></a><span class="co"># Get all read counts in class</span></span>
+<span id="cb42-574"><a href="#cb42-574" aria-hidden="true" tabindex="-1"></a>revtra_taxid <span class="ot">&lt;-</span> <span class="dv">2732514</span></span>
+<span id="cb42-575"><a href="#cb42-575" aria-hidden="true" tabindex="-1"></a>revtra_desc_taxids_old <span class="ot">&lt;-</span> revtra_taxid</span>
+<span id="cb42-576"><a href="#cb42-576" aria-hidden="true" tabindex="-1"></a>revtra_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(revtra_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> revtra_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb42-577"><a href="#cb42-577" aria-hidden="true" tabindex="-1"></a><span class="cf">while</span> (<span class="fu">length</span>(revtra_desc_taxids_new) <span class="sc">&gt;</span> <span class="fu">length</span>(revtra_desc_taxids_old)){</span>
+<span id="cb42-578"><a href="#cb42-578" aria-hidden="true" tabindex="-1"></a>  revtra_desc_taxids_old <span class="ot">&lt;-</span> revtra_desc_taxids_new</span>
+<span id="cb42-579"><a href="#cb42-579" aria-hidden="true" tabindex="-1"></a>  revtra_desc_taxids_new <span class="ot">&lt;-</span> <span class="fu">unique</span>(<span class="fu">c</span>(revtra_desc_taxids_old, viral_taxa <span class="sc">%&gt;%</span> <span class="fu">filter</span>(parent_taxid <span class="sc">%in%</span> revtra_desc_taxids_old) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(taxid)))</span>
+<span id="cb42-580"><a href="#cb42-580" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb42-581"><a href="#cb42-581" aria-hidden="true" tabindex="-1"></a>revtra_counts <span class="ot">&lt;-</span> kraken_reports_viral_cleaned <span class="sc">%&gt;%</span></span>
+<span id="cb42-582"><a href="#cb42-582" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(taxid <span class="sc">%in%</span> revtra_desc_taxids_new) <span class="sc">%&gt;%</span></span>
+<span id="cb42-583"><a href="#cb42-583" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_revtra =</span> n_reads_clade<span class="sc">/</span>n_reads_clade[<span class="dv">1</span>])</span>
+<span id="cb42-584"><a href="#cb42-584" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-585"><a href="#cb42-585" aria-hidden="true" tabindex="-1"></a><span class="co"># Get genus composition</span></span>
+<span id="cb42-586"><a href="#cb42-586" aria-hidden="true" tabindex="-1"></a>revtra_species <span class="ot">&lt;-</span> revtra_counts <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> <span class="st">"S"</span>, na_type <span class="sc">==</span> <span class="st">"RNA"</span>)</span>
+<span id="cb42-587"><a href="#cb42-587" aria-hidden="true" tabindex="-1"></a>revtra_species_major_tab <span class="ot">&lt;-</span> revtra_species <span class="sc">%&gt;%</span> </span>
+<span id="cb42-588"><a href="#cb42-588" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-589"><a href="#cb42-589" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_revtra_max =</span> <span class="fu">max</span>(p_reads_revtra), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-590"><a href="#cb42-590" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_revtra_max <span class="sc">&gt;=</span> <span class="fl">0.04</span>)</span>
+<span id="cb42-591"><a href="#cb42-591" aria-hidden="true" tabindex="-1"></a>revtra_species_major_list <span class="ot">&lt;-</span> revtra_species_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb42-592"><a href="#cb42-592" aria-hidden="true" tabindex="-1"></a>revtra_species_major <span class="ot">&lt;-</span> revtra_species <span class="sc">%&gt;%</span> </span>
+<span id="cb42-593"><a href="#cb42-593" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">%in%</span> revtra_species_major_list) <span class="sc">%&gt;%</span></span>
+<span id="cb42-594"><a href="#cb42-594" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, na_type, ctrl, date, p_reads_revtra)</span>
+<span id="cb42-595"><a href="#cb42-595" aria-hidden="true" tabindex="-1"></a>revtra_species_minor <span class="ot">&lt;-</span> revtra_species_major <span class="sc">%&gt;%</span> </span>
+<span id="cb42-596"><a href="#cb42-596" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, na_type, ctrl, date) <span class="sc">%&gt;%</span></span>
+<span id="cb42-597"><a href="#cb42-597" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">p_reads_revtra_major =</span> <span class="fu">sum</span>(p_reads_revtra), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-598"><a href="#cb42-598" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="st">"Other"</span>, <span class="at">taxid=</span><span class="cn">NA</span>, <span class="at">p_reads_revtra =</span> <span class="dv">1</span><span class="sc">-</span>p_reads_revtra_major) <span class="sc">%&gt;%</span></span>
+<span id="cb42-599"><a href="#cb42-599" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(name, taxid, sample, na_type, ctrl, date, p_reads_revtra)</span>
+<span id="cb42-600"><a href="#cb42-600" aria-hidden="true" tabindex="-1"></a>revtra_species_display <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(revtra_species_major, revtra_species_minor) <span class="sc">%&gt;%</span></span>
+<span id="cb42-601"><a href="#cb42-601" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_revtra)) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-602"><a href="#cb42-602" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">factor</span>(name, <span class="at">levels=</span><span class="fu">c</span>(revtra_species_major_list, <span class="st">"Other"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb42-603"><a href="#cb42-603" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_revtra, <span class="at">classification=</span>name)</span>
+<span id="cb42-604"><a href="#cb42-604" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-605"><a href="#cb42-605" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb42-606"><a href="#cb42-606" aria-hidden="true" tabindex="-1"></a>g_revtra_species <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb42-607"><a href="#cb42-607" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>revtra_species_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-608"><a href="#cb42-608" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% Revtraviricetes Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb42-609"><a href="#cb42-609" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb42-610"><a href="#cb42-610" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb42-611"><a href="#cb42-611" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral species"</span>)</span>
+<span id="cb42-612"><a href="#cb42-612" aria-hidden="true" tabindex="-1"></a>g_revtra_species</span>
+<span id="cb42-613"><a href="#cb42-613" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-614"><a href="#cb42-614" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-615"><a href="#cb42-615" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-616"><a href="#cb42-616" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting virus reads: validation</span></span>
+<span id="cb42-617"><a href="#cb42-617" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-618"><a href="#cb42-618" aria-hidden="true" tabindex="-1"></a>Next, I investigated the human-infecting virus read content of these unenriched samples. Using the same workflow I used for Brumfield et al, I identified 2811 RNA read pairs and 12792 DNA read pairs as putatively human viral: 0.003% and 0.005% of surviving reads, respectively.</span>
+<span id="cb42-619"><a href="#cb42-619" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-622"><a href="#cb42-622" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-623"><a href="#cb42-623" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-read-counts</span></span>
+<span id="cb42-624"><a href="#cb42-624" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"hv_hits_putative_filtered.tsv.gz"</span>)</span>
+<span id="cb42-625"><a href="#cb42-625" aria-hidden="true" tabindex="-1"></a>hv_reads_filtered <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(hv_reads_filtered_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-626"><a href="#cb42-626" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(libraries, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-627"><a href="#cb42-627" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(date, na_type)</span>
+<span id="cb42-628"><a href="#cb42-628" aria-hidden="true" tabindex="-1"></a>n_hv_filtered <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample, date, na_type, ctrl) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> <span class="fu">inner_join</span>(basic_stats <span class="sc">%&gt;%</span> <span class="fu">filter</span>(stage <span class="sc">==</span> <span class="st">"ribo_initial"</span>) <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, n_read_pairs), <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">n_putative =</span> n, <span class="at">n_total =</span> n_read_pairs) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads <span class="sc">*</span> <span class="dv">100</span>)</span>
+<span id="cb42-629"><a href="#cb42-629" aria-hidden="true" tabindex="-1"></a>n_hv_filtered_summ <span class="ot">&lt;-</span> n_hv_filtered <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type, ctrl) <span class="sc">%&gt;%</span> <span class="fu">summarize</span>(<span class="at">n_putative =</span> <span class="fu">sum</span>(n_putative), <span class="at">n_total =</span> <span class="fu">sum</span>(n_total), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p_reads =</span> n_putative<span class="sc">/</span>n_total, <span class="at">pc_reads =</span> p_reads<span class="sc">*</span><span class="dv">100</span>)</span>
+<span id="cb42-630"><a href="#cb42-630" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-631"><a href="#cb42-631" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-634"><a href="#cb42-634" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-635"><a href="#cb42-635" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-scores</span></span>
+<span id="cb42-636"><a href="#cb42-636" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb42-637"><a href="#cb42-637" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-width: 8</span></span>
+<span id="cb42-638"><a href="#cb42-638" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-639"><a href="#cb42-639" aria-hidden="true" tabindex="-1"></a>mrg <span class="ot">&lt;-</span> hv_reads_filtered <span class="sc">%&gt;%</span></span>
+<span id="cb42-640"><a href="#cb42-640" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">kraken_label =</span> <span class="fu">ifelse</span>(assigned_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">assignment"</span>,</span>
+<span id="cb42-641"><a href="#cb42-641" aria-hidden="true" tabindex="-1"></a>                               <span class="fu">ifelse</span>(hit_hv, <span class="st">"Kraken2 HV</span><span class="sc">\n</span><span class="st">hit"</span>,</span>
+<span id="cb42-642"><a href="#cb42-642" aria-hidden="true" tabindex="-1"></a>                                      <span class="st">"No hit or</span><span class="sc">\n</span><span class="st">assignment"</span>))) <span class="sc">%&gt;%</span></span>
+<span id="cb42-643"><a href="#cb42-643" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, na_type) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(adj_score_fwd), <span class="fu">desc</span>(adj_score_rev)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-644"><a href="#cb42-644" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_num =</span> <span class="fu">row_number</span>(),</span>
+<span id="cb42-645"><a href="#cb42-645" aria-hidden="true" tabindex="-1"></a>         <span class="at">adj_score_max =</span> <span class="fu">pmax</span>(adj_score_fwd, adj_score_rev))</span>
+<span id="cb42-646"><a href="#cb42-646" aria-hidden="true" tabindex="-1"></a><span class="co"># Import Bowtie2/Kraken data and perform filtering steps</span></span>
+<span id="cb42-647"><a href="#cb42-647" aria-hidden="true" tabindex="-1"></a>g_mrg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev)) <span class="sc">+</span></span>
+<span id="cb42-648"><a href="#cb42-648" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span> </span>
+<span id="cb42-649"><a href="#cb42-649" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-650"><a href="#cb42-650" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-651"><a href="#cb42-651" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb42-652"><a href="#cb42-652" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>)</span>
+<span id="cb42-653"><a href="#cb42-653" aria-hidden="true" tabindex="-1"></a>g_mrg</span>
+<span id="cb42-654"><a href="#cb42-654" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-655"><a href="#cb42-655" aria-hidden="true" tabindex="-1"></a>g_hist_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>,<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>kraken_label, <span class="at">scales=</span><span class="st">"free_y"</span>) <span class="sc">+</span> <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Dark2"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>)</span>
+<span id="cb42-656"><a href="#cb42-656" aria-hidden="true" tabindex="-1"></a>g_hist_0</span>
+<span id="cb42-657"><a href="#cb42-657" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-658"><a href="#cb42-658" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-659"><a href="#cb42-659" aria-hidden="true" tabindex="-1"></a>As previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I've used for previous datasets.</span>
+<span id="cb42-660"><a href="#cb42-660" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-663"><a href="#cb42-663" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-664"><a href="#cb42-664" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: make-blast-fasta</span></span>
+<span id="cb42-665"><a href="#cb42-665" aria-hidden="true" tabindex="-1"></a>mrg_fasta <span class="ot">&lt;-</span>  mrg <span class="sc">%&gt;%</span></span>
+<span id="cb42-666"><a href="#cb42-666" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">seq_head =</span> <span class="fu">paste0</span>(<span class="st">"&gt;"</span>, seq_id)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-667"><a href="#cb42-667" aria-hidden="true" tabindex="-1"></a>  ungroup <span class="sc">%&gt;%</span></span>
+<span id="cb42-668"><a href="#cb42-668" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="at">header1=</span>seq_head, <span class="at">seq1=</span>query_seq_fwd, </span>
+<span id="cb42-669"><a href="#cb42-669" aria-hidden="true" tabindex="-1"></a>         <span class="at">header2=</span>seq_head, <span class="at">seq2=</span>query_seq_rev) <span class="sc">%&gt;%</span></span>
+<span id="cb42-670"><a href="#cb42-670" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">header1=</span><span class="fu">paste0</span>(header1, <span class="st">"_1"</span>), <span class="at">header2=</span><span class="fu">paste0</span>(header2, <span class="st">"_2"</span>))</span>
+<span id="cb42-671"><a href="#cb42-671" aria-hidden="true" tabindex="-1"></a>mrg_fasta_out <span class="ot">&lt;-</span> <span class="fu">do.call</span>(paste, <span class="fu">c</span>(mrg_fasta, <span class="at">sep=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)) <span class="sc">%&gt;%</span> <span class="fu">paste</span>(<span class="at">collapse=</span><span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
+<span id="cb42-672"><a href="#cb42-672" aria-hidden="true" tabindex="-1"></a><span class="fu">write</span>(mrg_fasta_out, <span class="fu">file.path</span>(data_dir, <span class="st">"blast/putative-viral.fasta"</span>))</span>
+<span id="cb42-673"><a href="#cb42-673" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-674"><a href="#cb42-674" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-677"><a href="#cb42-677" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-678"><a href="#cb42-678" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: process-blast-data</span></span>
+<span id="cb42-679"><a href="#cb42-679" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb42-680"><a href="#cb42-680" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-681"><a href="#cb42-681" aria-hidden="true" tabindex="-1"></a><span class="co"># Import BLAST results</span></span>
+<span id="cb42-682"><a href="#cb42-682" aria-hidden="true" tabindex="-1"></a><span class="co"># Pre-filtering BLAST results to save space</span></span>
+<span id="cb42-683"><a href="#cb42-683" aria-hidden="true" tabindex="-1"></a><span class="co"># blast_results_path &lt;- file.path(data_dir, "blast/putative-viral.blast.gz")</span></span>
+<span id="cb42-684"><a href="#cb42-684" aria-hidden="true" tabindex="-1"></a><span class="co"># blast_cols &lt;- c("qseqid", "sseqid", "sgi", "staxid", "qlen", "evalue", "bitscore", "qcovs", "length", "pident", "mismatch", "gapopen", "sstrand", "qstart", "qend", "sstart", "send")</span></span>
+<span id="cb42-685"><a href="#cb42-685" aria-hidden="true" tabindex="-1"></a><span class="co"># blast_results &lt;- read_tsv(blast_results_path, show_col_types = FALSE,</span></span>
+<span id="cb42-686"><a href="#cb42-686" aria-hidden="true" tabindex="-1"></a><span class="co">#                           col_names = blast_cols, col_types = cols(.default="c"))</span></span>
+<span id="cb42-687"><a href="#cb42-687" aria-hidden="true" tabindex="-1"></a>blast_results_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"blast/putative-viral-best.blast.gz"</span>)</span>
+<span id="cb42-688"><a href="#cb42-688" aria-hidden="true" tabindex="-1"></a>blast_results <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(blast_results_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb42-689"><a href="#cb42-689" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-690"><a href="#cb42-690" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter for best hit for each query/subject combination</span></span>
+<span id="cb42-691"><a href="#cb42-691" aria-hidden="true" tabindex="-1"></a>blast_results_best <span class="ot">&lt;-</span> blast_results <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(qseqid, staxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-692"><a href="#cb42-692" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(bitscore <span class="sc">==</span> <span class="fu">max</span>(bitscore)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-693"><a href="#cb42-693" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(length <span class="sc">==</span> <span class="fu">max</span>(length)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>)</span>
+<span id="cb42-694"><a href="#cb42-694" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-695"><a href="#cb42-695" aria-hidden="true" tabindex="-1"></a><span class="co"># Rank hits for each query and filter for high-ranking hits</span></span>
+<span id="cb42-696"><a href="#cb42-696" aria-hidden="true" tabindex="-1"></a>blast_results_ranked <span class="ot">&lt;-</span> blast_results_best <span class="sc">%&gt;%</span> </span>
+<span id="cb42-697"><a href="#cb42-697" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(qseqid) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">rank =</span> <span class="fu">dense_rank</span>(<span class="fu">desc</span>(bitscore)))</span>
+<span id="cb42-698"><a href="#cb42-698" aria-hidden="true" tabindex="-1"></a>blast_results_highrank <span class="ot">&lt;-</span> blast_results_ranked <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">&lt;=</span> <span class="dv">5</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-699"><a href="#cb42-699" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">read_pair =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="sc">-</span><span class="dv">1</span>), </span>
+<span id="cb42-700"><a href="#cb42-700" aria-hidden="true" tabindex="-1"></a>         <span class="at">seq_id =</span> <span class="fu">str_split</span>(qseqid, <span class="st">"_"</span>) <span class="sc">%&gt;%</span> <span class="fu">sapply</span>(nth, <span class="at">n=</span><span class="dv">1</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-701"><a href="#cb42-701" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">bitscore =</span> <span class="fu">as.numeric</span>(bitscore))</span>
+<span id="cb42-702"><a href="#cb42-702" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-703"><a href="#cb42-703" aria-hidden="true" tabindex="-1"></a><span class="co"># Summarize by read pair and taxid</span></span>
+<span id="cb42-704"><a href="#cb42-704" aria-hidden="true" tabindex="-1"></a>blast_results_paired <span class="ot">&lt;-</span> blast_results_highrank <span class="sc">%&gt;%</span></span>
+<span id="cb42-705"><a href="#cb42-705" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id, staxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-706"><a href="#cb42-706" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">bitscore_max =</span> <span class="fu">max</span>(bitscore), <span class="at">bitscore_min =</span> <span class="fu">min</span>(bitscore),</span>
+<span id="cb42-707"><a href="#cb42-707" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads =</span> <span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb42-708"><a href="#cb42-708" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-709"><a href="#cb42-709" aria-hidden="true" tabindex="-1"></a><span class="co"># Add viral status</span></span>
+<span id="cb42-710"><a href="#cb42-710" aria-hidden="true" tabindex="-1"></a>blast_results_viral <span class="ot">&lt;-</span> <span class="fu">mutate</span>(blast_results_paired, <span class="at">viral =</span> staxid <span class="sc">%in%</span> viral_taxa<span class="sc">$</span>taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-711"><a href="#cb42-711" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_full =</span> viral <span class="sc">&amp;</span> n_reads <span class="sc">==</span> <span class="dv">2</span>)</span>
+<span id="cb42-712"><a href="#cb42-712" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-713"><a href="#cb42-713" aria-hidden="true" tabindex="-1"></a><span class="co"># Compare to Kraken &amp; Bowtie assignments</span></span>
+<span id="cb42-714"><a href="#cb42-714" aria-hidden="true" tabindex="-1"></a>mrg_assign <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, seq_id, taxid, assigned_taxid, adj_score_max, na_type)</span>
+<span id="cb42-715"><a href="#cb42-715" aria-hidden="true" tabindex="-1"></a>blast_results_assign <span class="ot">&lt;-</span> <span class="fu">left_join</span>(blast_results_viral, mrg_assign, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-716"><a href="#cb42-716" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">taxid_match_bowtie =</span> (staxid <span class="sc">==</span> taxid),</span>
+<span id="cb42-717"><a href="#cb42-717" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_kraken =</span> (staxid <span class="sc">==</span> assigned_taxid),</span>
+<span id="cb42-718"><a href="#cb42-718" aria-hidden="true" tabindex="-1"></a>           <span class="at">taxid_match_any =</span> taxid_match_bowtie <span class="sc">|</span> taxid_match_kraken)</span>
+<span id="cb42-719"><a href="#cb42-719" aria-hidden="true" tabindex="-1"></a>blast_results_out <span class="ot">&lt;-</span> blast_results_assign <span class="sc">%&gt;%</span></span>
+<span id="cb42-720"><a href="#cb42-720" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(seq_id) <span class="sc">%&gt;%</span></span>
+<span id="cb42-721"><a href="#cb42-721" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">viral_status =</span> <span class="fu">ifelse</span>(<span class="fu">any</span>(viral_full), <span class="dv">2</span>,</span>
+<span id="cb42-722"><a href="#cb42-722" aria-hidden="true" tabindex="-1"></a>                                  <span class="fu">ifelse</span>(<span class="fu">any</span>(taxid_match_any), <span class="dv">2</span>,</span>
+<span id="cb42-723"><a href="#cb42-723" aria-hidden="true" tabindex="-1"></a>                                             <span class="fu">ifelse</span>(<span class="fu">any</span>(viral), <span class="dv">1</span>, <span class="dv">0</span>))),</span>
+<span id="cb42-724"><a href="#cb42-724" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups =</span> <span class="st">"drop"</span>)</span>
+<span id="cb42-725"><a href="#cb42-725" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-726"><a href="#cb42-726" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-729"><a href="#cb42-729" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-730"><a href="#cb42-730" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-blast-results</span></span>
+<span id="cb42-731"><a href="#cb42-731" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb42-732"><a href="#cb42-732" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb42-733"><a href="#cb42-733" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-734"><a href="#cb42-734" aria-hidden="true" tabindex="-1"></a><span class="co"># Merge BLAST results with unenriched read data</span></span>
+<span id="cb42-735"><a href="#cb42-735" aria-hidden="true" tabindex="-1"></a>mrg_blast <span class="ot">&lt;-</span> <span class="fu">full_join</span>(mrg, blast_results_out, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-736"><a href="#cb42-736" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">viral_status =</span> <span class="fu">replace_na</span>(viral_status, <span class="dv">0</span>),</span>
+<span id="cb42-737"><a href="#cb42-737" aria-hidden="true" tabindex="-1"></a>         <span class="at">viral_status_out =</span> <span class="fu">ifelse</span>(viral_status <span class="sc">==</span> <span class="dv">0</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>))</span>
+<span id="cb42-738"><a href="#cb42-738" aria-hidden="true" tabindex="-1"></a>mrg_blast_rna <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(na_type <span class="sc">==</span> <span class="st">"RNA"</span>)</span>
+<span id="cb42-739"><a href="#cb42-739" aria-hidden="true" tabindex="-1"></a>mrg_blast_dna <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(na_type <span class="sc">==</span> <span class="st">"DNA"</span>)</span>
+<span id="cb42-740"><a href="#cb42-740" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-741"><a href="#cb42-741" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot RNA</span></span>
+<span id="cb42-742"><a href="#cb42-742" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_rna <span class="ot">&lt;-</span> mrg_blast_rna <span class="sc">%&gt;%</span></span>
+<span id="cb42-743"><a href="#cb42-743" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev, <span class="at">color=</span>viral_status_out)) <span class="sc">+</span></span>
+<span id="cb42-744"><a href="#cb42-744" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span></span>
+<span id="cb42-745"><a href="#cb42-745" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-746"><a href="#cb42-746" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-747"><a href="#cb42-747" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Viral status"</span>) <span class="sc">+</span></span>
+<span id="cb42-748"><a href="#cb42-748" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(viral_status_out<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb42-749"><a href="#cb42-749" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"RNA"</span>) <span class="sc">+</span> </span>
+<span id="cb42-750"><a href="#cb42-750" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>, <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="dv">2</span>), <span class="at">hjust=</span><span class="dv">0</span>))</span>
+<span id="cb42-751"><a href="#cb42-751" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_rna</span>
+<span id="cb42-752"><a href="#cb42-752" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-753"><a href="#cb42-753" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot DNA</span></span>
+<span id="cb42-754"><a href="#cb42-754" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_dna <span class="ot">&lt;-</span> mrg_blast_dna <span class="sc">%&gt;%</span></span>
+<span id="cb42-755"><a href="#cb42-755" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>adj_score_fwd, <span class="at">y=</span>adj_score_rev, <span class="at">color=</span>viral_status_out)) <span class="sc">+</span></span>
+<span id="cb42-756"><a href="#cb42-756" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">alpha=</span><span class="fl">0.5</span>, <span class="at">shape=</span><span class="dv">16</span>) <span class="sc">+</span></span>
+<span id="cb42-757"><a href="#cb42-757" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"S(forward read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-758"><a href="#cb42-758" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"S(reverse read)"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">65</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">10</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-759"><a href="#cb42-759" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette =</span> <span class="st">"Set1"</span>, <span class="at">name =</span> <span class="st">"Viral status"</span>) <span class="sc">+</span></span>
+<span id="cb42-760"><a href="#cb42-760" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(viral_status_out<span class="sc">~</span>kraken_label, <span class="at">labeller =</span> <span class="fu">labeller</span>(<span class="at">kit =</span> <span class="fu">label_wrap_gen</span>(<span class="dv">20</span>))) <span class="sc">+</span></span>
+<span id="cb42-761"><a href="#cb42-761" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">"DNA"</span>) <span class="sc">+</span> </span>
+<span id="cb42-762"><a href="#cb42-762" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">aspect.ratio=</span><span class="dv">1</span>, <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="dv">2</span>), <span class="at">hjust=</span><span class="dv">0</span>))</span>
+<span id="cb42-763"><a href="#cb42-763" aria-hidden="true" tabindex="-1"></a>g_mrg_blast_dna</span>
+<span id="cb42-764"><a href="#cb42-764" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-765"><a href="#cb42-765" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-768"><a href="#cb42-768" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-769"><a href="#cb42-769" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-blast-histogram</span></span>
+<span id="cb42-770"><a href="#cb42-770" aria-hidden="true" tabindex="-1"></a>g_hist_1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mrg_blast, <span class="fu">aes</span>(<span class="at">x=</span>adj_score_max)) <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="at">binwidth=</span><span class="dv">5</span>,<span class="at">boundary=</span><span class="dv">0</span>,<span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>viral_status_out, <span class="at">scales =</span> <span class="st">"free_y"</span>) <span class="sc">+</span> <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Maximum adjusted alignment score"</span>) <span class="sc">+</span> <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"# Read pairs"</span>) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">"Dark2"</span>) <span class="sc">+</span> theme_base <span class="sc">+</span> <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">20</span>, <span class="at">linetype=</span><span class="st">"dashed"</span>, <span class="at">color=</span><span class="st">"red"</span>)</span>
+<span id="cb42-771"><a href="#cb42-771" aria-hidden="true" tabindex="-1"></a>g_hist_1</span>
+<span id="cb42-772"><a href="#cb42-772" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-773"><a href="#cb42-773" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-774"><a href="#cb42-774" aria-hidden="true" tabindex="-1"></a>These results look good on visual inspection, and indeed precision and sensitivity are both very high. For a disjunctive score threshold of 20, my updated workflow achieves an F1 score of 96.7% for RNA sequences and 98.2% for DNA sequences.</span>
+<span id="cb42-775"><a href="#cb42-775" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-778"><a href="#cb42-778" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-779"><a href="#cb42-779" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-f1</span></span>
+<span id="cb42-780"><a href="#cb42-780" aria-hidden="true" tabindex="-1"></a>test_sens_spec <span class="ot">&lt;-</span> <span class="cf">function</span>(tab, score_threshold, conjunctive, include_special){</span>
+<span id="cb42-781"><a href="#cb42-781" aria-hidden="true" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span>include_special) tab <span class="ot">&lt;-</span> <span class="fu">filter</span>(tab, viral_status_out <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"TRUE"</span>, <span class="st">"FALSE"</span>))</span>
+<span id="cb42-782"><a href="#cb42-782" aria-hidden="true" tabindex="-1"></a>  tab_retained <span class="ot">&lt;-</span> tab <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(</span>
+<span id="cb42-783"><a href="#cb42-783" aria-hidden="true" tabindex="-1"></a>    <span class="at">conjunctive =</span> conjunctive,</span>
+<span id="cb42-784"><a href="#cb42-784" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score_conjunctive =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">&amp;</span> adj_score_rev <span class="sc">&gt;</span> score_threshold), </span>
+<span id="cb42-785"><a href="#cb42-785" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score_disjunctive =</span> (adj_score_fwd <span class="sc">&gt;</span> score_threshold <span class="sc">|</span> adj_score_rev <span class="sc">&gt;</span> score_threshold),</span>
+<span id="cb42-786"><a href="#cb42-786" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain_score =</span> <span class="fu">ifelse</span>(conjunctive, retain_score_conjunctive, retain_score_disjunctive),</span>
+<span id="cb42-787"><a href="#cb42-787" aria-hidden="true" tabindex="-1"></a>    <span class="at">retain =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> retain_score) <span class="sc">%&gt;%</span></span>
+<span id="cb42-788"><a href="#cb42-788" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(viral_status_out, retain) <span class="sc">%&gt;%</span> count</span>
+<span id="cb42-789"><a href="#cb42-789" aria-hidden="true" tabindex="-1"></a>  pos_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb42-790"><a href="#cb42-790" aria-hidden="true" tabindex="-1"></a>  pos_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb42-791"><a href="#cb42-791" aria-hidden="true" tabindex="-1"></a>  neg_tru <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">!=</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb42-792"><a href="#cb42-792" aria-hidden="true" tabindex="-1"></a>  neg_fls <span class="ot">&lt;-</span> tab_retained <span class="sc">%&gt;%</span> <span class="fu">filter</span>(viral_status_out <span class="sc">==</span> <span class="st">"TRUE"</span>, <span class="sc">!</span>retain) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(n) <span class="sc">%&gt;%</span> sum</span>
+<span id="cb42-793"><a href="#cb42-793" aria-hidden="true" tabindex="-1"></a>  sensitivity <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> neg_fls)</span>
+<span id="cb42-794"><a href="#cb42-794" aria-hidden="true" tabindex="-1"></a>  specificity <span class="ot">&lt;-</span> neg_tru <span class="sc">/</span> (neg_tru <span class="sc">+</span> pos_fls)</span>
+<span id="cb42-795"><a href="#cb42-795" aria-hidden="true" tabindex="-1"></a>  precision   <span class="ot">&lt;-</span> pos_tru <span class="sc">/</span> (pos_tru <span class="sc">+</span> pos_fls)</span>
+<span id="cb42-796"><a href="#cb42-796" aria-hidden="true" tabindex="-1"></a>  f1 <span class="ot">&lt;-</span> <span class="dv">2</span> <span class="sc">*</span> precision <span class="sc">*</span> sensitivity <span class="sc">/</span> (precision <span class="sc">+</span> sensitivity)</span>
+<span id="cb42-797"><a href="#cb42-797" aria-hidden="true" tabindex="-1"></a>  out <span class="ot">&lt;-</span> <span class="fu">tibble</span>(<span class="at">threshold=</span>score_threshold, <span class="at">include_special =</span> include_special, </span>
+<span id="cb42-798"><a href="#cb42-798" aria-hidden="true" tabindex="-1"></a>                <span class="at">conjunctive =</span> conjunctive, <span class="at">sensitivity=</span>sensitivity, </span>
+<span id="cb42-799"><a href="#cb42-799" aria-hidden="true" tabindex="-1"></a>                <span class="at">specificity=</span>specificity, <span class="at">precision=</span>precision, <span class="at">f1=</span>f1)</span>
+<span id="cb42-800"><a href="#cb42-800" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(out)</span>
+<span id="cb42-801"><a href="#cb42-801" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb42-802"><a href="#cb42-802" aria-hidden="true" tabindex="-1"></a>range_f1 <span class="ot">&lt;-</span> <span class="cf">function</span>(intab, inc_special, <span class="at">inrange=</span><span class="dv">15</span><span class="sc">:</span><span class="dv">45</span>){</span>
+<span id="cb42-803"><a href="#cb42-803" aria-hidden="true" tabindex="-1"></a>  tss <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">partial</span>(test_sens_spec, <span class="at">tab=</span>intab, <span class="at">include_special=</span>inc_special)</span>
+<span id="cb42-804"><a href="#cb42-804" aria-hidden="true" tabindex="-1"></a>  stats_conj <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss, <span class="at">conjunctive=</span><span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb42-805"><a href="#cb42-805" aria-hidden="true" tabindex="-1"></a>  stats_disj <span class="ot">&lt;-</span> <span class="fu">lapply</span>(inrange, tss, <span class="at">conjunctive=</span><span class="cn">FALSE</span>) <span class="sc">%&gt;%</span> bind_rows</span>
+<span id="cb42-806"><a href="#cb42-806" aria-hidden="true" tabindex="-1"></a>  stats_all <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(stats_conj, stats_disj) <span class="sc">%&gt;%</span></span>
+<span id="cb42-807"><a href="#cb42-807" aria-hidden="true" tabindex="-1"></a>    <span class="fu">pivot_longer</span>(<span class="sc">!</span>(threshold<span class="sc">:</span>conjunctive), <span class="at">names_to=</span><span class="st">"metric"</span>, <span class="at">values_to=</span><span class="st">"value"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-808"><a href="#cb42-808" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">conj_label =</span> <span class="fu">ifelse</span>(conjunctive, <span class="st">"Conjunctive"</span>, <span class="st">"Disjunctive"</span>))</span>
+<span id="cb42-809"><a href="#cb42-809" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(stats_all)</span>
+<span id="cb42-810"><a href="#cb42-810" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb42-811"><a href="#cb42-811" aria-hidden="true" tabindex="-1"></a>inc_special <span class="ot">&lt;-</span> <span class="cn">FALSE</span></span>
+<span id="cb42-812"><a href="#cb42-812" aria-hidden="true" tabindex="-1"></a>stats_rna <span class="ot">&lt;-</span> <span class="fu">range_f1</span>(mrg_blast_rna, inc_special) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">na_type =</span> <span class="st">"RNA"</span>)</span>
+<span id="cb42-813"><a href="#cb42-813" aria-hidden="true" tabindex="-1"></a>stats_dna <span class="ot">&lt;-</span> <span class="fu">range_f1</span>(mrg_blast_dna, inc_special) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">na_type =</span> <span class="st">"DNA"</span>)</span>
+<span id="cb42-814"><a href="#cb42-814" aria-hidden="true" tabindex="-1"></a>stats_0 <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(stats_rna, stats_dna) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">attempt=</span><span class="dv">0</span>)</span>
+<span id="cb42-815"><a href="#cb42-815" aria-hidden="true" tabindex="-1"></a>threshold_opt_0 <span class="ot">&lt;-</span> stats_0 <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(conj_label,attempt,na_type) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-816"><a href="#cb42-816" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(metric <span class="sc">==</span> <span class="st">"f1"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-817"><a href="#cb42-817" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="fu">max</span>(value)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(threshold <span class="sc">==</span> <span class="fu">min</span>(threshold))</span>
+<span id="cb42-818"><a href="#cb42-818" aria-hidden="true" tabindex="-1"></a>g_stats_0 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(stats_0, <span class="fu">aes</span>(<span class="at">x=</span>threshold, <span class="at">y=</span>value, <span class="at">color=</span>metric)) <span class="sc">+</span></span>
+<span id="cb42-819"><a href="#cb42-819" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">data =</span> threshold_opt_0, <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">xintercept=</span>threshold),</span>
+<span id="cb42-820"><a href="#cb42-820" aria-hidden="true" tabindex="-1"></a>             <span class="at">color =</span> <span class="st">"red"</span>, <span class="at">linetype =</span> <span class="st">"dashed"</span>) <span class="sc">+</span></span>
+<span id="cb42-821"><a href="#cb42-821" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
+<span id="cb42-822"><a href="#cb42-822" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"Value"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-823"><a href="#cb42-823" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name =</span> <span class="st">"Threshold"</span>, <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-824"><a href="#cb42-824" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_brewer</span>(<span class="at">palette=</span><span class="st">"Set3"</span>) <span class="sc">+</span></span>
+<span id="cb42-825"><a href="#cb42-825" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>conj_label) <span class="sc">+</span></span>
+<span id="cb42-826"><a href="#cb42-826" aria-hidden="true" tabindex="-1"></a>  theme_base</span>
+<span id="cb42-827"><a href="#cb42-827" aria-hidden="true" tabindex="-1"></a>g_stats_0</span>
+<span id="cb42-828"><a href="#cb42-828" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-829"><a href="#cb42-829" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-830"><a href="#cb42-830" aria-hidden="true" tabindex="-1"></a>Looking into the composition of different read groups, nothing stands out except the predominance of HCMV (human betaherpesvirus 5), which is consistent with the Kraken results above and borne out by the BLASTN alignments:</span>
+<span id="cb42-831"><a href="#cb42-831" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-834"><a href="#cb42-834" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-835"><a href="#cb42-835" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fp</span></span>
+<span id="cb42-836"><a href="#cb42-836" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb42-837"><a href="#cb42-837" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-838"><a href="#cb42-838" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">211787</span>] <span class="ot">&lt;-</span> <span class="st">"Human papillomavirus type 92"</span></span>
+<span id="cb42-839"><a href="#cb42-839" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">509154</span>] <span class="ot">&lt;-</span> <span class="st">"Porcine endogenous retrovirus C"</span></span>
+<span id="cb42-840"><a href="#cb42-840" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-841"><a href="#cb42-841" aria-hidden="true" tabindex="-1"></a>major_threshold <span class="ot">&lt;-</span> <span class="fl">0.05</span></span>
+<span id="cb42-842"><a href="#cb42-842" aria-hidden="true" tabindex="-1"></a>fp <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> </span>
+<span id="cb42-843"><a href="#cb42-843" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(na_type, viral_status_out, </span>
+<span id="cb42-844"><a href="#cb42-844" aria-hidden="true" tabindex="-1"></a>           <span class="at">highscore =</span> adj_score_max <span class="sc">&gt;=</span> <span class="dv">20</span>, taxid) <span class="sc">%&gt;%</span> count <span class="sc">%&gt;%</span> </span>
+<span id="cb42-845"><a href="#cb42-845" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(na_type, viral_status_out, highscore) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">p=</span>n<span class="sc">/</span><span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-846"><a href="#cb42-846" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"taxid"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-847"><a href="#cb42-847" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-848"><a href="#cb42-848" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name =</span> <span class="fu">ifelse</span>(taxid <span class="sc">==</span> <span class="dv">194958</span>, <span class="st">"Porcine endogenous retrovirus A"</span>, name))</span>
+<span id="cb42-849"><a href="#cb42-849" aria-hidden="true" tabindex="-1"></a>fp_major_tab <span class="ot">&lt;-</span> fp <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p <span class="sc">&gt;</span> major_threshold) <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(<span class="fu">desc</span>(p))</span>
+<span id="cb42-850"><a href="#cb42-850" aria-hidden="true" tabindex="-1"></a>fp_major_list <span class="ot">&lt;-</span> fp_major_tab <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name) <span class="sc">%&gt;%</span> sort <span class="sc">%&gt;%</span> unique <span class="sc">%&gt;%</span> <span class="fu">c</span>(., <span class="st">"Other"</span>)</span>
+<span id="cb42-851"><a href="#cb42-851" aria-hidden="true" tabindex="-1"></a>fp_major <span class="ot">&lt;-</span> fp <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">major =</span> p <span class="sc">&gt;</span> major_threshold) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-852"><a href="#cb42-852" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(major, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-853"><a href="#cb42-853" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(na_type, viral_status_out, highscore, name_display) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-854"><a href="#cb42-854" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n=</span><span class="fu">sum</span>(n), <span class="at">p=</span><span class="fu">sum</span>(p), <span class="at">.groups =</span> <span class="st">"drop"</span>)  <span class="sc">%&gt;%</span></span>
+<span id="cb42-855"><a href="#cb42-855" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, <span class="at">levels =</span> fp_major_list),</span>
+<span id="cb42-856"><a href="#cb42-856" aria-hidden="true" tabindex="-1"></a>         <span class="at">score_display =</span> <span class="fu">ifelse</span>(highscore, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span>),</span>
+<span id="cb42-857"><a href="#cb42-857" aria-hidden="true" tabindex="-1"></a>         <span class="at">status_display =</span> <span class="fu">ifelse</span>(viral_status_out, <span class="st">"True positive"</span>, <span class="st">"False positive"</span>))</span>
+<span id="cb42-858"><a href="#cb42-858" aria-hidden="true" tabindex="-1"></a>g_fp <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(fp_major, <span class="fu">aes</span>(<span class="at">x=</span>score_display, <span class="at">y=</span>p, <span class="at">fill=</span>name_display)) <span class="sc">+</span></span>
+<span id="cb42-859"><a href="#cb42-859" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position=</span><span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-860"><a href="#cb42-860" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">name =</span> <span class="st">"True positive?"</span>) <span class="sc">+</span></span>
+<span id="cb42-861"><a href="#cb42-861" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name =</span> <span class="st">"% reads"</span>, <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb42-862"><a href="#cb42-862" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-863"><a href="#cb42-863" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> palette_viral, <span class="at">name =</span> <span class="st">"Viral</span><span class="sc">\n</span><span class="st">taxon"</span>) <span class="sc">+</span></span>
+<span id="cb42-864"><a href="#cb42-864" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>status_display) <span class="sc">+</span></span>
+<span id="cb42-865"><a href="#cb42-865" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">fill=</span><span class="fu">guide_legend</span>(<span class="at">ncol=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
+<span id="cb42-866"><a href="#cb42-866" aria-hidden="true" tabindex="-1"></a>  theme_kit</span>
+<span id="cb42-867"><a href="#cb42-867" aria-hidden="true" tabindex="-1"></a>g_fp</span>
+<span id="cb42-868"><a href="#cb42-868" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-869"><a href="#cb42-869" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-870"><a href="#cb42-870" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-873"><a href="#cb42-873" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-874"><a href="#cb42-874" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hcmv-blast-hits</span></span>
+<span id="cb42-875"><a href="#cb42-875" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-876"><a href="#cb42-876" aria-hidden="true" tabindex="-1"></a><span class="co"># Configure</span></span>
+<span id="cb42-877"><a href="#cb42-877" aria-hidden="true" tabindex="-1"></a>ref_taxid <span class="ot">&lt;-</span> <span class="dv">10359</span></span>
+<span id="cb42-878"><a href="#cb42-878" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-879"><a href="#cb42-879" aria-hidden="true" tabindex="-1"></a><span class="co"># Get taxon names</span></span>
+<span id="cb42-880"><a href="#cb42-880" aria-hidden="true" tabindex="-1"></a>tax_names_path <span class="ot">&lt;-</span> <span class="fu">file.path</span>(data_dir, <span class="st">"taxid-names.tsv.gz"</span>)</span>
+<span id="cb42-881"><a href="#cb42-881" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">read_tsv</span>(tax_names_path, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb42-882"><a href="#cb42-882" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-883"><a href="#cb42-883" aria-hidden="true" tabindex="-1"></a><span class="co"># Add missing names</span></span>
+<span id="cb42-884"><a href="#cb42-884" aria-hidden="true" tabindex="-1"></a>tax_names_new <span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>staxid, <span class="sc">~</span>name,</span>
+<span id="cb42-885"><a href="#cb42-885" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">3050295</span>, <span class="st">"Cytomegalovirus humanbeta5"</span>,</span>
+<span id="cb42-886"><a href="#cb42-886" aria-hidden="true" tabindex="-1"></a>                         <span class="dv">459231</span>, <span class="st">"FLAG-tagging vector pFLAG97-TSR"</span>)</span>
+<span id="cb42-887"><a href="#cb42-887" aria-hidden="true" tabindex="-1"></a>tax_names <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(tax_names, tax_names_new)</span>
+<span id="cb42-888"><a href="#cb42-888" aria-hidden="true" tabindex="-1"></a>ref_name <span class="ot">&lt;-</span> tax_names <span class="sc">%&gt;%</span> <span class="fu">filter</span>(staxid <span class="sc">==</span> ref_taxid) <span class="sc">%&gt;%</span> <span class="fu">pull</span>(name)</span>
+<span id="cb42-889"><a href="#cb42-889" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-890"><a href="#cb42-890" aria-hidden="true" tabindex="-1"></a><span class="co"># Get major matches</span></span>
+<span id="cb42-891"><a href="#cb42-891" aria-hidden="true" tabindex="-1"></a>fp_staxid <span class="ot">&lt;-</span> mrg_blast <span class="sc">%&gt;%</span> <span class="fu">filter</span>(taxid <span class="sc">==</span> ref_taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-892"><a href="#cb42-892" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(na_type, <span class="at">highscore =</span> adj_score_max <span class="sc">&gt;=</span> <span class="dv">20</span>) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">n_seq =</span> <span class="fu">n</span>()) <span class="sc">%&gt;%</span></span>
+<span id="cb42-893"><a href="#cb42-893" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(blast_results_paired, <span class="at">by=</span><span class="st">"seq_id"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-894"><a href="#cb42-894" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">staxid =</span> <span class="fu">as.integer</span>(staxid)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-895"><a href="#cb42-895" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names, <span class="at">by=</span><span class="st">"staxid"</span>) <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">sname=</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb42-896"><a href="#cb42-896" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(tax_names <span class="sc">%&gt;%</span> <span class="fu">rename</span>(<span class="at">taxid=</span>staxid), <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb42-897"><a href="#cb42-897" aria-hidden="true" tabindex="-1"></a>fp_staxid_count <span class="ot">&lt;-</span> fp_staxid <span class="sc">%&gt;%</span></span>
+<span id="cb42-898"><a href="#cb42-898" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore, na_type, </span>
+<span id="cb42-899"><a href="#cb42-899" aria-hidden="true" tabindex="-1"></a>           taxid, name, staxid, sname, n_seq) <span class="sc">%&gt;%</span></span>
+<span id="cb42-900"><a href="#cb42-900" aria-hidden="true" tabindex="-1"></a>  count <span class="sc">%&gt;%</span></span>
+<span id="cb42-901"><a href="#cb42-901" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(viral_status_out, highscore, na_type, taxid, name) <span class="sc">%&gt;%</span></span>
+<span id="cb42-902"><a href="#cb42-902" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p=</span>n<span class="sc">/</span>n_seq)</span>
+<span id="cb42-903"><a href="#cb42-903" aria-hidden="true" tabindex="-1"></a>fp_staxid_count_major <span class="ot">&lt;-</span> fp_staxid_count <span class="sc">%&gt;%</span></span>
+<span id="cb42-904"><a href="#cb42-904" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(n<span class="sc">&gt;</span><span class="dv">1</span>, p<span class="sc">&gt;</span><span class="fl">0.1</span>, <span class="sc">!</span><span class="fu">is.na</span>(staxid)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-905"><a href="#cb42-905" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">score_display =</span> <span class="fu">ifelse</span>(highscore, <span class="st">"S &gt;= 20"</span>, <span class="st">"S &lt; 20"</span>),</span>
+<span id="cb42-906"><a href="#cb42-906" aria-hidden="true" tabindex="-1"></a>         <span class="at">status_display =</span> <span class="fu">ifelse</span>(viral_status_out, </span>
+<span id="cb42-907"><a href="#cb42-907" aria-hidden="true" tabindex="-1"></a>                                 <span class="st">"True positive"</span>, <span class="st">"False positive"</span>))</span>
+<span id="cb42-908"><a href="#cb42-908" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-909"><a href="#cb42-909" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb42-910"><a href="#cb42-910" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(fp_staxid_count_major, <span class="fu">aes</span>(<span class="at">x=</span>p, <span class="at">y=</span>sname)) <span class="sc">+</span> </span>
+<span id="cb42-911"><a href="#cb42-911" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>() <span class="sc">+</span> </span>
+<span id="cb42-912"><a href="#cb42-912" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(na_type<span class="sc">~</span>status_display<span class="sc">+</span>score_display, <span class="at">scales=</span><span class="st">"free"</span>,</span>
+<span id="cb42-913"><a href="#cb42-913" aria-hidden="true" tabindex="-1"></a>             <span class="at">labeller =</span> <span class="fu">label_wrap_gen</span>(<span class="at">multi_line =</span> <span class="cn">FALSE</span>)) <span class="sc">+</span></span>
+<span id="cb42-914"><a href="#cb42-914" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">name=</span><span class="st">"% mapped reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">breaks=</span><span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb42-915"><a href="#cb42-915" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>)) <span class="sc">+</span></span>
+<span id="cb42-916"><a href="#cb42-916" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="fu">paste0</span>(ref_name, <span class="st">" (taxid "</span>, ref_taxid, <span class="st">")"</span>)) <span class="sc">+</span></span>
+<span id="cb42-917"><a href="#cb42-917" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(</span>
+<span id="cb42-918"><a href="#cb42-918" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.title.y =</span> <span class="fu">element_blank</span>(),</span>
+<span id="cb42-919"><a href="#cb42-919" aria-hidden="true" tabindex="-1"></a>    <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="fu">rel</span>(<span class="fl">1.5</span>), <span class="at">hjust=</span><span class="dv">0</span>, <span class="at">face=</span><span class="st">"plain"</span>))</span>
+<span id="cb42-920"><a href="#cb42-920" aria-hidden="true" tabindex="-1"></a>g</span>
+<span id="cb42-921"><a href="#cb42-921" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-922"><a href="#cb42-922" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-923"><a href="#cb42-923" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting viruses: overall relative abundance</span></span>
+<span id="cb42-924"><a href="#cb42-924" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-927"><a href="#cb42-927" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-928"><a href="#cb42-928" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: count-hv-reads</span></span>
+<span id="cb42-929"><a href="#cb42-929" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-930"><a href="#cb42-930" aria-hidden="true" tabindex="-1"></a><span class="co"># Get raw read counts</span></span>
+<span id="cb42-931"><a href="#cb42-931" aria-hidden="true" tabindex="-1"></a>read_counts_raw <span class="ot">&lt;-</span> basic_stats_raw <span class="sc">%&gt;%</span></span>
+<span id="cb42-932"><a href="#cb42-932" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sample, date, na_type, ctrl, <span class="at">n_reads_raw =</span> n_read_pairs)</span>
+<span id="cb42-933"><a href="#cb42-933" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-934"><a href="#cb42-934" aria-hidden="true" tabindex="-1"></a><span class="co"># Get HV read counts</span></span>
+<span id="cb42-935"><a href="#cb42-935" aria-hidden="true" tabindex="-1"></a>mrg_hv <span class="ot">&lt;-</span> mrg <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">hv_status =</span> assigned_hv <span class="sc">|</span> hit_hv <span class="sc">|</span> adj_score_max <span class="sc">&gt;=</span> <span class="dv">20</span>)</span>
+<span id="cb42-936"><a href="#cb42-936" aria-hidden="true" tabindex="-1"></a>read_counts_hv <span class="ot">&lt;-</span> mrg_hv <span class="sc">%&gt;%</span> <span class="fu">filter</span>(hv_status) <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(sample) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-937"><a href="#cb42-937" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name=</span><span class="st">"n_reads_hv"</span>)</span>
+<span id="cb42-938"><a href="#cb42-938" aria-hidden="true" tabindex="-1"></a>read_counts <span class="ot">&lt;-</span> read_counts_raw <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(read_counts_hv, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-939"><a href="#cb42-939" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">n_reads_hv =</span> <span class="fu">replace_na</span>(n_reads_hv, <span class="dv">0</span>))</span>
+<span id="cb42-940"><a href="#cb42-940" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-941"><a href="#cb42-941" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate</span></span>
+<span id="cb42-942"><a href="#cb42-942" aria-hidden="true" tabindex="-1"></a>read_counts_total <span class="ot">&lt;-</span> read_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(na_type, ctrl) <span class="sc">%&gt;%</span></span>
+<span id="cb42-943"><a href="#cb42-943" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_raw =</span> <span class="fu">sum</span>(n_reads_raw),</span>
+<span id="cb42-944"><a href="#cb42-944" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-945"><a href="#cb42-945" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample=</span> <span class="st">"All samples"</span>, <span class="at">date =</span> <span class="st">"All dates"</span>)</span>
+<span id="cb42-946"><a href="#cb42-946" aria-hidden="true" tabindex="-1"></a>read_counts_agg <span class="ot">&lt;-</span> read_counts <span class="sc">%&gt;%</span> <span class="fu">arrange</span>(date) <span class="sc">%&gt;%</span></span>
+<span id="cb42-947"><a href="#cb42-947" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(sample) <span class="sc">%&gt;%</span></span>
+<span id="cb42-948"><a href="#cb42-948" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bind_rows</span>(read_counts_total) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-949"><a href="#cb42-949" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> <span class="fu">fct_inorder</span>(sample),</span>
+<span id="cb42-950"><a href="#cb42-950" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span>n_reads_raw)</span>
+<span id="cb42-951"><a href="#cb42-951" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-952"><a href="#cb42-952" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-953"><a href="#cb42-953" aria-hidden="true" tabindex="-1"></a>In non-control samples, applying a disjunctive cutoff at S=20 identifies 2591 RNA reads and 12236 DNA reads as human-viral. This gives an overall relative HV abundance of $1.63 \times 10^{-5}$ for RNA reads and $4.16 \times 10^{-5}$ for DNA reads. Reassuringly, relative abundances in control samples are at least 10x lower. We also see a sharp drop in relative abundance for both nucleic-acid types for the period when the daycare was closed (2015-01-20):</span>
+<span id="cb42-954"><a href="#cb42-954" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-957"><a href="#cb42-957" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-958"><a href="#cb42-958" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: plot-hv-ra</span></span>
+<span id="cb42-959"><a href="#cb42-959" aria-hidden="true" tabindex="-1"></a><span class="co">#| warning: false</span></span>
+<span id="cb42-960"><a href="#cb42-960" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualize</span></span>
+<span id="cb42-961"><a href="#cb42-961" aria-hidden="true" tabindex="-1"></a>g_phv_agg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(read_counts_agg, <span class="fu">aes</span>(<span class="at">x=</span>date, <span class="at">color=</span>na_type)) <span class="sc">+</span></span>
+<span id="cb42-962"><a href="#cb42-962" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">y=</span>p_reads_hv)) <span class="sc">+</span></span>
+<span id="cb42-963"><a href="#cb42-963" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_log10</span>(<span class="st">"Relative abundance of human virus reads"</span>) <span class="sc">+</span></span>
+<span id="cb42-964"><a href="#cb42-964" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_discrete</span>(<span class="at">name=</span><span class="st">"Collection Date"</span>) <span class="sc">+</span></span>
+<span id="cb42-965"><a href="#cb42-965" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(.<span class="sc">~</span>ctrl, <span class="at">scales =</span> <span class="st">"free"</span>, <span class="at">space =</span> <span class="st">"free_x"</span>) <span class="sc">+</span></span>
+<span id="cb42-966"><a href="#cb42-966" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span> theme_rotate</span>
+<span id="cb42-967"><a href="#cb42-967" aria-hidden="true" tabindex="-1"></a>g_phv_agg</span>
+<span id="cb42-968"><a href="#cb42-968" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-969"><a href="#cb42-969" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-970"><a href="#cb42-970" aria-hidden="true" tabindex="-1"></a>These overall RA values are similar to those we've seen previously for non-panel-enriched wastewater RNA data. That said, it's notable that the DNA read RA seen here is much higher than that seen in the only DNA wastewater dataset I've analyzed so far (Brumfield):</span>
+<span id="cb42-971"><a href="#cb42-971" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-974"><a href="#cb42-974" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-975"><a href="#cb42-975" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: ra-hv-past</span></span>
+<span id="cb42-976"><a href="#cb42-976" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-977"><a href="#cb42-977" aria-hidden="true" tabindex="-1"></a><span class="co"># Collate past RA values</span></span>
+<span id="cb42-978"><a href="#cb42-978" aria-hidden="true" tabindex="-1"></a>ra_past <span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>dataset, <span class="sc">~</span>ra, <span class="sc">~</span>na_type, <span class="sc">~</span>panel_enriched,</span>
+<span id="cb42-979"><a href="#cb42-979" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Brumfield"</span>, <span class="fl">5e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb42-980"><a href="#cb42-980" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Brumfield"</span>, <span class="fl">3.66e-7</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb42-981"><a href="#cb42-981" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Spurbeck"</span>, <span class="fl">5.44e-6</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb42-982"><a href="#cb42-982" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Yang"</span>, <span class="fl">3.62e-4</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb42-983"><a href="#cb42-983" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Rothman (unenriched)"</span>, <span class="fl">1.87e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb42-984"><a href="#cb42-984" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Rothman (panel-enriched)"</span>, <span class="fl">3.3e-5</span>, <span class="st">"RNA"</span>, <span class="cn">TRUE</span>,</span>
+<span id="cb42-985"><a href="#cb42-985" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Crits-Christoph (unenriched)"</span>, <span class="fl">1.37e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb42-986"><a href="#cb42-986" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Crits-Christoph (panel-enriched)"</span>, <span class="fl">1.26e-2</span>, <span class="st">"RNA"</span>, <span class="cn">TRUE</span>)</span>
+<span id="cb42-987"><a href="#cb42-987" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-988"><a href="#cb42-988" aria-hidden="true" tabindex="-1"></a><span class="co"># Collate new RA values</span></span>
+<span id="cb42-989"><a href="#cb42-989" aria-hidden="true" tabindex="-1"></a>ra_new <span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>dataset, <span class="sc">~</span>ra, <span class="sc">~</span>na_type, <span class="sc">~</span>panel_enriched,</span>
+<span id="cb42-990"><a href="#cb42-990" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Prussin (non-control)"</span>, <span class="fl">1.63e-5</span>, <span class="st">"RNA"</span>, <span class="cn">FALSE</span>,</span>
+<span id="cb42-991"><a href="#cb42-991" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"Prussin (non-control)"</span>, <span class="fl">4.16e-5</span>, <span class="st">"DNA"</span>, <span class="cn">FALSE</span>)</span>
+<span id="cb42-992"><a href="#cb42-992" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-993"><a href="#cb42-993" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb42-994"><a href="#cb42-994" aria-hidden="true" tabindex="-1"></a>ra_comp <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(ra_past, ra_new) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">dataset =</span> <span class="fu">fct_inorder</span>(dataset))</span>
+<span id="cb42-995"><a href="#cb42-995" aria-hidden="true" tabindex="-1"></a>g_ra_comp <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(ra_comp, <span class="fu">aes</span>(<span class="at">y=</span>dataset, <span class="at">x=</span>ra, <span class="at">color=</span>na_type)) <span class="sc">+</span></span>
+<span id="cb42-996"><a href="#cb42-996" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>() <span class="sc">+</span></span>
+<span id="cb42-997"><a href="#cb42-997" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span></span>
+<span id="cb42-998"><a href="#cb42-998" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_log10</span>(<span class="at">name=</span><span class="st">"Relative abundance of human virus reads"</span>) <span class="sc">+</span></span>
+<span id="cb42-999"><a href="#cb42-999" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
+<span id="cb42-1000"><a href="#cb42-1000" aria-hidden="true" tabindex="-1"></a>g_ra_comp</span>
+<span id="cb42-1001"><a href="#cb42-1001" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-1002"><a href="#cb42-1002" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1003"><a href="#cb42-1003" aria-hidden="true" tabindex="-1"></a><span class="fu"># Human-infecting viruses: taxonomy and composition</span></span>
+<span id="cb42-1004"><a href="#cb42-1004" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1005"><a href="#cb42-1005" aria-hidden="true" tabindex="-1"></a>At the family level, we see that *Papillomaviridae*, *Herpesviridae*, and *Polyomaviridae* are the most abundant families in both DNA and RNA reads, with *Adenoviridae* and (in RNA reads) *Picornaviridae* also making a respectable showing. The *Herpesviridae* reads are, predictably, overwhelmingly from HCMV, while the *Papillomaviridae* and *Polyomaviridae* reads are split up among a larger number of related viruses:</span>
+<span id="cb42-1006"><a href="#cb42-1006" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1009"><a href="#cb42-1009" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-1010"><a href="#cb42-1010" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: raise-hv-taxa</span></span>
+<span id="cb42-1011"><a href="#cb42-1011" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1012"><a href="#cb42-1012" aria-hidden="true" tabindex="-1"></a><span class="co"># Get viral taxon names for putative HV reads</span></span>
+<span id="cb42-1013"><a href="#cb42-1013" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">249588</span>] <span class="ot">&lt;-</span> <span class="st">"Mamastrovirus"</span></span>
+<span id="cb42-1014"><a href="#cb42-1014" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">194960</span>] <span class="ot">&lt;-</span> <span class="st">"Kobuvirus"</span></span>
+<span id="cb42-1015"><a href="#cb42-1015" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">688449</span>] <span class="ot">&lt;-</span> <span class="st">"Salivirus"</span></span>
+<span id="cb42-1016"><a href="#cb42-1016" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">585893</span>] <span class="ot">&lt;-</span> <span class="st">"Picobirnaviridae"</span></span>
+<span id="cb42-1017"><a href="#cb42-1017" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333922</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus"</span></span>
+<span id="cb42-1018"><a href="#cb42-1018" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334207</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 3"</span></span>
+<span id="cb42-1019"><a href="#cb42-1019" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">369960</span>] <span class="ot">&lt;-</span> <span class="st">"Porcine type-C oncovirus"</span></span>
+<span id="cb42-1020"><a href="#cb42-1020" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333924</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 2"</span></span>
+<span id="cb42-1021"><a href="#cb42-1021" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">687329</span>] <span class="ot">&lt;-</span> <span class="st">"Anelloviridae"</span></span>
+<span id="cb42-1022"><a href="#cb42-1022" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">325455</span>] <span class="ot">&lt;-</span> <span class="st">"Gammapapillomavirus"</span></span>
+<span id="cb42-1023"><a href="#cb42-1023" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333750</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus"</span></span>
+<span id="cb42-1024"><a href="#cb42-1024" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">694002</span>] <span class="ot">&lt;-</span> <span class="st">"Betacoronavirus"</span></span>
+<span id="cb42-1025"><a href="#cb42-1025" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334202</span>] <span class="ot">&lt;-</span> <span class="st">"Mupapillomavirus"</span></span>
+<span id="cb42-1026"><a href="#cb42-1026" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">197911</span>] <span class="ot">&lt;-</span> <span class="st">"Alphainfluenzavirus"</span></span>
+<span id="cb42-1027"><a href="#cb42-1027" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">186938</span>] <span class="ot">&lt;-</span> <span class="st">"Respirovirus"</span></span>
+<span id="cb42-1028"><a href="#cb42-1028" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">333926</span>] <span class="ot">&lt;-</span> <span class="st">"Gammapapillomavirus 1"</span></span>
+<span id="cb42-1029"><a href="#cb42-1029" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">337051</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 1"</span></span>
+<span id="cb42-1030"><a href="#cb42-1030" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">337043</span>] <span class="ot">&lt;-</span> <span class="st">"Alphapapillomavirus 4"</span></span>
+<span id="cb42-1031"><a href="#cb42-1031" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">694003</span>] <span class="ot">&lt;-</span> <span class="st">"Betacoronavirus 1"</span></span>
+<span id="cb42-1032"><a href="#cb42-1032" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334204</span>] <span class="ot">&lt;-</span> <span class="st">"Mupapillomavirus 2"</span></span>
+<span id="cb42-1033"><a href="#cb42-1033" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334208</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span id="cb42-1034"><a href="#cb42-1034" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1035"><a href="#cb42-1035" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334208</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span id="cb42-1036"><a href="#cb42-1036" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334208</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span id="cb42-1037"><a href="#cb42-1037" aria-hidden="true" tabindex="-1"></a>viral_taxa<span class="sc">$</span>name[viral_taxa<span class="sc">$</span>taxid <span class="sc">==</span> <span class="dv">334208</span>] <span class="ot">&lt;-</span> <span class="st">"Betapapillomavirus 4"</span></span>
+<span id="cb42-1038"><a href="#cb42-1038" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1039"><a href="#cb42-1039" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1040"><a href="#cb42-1040" aria-hidden="true" tabindex="-1"></a>mrg_hv_named <span class="ot">&lt;-</span> mrg_hv <span class="sc">%&gt;%</span> <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb42-1041"><a href="#cb42-1041" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1042"><a href="#cb42-1042" aria-hidden="true" tabindex="-1"></a><span class="co"># Discover viral species &amp; genera for HV reads</span></span>
+<span id="cb42-1043"><a href="#cb42-1043" aria-hidden="true" tabindex="-1"></a>raise_rank <span class="ot">&lt;-</span> <span class="cf">function</span>(read_db, taxid_db, <span class="at">out_rank =</span> <span class="st">"species"</span>, <span class="at">verbose =</span> <span class="cn">FALSE</span>){</span>
+<span id="cb42-1044"><a href="#cb42-1044" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Get higher ranks than search rank</span></span>
+<span id="cb42-1045"><a href="#cb42-1045" aria-hidden="true" tabindex="-1"></a>  ranks <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"subspecies"</span>, <span class="st">"species"</span>, <span class="st">"subgenus"</span>, <span class="st">"genus"</span>, <span class="st">"subfamily"</span>, <span class="st">"family"</span>, <span class="st">"suborder"</span>, <span class="st">"order"</span>, <span class="st">"class"</span>, <span class="st">"subphylum"</span>, <span class="st">"phylum"</span>, <span class="st">"kingdom"</span>, <span class="st">"superkingdom"</span>)</span>
+<span id="cb42-1046"><a href="#cb42-1046" aria-hidden="true" tabindex="-1"></a>  rank_match <span class="ot">&lt;-</span> <span class="fu">which.max</span>(ranks <span class="sc">==</span> out_rank)</span>
+<span id="cb42-1047"><a href="#cb42-1047" aria-hidden="true" tabindex="-1"></a>  high_ranks <span class="ot">&lt;-</span> ranks[rank_match<span class="sc">:</span><span class="fu">length</span>(ranks)]</span>
+<span id="cb42-1048"><a href="#cb42-1048" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Merge read DB and taxid DB</span></span>
+<span id="cb42-1049"><a href="#cb42-1049" aria-hidden="true" tabindex="-1"></a>  reads <span class="ot">&lt;-</span> read_db <span class="sc">%&gt;%</span> <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1050"><a href="#cb42-1050" aria-hidden="true" tabindex="-1"></a>    <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb42-1051"><a href="#cb42-1051" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Extract sequences that are already at appropriate rank</span></span>
+<span id="cb42-1052"><a href="#cb42-1052" aria-hidden="true" tabindex="-1"></a>  reads_rank <span class="ot">&lt;-</span> <span class="fu">filter</span>(reads, rank <span class="sc">==</span> out_rank)</span>
+<span id="cb42-1053"><a href="#cb42-1053" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Drop sequences at a higher rank and return unclassified sequences</span></span>
+<span id="cb42-1054"><a href="#cb42-1054" aria-hidden="true" tabindex="-1"></a>  reads_norank <span class="ot">&lt;-</span> reads <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">!=</span> out_rank, <span class="sc">!</span>rank <span class="sc">%in%</span> high_ranks, <span class="sc">!</span><span class="fu">is.na</span>(taxid))</span>
+<span id="cb42-1055"><a href="#cb42-1055" aria-hidden="true" tabindex="-1"></a>  <span class="cf">while</span>(<span class="fu">nrow</span>(reads_norank) <span class="sc">&gt;</span> <span class="dv">0</span>){ <span class="co"># As long as there are unclassified sequences...</span></span>
+<span id="cb42-1056"><a href="#cb42-1056" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Promote read taxids and re-merge with taxid DB, then re-classify and filter</span></span>
+<span id="cb42-1057"><a href="#cb42-1057" aria-hidden="true" tabindex="-1"></a>    reads_remaining <span class="ot">&lt;-</span> reads_norank <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">taxid =</span> parent_taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1058"><a href="#cb42-1058" aria-hidden="true" tabindex="-1"></a>      <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1059"><a href="#cb42-1059" aria-hidden="true" tabindex="-1"></a>      <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb42-1060"><a href="#cb42-1060" aria-hidden="true" tabindex="-1"></a>    reads_rank <span class="ot">&lt;-</span> reads_remaining <span class="sc">%&gt;%</span> <span class="fu">filter</span>(rank <span class="sc">==</span> out_rank) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1061"><a href="#cb42-1061" aria-hidden="true" tabindex="-1"></a>      <span class="fu">bind_rows</span>(reads_rank)</span>
+<span id="cb42-1062"><a href="#cb42-1062" aria-hidden="true" tabindex="-1"></a>    reads_norank <span class="ot">&lt;-</span> reads_remaining <span class="sc">%&gt;%</span></span>
+<span id="cb42-1063"><a href="#cb42-1063" aria-hidden="true" tabindex="-1"></a>      <span class="fu">filter</span>(rank <span class="sc">!=</span> out_rank, <span class="sc">!</span>rank <span class="sc">%in%</span> high_ranks, <span class="sc">!</span><span class="fu">is.na</span>(taxid))</span>
+<span id="cb42-1064"><a href="#cb42-1064" aria-hidden="true" tabindex="-1"></a>  }</span>
+<span id="cb42-1065"><a href="#cb42-1065" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Finally, extract and append reads that were excluded during the process</span></span>
+<span id="cb42-1066"><a href="#cb42-1066" aria-hidden="true" tabindex="-1"></a>  reads_dropped <span class="ot">&lt;-</span> reads <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="sc">!</span>seq_id <span class="sc">%in%</span> reads_rank<span class="sc">$</span>seq_id)</span>
+<span id="cb42-1067"><a href="#cb42-1067" aria-hidden="true" tabindex="-1"></a>  reads_out <span class="ot">&lt;-</span> reads_rank <span class="sc">%&gt;%</span> <span class="fu">bind_rows</span>(reads_dropped) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1068"><a href="#cb42-1068" aria-hidden="true" tabindex="-1"></a>    <span class="fu">select</span>(<span class="sc">-</span>parent_taxid, <span class="sc">-</span>rank, <span class="sc">-</span>name) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1069"><a href="#cb42-1069" aria-hidden="true" tabindex="-1"></a>    <span class="fu">left_join</span>(taxid_db, <span class="at">by=</span><span class="st">"taxid"</span>)</span>
+<span id="cb42-1070"><a href="#cb42-1070" aria-hidden="true" tabindex="-1"></a>  <span class="fu">return</span>(reads_out)</span>
+<span id="cb42-1071"><a href="#cb42-1071" aria-hidden="true" tabindex="-1"></a>}</span>
+<span id="cb42-1072"><a href="#cb42-1072" aria-hidden="true" tabindex="-1"></a>hv_reads_species <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"species"</span>)</span>
+<span id="cb42-1073"><a href="#cb42-1073" aria-hidden="true" tabindex="-1"></a>hv_reads_genus <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"genus"</span>)</span>
+<span id="cb42-1074"><a href="#cb42-1074" aria-hidden="true" tabindex="-1"></a>hv_reads_family <span class="ot">&lt;-</span> <span class="fu">raise_rank</span>(mrg_hv_named, viral_taxa, <span class="st">"family"</span>)</span>
+<span id="cb42-1075"><a href="#cb42-1075" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-1076"><a href="#cb42-1076" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1079"><a href="#cb42-1079" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-1080"><a href="#cb42-1080" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-family</span></span>
+<span id="cb42-1081"><a href="#cb42-1081" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb42-1082"><a href="#cb42-1082" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1083"><a href="#cb42-1083" aria-hidden="true" tabindex="-1"></a>threshold_major_family <span class="ot">&lt;-</span> <span class="fl">0.06</span></span>
+<span id="cb42-1084"><a href="#cb42-1084" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1085"><a href="#cb42-1085" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral family</span></span>
+<span id="cb42-1086"><a href="#cb42-1086" aria-hidden="true" tabindex="-1"></a>hv_family_counts <span class="ot">&lt;-</span> hv_reads_family <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1087"><a href="#cb42-1087" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1088"><a href="#cb42-1088" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1089"><a href="#cb42-1089" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1090"><a href="#cb42-1090" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb42-1091"><a href="#cb42-1091" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1092"><a href="#cb42-1092" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb42-1093"><a href="#cb42-1093" aria-hidden="true" tabindex="-1"></a>hv_family_major_tab <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1094"><a href="#cb42-1094" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1095"><a href="#cb42-1095" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> threshold_major_family)</span>
+<span id="cb42-1096"><a href="#cb42-1096" aria-hidden="true" tabindex="-1"></a>hv_family_counts_major <span class="ot">&lt;-</span> hv_family_counts <span class="sc">%&gt;%</span></span>
+<span id="cb42-1097"><a href="#cb42-1097" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_family_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1098"><a href="#cb42-1098" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1099"><a href="#cb42-1099" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
+<span id="cb42-1100"><a href="#cb42-1100" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1101"><a href="#cb42-1101" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb42-1102"><a href="#cb42-1102" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_family_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb42-1103"><a href="#cb42-1103" aria-hidden="true" tabindex="-1"></a>hv_family_counts_display <span class="ot">&lt;-</span> hv_family_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb42-1104"><a href="#cb42-1104" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_hv, <span class="at">classification =</span> name_display)</span>
+<span id="cb42-1105"><a href="#cb42-1105" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1106"><a href="#cb42-1106" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb42-1107"><a href="#cb42-1107" aria-hidden="true" tabindex="-1"></a>g_hv_family <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb42-1108"><a href="#cb42-1108" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>hv_family_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-1109"><a href="#cb42-1109" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb42-1110"><a href="#cb42-1110" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb42-1111"><a href="#cb42-1111" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb42-1112"><a href="#cb42-1112" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral family"</span>)</span>
+<span id="cb42-1113"><a href="#cb42-1113" aria-hidden="true" tabindex="-1"></a>g_hv_family</span>
+<span id="cb42-1114"><a href="#cb42-1114" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-1115"><a href="#cb42-1115" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1118"><a href="#cb42-1118" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-1119"><a href="#cb42-1119" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-genus</span></span>
+<span id="cb42-1120"><a href="#cb42-1120" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb42-1121"><a href="#cb42-1121" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1122"><a href="#cb42-1122" aria-hidden="true" tabindex="-1"></a>threshold_major_genus <span class="ot">&lt;-</span> <span class="fl">0.06</span></span>
+<span id="cb42-1123"><a href="#cb42-1123" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1124"><a href="#cb42-1124" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral genus</span></span>
+<span id="cb42-1125"><a href="#cb42-1125" aria-hidden="true" tabindex="-1"></a>hv_genus_counts <span class="ot">&lt;-</span> hv_reads_genus <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1126"><a href="#cb42-1126" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1127"><a href="#cb42-1127" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1128"><a href="#cb42-1128" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1129"><a href="#cb42-1129" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb42-1130"><a href="#cb42-1130" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1131"><a href="#cb42-1131" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb42-1132"><a href="#cb42-1132" aria-hidden="true" tabindex="-1"></a>hv_genus_major_tab <span class="ot">&lt;-</span> hv_genus_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1133"><a href="#cb42-1133" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1134"><a href="#cb42-1134" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> threshold_major_genus)</span>
+<span id="cb42-1135"><a href="#cb42-1135" aria-hidden="true" tabindex="-1"></a>hv_genus_counts_major <span class="ot">&lt;-</span> hv_genus_counts <span class="sc">%&gt;%</span></span>
+<span id="cb42-1136"><a href="#cb42-1136" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_genus_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1137"><a href="#cb42-1137" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1138"><a href="#cb42-1138" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
+<span id="cb42-1139"><a href="#cb42-1139" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1140"><a href="#cb42-1140" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb42-1141"><a href="#cb42-1141" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_genus_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb42-1142"><a href="#cb42-1142" aria-hidden="true" tabindex="-1"></a>hv_genus_counts_display <span class="ot">&lt;-</span> hv_genus_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb42-1143"><a href="#cb42-1143" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_hv, <span class="at">classification =</span> name_display)</span>
+<span id="cb42-1144"><a href="#cb42-1144" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1145"><a href="#cb42-1145" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb42-1146"><a href="#cb42-1146" aria-hidden="true" tabindex="-1"></a>g_hv_genus <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb42-1147"><a href="#cb42-1147" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>hv_genus_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-1148"><a href="#cb42-1148" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb42-1149"><a href="#cb42-1149" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb42-1150"><a href="#cb42-1150" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb42-1151"><a href="#cb42-1151" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral genus"</span>)</span>
+<span id="cb42-1152"><a href="#cb42-1152" aria-hidden="true" tabindex="-1"></a>g_hv_genus</span>
+<span id="cb42-1153"><a href="#cb42-1153" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-1154"><a href="#cb42-1154" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1157"><a href="#cb42-1157" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-1158"><a href="#cb42-1158" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: hv-species</span></span>
+<span id="cb42-1159"><a href="#cb42-1159" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 6</span></span>
+<span id="cb42-1160"><a href="#cb42-1160" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1161"><a href="#cb42-1161" aria-hidden="true" tabindex="-1"></a>threshold_major_species <span class="ot">&lt;-</span> <span class="fl">0.10</span></span>
+<span id="cb42-1162"><a href="#cb42-1162" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1163"><a href="#cb42-1163" aria-hidden="true" tabindex="-1"></a><span class="co"># Count reads for each human-viral species</span></span>
+<span id="cb42-1164"><a href="#cb42-1164" aria-hidden="true" tabindex="-1"></a>hv_species_counts <span class="ot">&lt;-</span> hv_reads_species <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1165"><a href="#cb42-1165" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type, name, taxid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1166"><a href="#cb42-1166" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name =</span> <span class="st">"n_reads_hv"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1167"><a href="#cb42-1167" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1168"><a href="#cb42-1168" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_hv =</span> n_reads_hv<span class="sc">/</span><span class="fu">sum</span>(n_reads_hv))</span>
+<span id="cb42-1169"><a href="#cb42-1169" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1170"><a href="#cb42-1170" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify high-ranking families and group others</span></span>
+<span id="cb42-1171"><a href="#cb42-1171" aria-hidden="true" tabindex="-1"></a>hv_species_major_tab <span class="ot">&lt;-</span> hv_species_counts <span class="sc">%&gt;%</span> <span class="fu">group_by</span>(name) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1172"><a href="#cb42-1172" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(p_reads_hv <span class="sc">==</span> <span class="fu">max</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(<span class="fu">row_number</span>() <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1173"><a href="#cb42-1173" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(p_reads_hv)) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(p_reads_hv <span class="sc">&gt;</span> threshold_major_species)</span>
+<span id="cb42-1174"><a href="#cb42-1174" aria-hidden="true" tabindex="-1"></a>hv_species_counts_major <span class="ot">&lt;-</span> hv_species_counts <span class="sc">%&gt;%</span></span>
+<span id="cb42-1175"><a href="#cb42-1175" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">ifelse</span>(name <span class="sc">%in%</span> hv_species_major_tab<span class="sc">$</span>name, name, <span class="st">"Other"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1176"><a href="#cb42-1176" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, ctrl, na_type, name_display) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1177"><a href="#cb42-1177" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_hv =</span> <span class="fu">sum</span>(n_reads_hv), <span class="at">p_reads_hv =</span> <span class="fu">sum</span>(p_reads_hv), </span>
+<span id="cb42-1178"><a href="#cb42-1178" aria-hidden="true" tabindex="-1"></a>            <span class="at">.groups=</span><span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1179"><a href="#cb42-1179" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">name_display =</span> <span class="fu">factor</span>(name_display, </span>
+<span id="cb42-1180"><a href="#cb42-1180" aria-hidden="true" tabindex="-1"></a>                               <span class="at">levels =</span> <span class="fu">c</span>(hv_species_major_tab<span class="sc">$</span>name, <span class="st">"Other"</span>)))</span>
+<span id="cb42-1181"><a href="#cb42-1181" aria-hidden="true" tabindex="-1"></a>hv_species_counts_display <span class="ot">&lt;-</span> hv_species_counts_major <span class="sc">%&gt;%</span></span>
+<span id="cb42-1182"><a href="#cb42-1182" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">p_reads =</span> p_reads_hv, <span class="at">classification =</span> name_display)</span>
+<span id="cb42-1183"><a href="#cb42-1183" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1184"><a href="#cb42-1184" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb42-1185"><a href="#cb42-1185" aria-hidden="true" tabindex="-1"></a>g_hv_species <span class="ot">&lt;-</span> g_comp_base <span class="sc">+</span> </span>
+<span id="cb42-1186"><a href="#cb42-1186" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">data=</span>hv_species_counts_display, <span class="at">position =</span> <span class="st">"stack"</span>) <span class="sc">+</span></span>
+<span id="cb42-1187"><a href="#cb42-1187" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">"% HV Reads"</span>, <span class="at">limits=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="fl">1.01</span>), </span>
+<span id="cb42-1188"><a href="#cb42-1188" aria-hidden="true" tabindex="-1"></a>                     <span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="fl">0.2</span>),</span>
+<span id="cb42-1189"><a href="#cb42-1189" aria-hidden="true" tabindex="-1"></a>                     <span class="at">expand=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">0</span>), <span class="at">labels =</span> <span class="cf">function</span>(y) y<span class="sc">*</span><span class="dv">100</span>) <span class="sc">+</span></span>
+<span id="cb42-1190"><a href="#cb42-1190" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values=</span>palette_viral, <span class="at">name =</span> <span class="st">"Viral species"</span>)</span>
+<span id="cb42-1191"><a href="#cb42-1191" aria-hidden="true" tabindex="-1"></a>g_hv_species</span>
+<span id="cb42-1192"><a href="#cb42-1192" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-1193"><a href="#cb42-1193" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1194"><a href="#cb42-1194" aria-hidden="true" tabindex="-1"></a>Compared to the previous datasets I've analyzed, the most notable difference is the absence of enteric viruses: *Norovirus*, *Rotavirus*, and *Enterovirus* are all absent from the list of abundant human-viral taxa, as are \~all other gastrointestinal pathogens.</span>
+<span id="cb42-1195"><a href="#cb42-1195" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1196"><a href="#cb42-1196" aria-hidden="true" tabindex="-1"></a>Finally, here are the overall relative abundances of a set of specific viral genera picked out manually on the basis of scientific or medical interest. In the future, I'll quantify the RA of these genera across all datasets analyzed with this pipeline to date, which should give us a better sense of how these data compare to others' under this pipeline.</span>
+<span id="cb42-1197"><a href="#cb42-1197" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1200"><a href="#cb42-1200" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb42-1201"><a href="#cb42-1201" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-height: 5</span></span>
+<span id="cb42-1202"><a href="#cb42-1202" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: ra-genera</span></span>
+<span id="cb42-1203"><a href="#cb42-1203" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1204"><a href="#cb42-1204" aria-hidden="true" tabindex="-1"></a><span class="co"># Define reference genera</span></span>
+<span id="cb42-1205"><a href="#cb42-1205" aria-hidden="true" tabindex="-1"></a>path_genera_rna <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"Mamastrovirus"</span>, <span class="st">"Enterovirus"</span>, <span class="st">"Salivirus"</span>, <span class="st">"Kobuvirus"</span>, <span class="st">"Norovirus"</span>, <span class="st">"Sapovirus"</span>, <span class="st">"Rotavirus"</span>, <span class="st">"Alphacoronavirus"</span>, <span class="st">"Betacoronavirus"</span>,</span>
+<span id="cb42-1206"><a href="#cb42-1206" aria-hidden="true" tabindex="-1"></a>                 <span class="st">"Alphainfluenzavirus"</span>, <span class="st">"Betainfluenzavirus"</span>, <span class="st">"Lentivirus"</span>)</span>
+<span id="cb42-1207"><a href="#cb42-1207" aria-hidden="true" tabindex="-1"></a>path_genera_dna <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"Mastadenovirus"</span>, <span class="st">"Alphapolyomavirus"</span>, <span class="st">"Betapolyomavirus"</span>, <span class="st">"Alphapapillomavirus"</span>, <span class="st">"Betapapillomavirus"</span>, <span class="st">"Orthopoxvirus"</span>, <span class="st">"Simplexvirus"</span>,</span>
+<span id="cb42-1208"><a href="#cb42-1208" aria-hidden="true" tabindex="-1"></a>                     <span class="st">"Lymphocryptovirus"</span>, <span class="st">"Cytomegalovirus"</span>, <span class="st">"Dependoparvovirus"</span>)</span>
+<span id="cb42-1209"><a href="#cb42-1209" aria-hidden="true" tabindex="-1"></a>path_genera <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(<span class="fu">tibble</span>(<span class="at">name=</span>path_genera_rna, <span class="at">genome_type=</span><span class="st">"RNA genome"</span>),</span>
+<span id="cb42-1210"><a href="#cb42-1210" aria-hidden="true" tabindex="-1"></a>                         <span class="fu">tibble</span>(<span class="at">name=</span>path_genera_dna, <span class="at">genome_type=</span><span class="st">"DNA genome"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1211"><a href="#cb42-1211" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(viral_taxa, <span class="at">by=</span><span class="st">"name"</span>)</span>
+<span id="cb42-1212"><a href="#cb42-1212" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1213"><a href="#cb42-1213" aria-hidden="true" tabindex="-1"></a><span class="co"># Count in each sample</span></span>
+<span id="cb42-1214"><a href="#cb42-1214" aria-hidden="true" tabindex="-1"></a>n_path_genera <span class="ot">&lt;-</span> hv_reads_genus <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1215"><a href="#cb42-1215" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(sample, date, na_type, ctrl, name, taxid) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1216"><a href="#cb42-1216" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(<span class="at">name=</span><span class="st">"n_reads_viral"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1217"><a href="#cb42-1217" aria-hidden="true" tabindex="-1"></a>  <span class="fu">inner_join</span>(path_genera, <span class="at">by=</span><span class="fu">c</span>(<span class="st">"name"</span>, <span class="st">"taxid"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1218"><a href="#cb42-1218" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(read_counts_raw, <span class="at">by=</span><span class="fu">c</span>(<span class="st">"sample"</span>, <span class="st">"date"</span>, <span class="st">"na_type"</span>, <span class="st">"ctrl"</span>)) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1219"><a href="#cb42-1219" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_viral =</span> n_reads_viral<span class="sc">/</span>n_reads_raw)</span>
+<span id="cb42-1220"><a href="#cb42-1220" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1221"><a href="#cb42-1221" aria-hidden="true" tabindex="-1"></a><span class="co"># Pivot out and back to add zero lines</span></span>
+<span id="cb42-1222"><a href="#cb42-1222" aria-hidden="true" tabindex="-1"></a>n_path_genera_out <span class="ot">&lt;-</span> n_path_genera <span class="sc">%&gt;%</span> ungroup <span class="sc">%&gt;%</span> <span class="fu">select</span>(sample, name, n_reads_viral) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1223"><a href="#cb42-1223" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">names_from=</span><span class="st">"name"</span>, <span class="at">values_from=</span><span class="st">"n_reads_viral"</span>, <span class="at">values_fill=</span><span class="dv">0</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1224"><a href="#cb42-1224" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="sc">-</span>sample, <span class="at">names_to=</span><span class="st">"name"</span>, <span class="at">values_to=</span><span class="st">"n_reads_viral"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1225"><a href="#cb42-1225" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(read_counts_raw, <span class="at">by=</span><span class="st">"sample"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1226"><a href="#cb42-1226" aria-hidden="true" tabindex="-1"></a>  <span class="fu">left_join</span>(path_genera, <span class="at">by=</span><span class="st">"name"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1227"><a href="#cb42-1227" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">p_reads_viral =</span> n_reads_viral<span class="sc">/</span>n_reads_raw)</span>
+<span id="cb42-1228"><a href="#cb42-1228" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1229"><a href="#cb42-1229" aria-hidden="true" tabindex="-1"></a><span class="do">## Aggregate across dates</span></span>
+<span id="cb42-1230"><a href="#cb42-1230" aria-hidden="true" tabindex="-1"></a>n_path_genera_stype <span class="ot">&lt;-</span> n_path_genera_out <span class="sc">%&gt;%</span> </span>
+<span id="cb42-1231"><a href="#cb42-1231" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(na_type, ctrl,</span>
+<span id="cb42-1232"><a href="#cb42-1232" aria-hidden="true" tabindex="-1"></a>           name, taxid, genome_type) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1233"><a href="#cb42-1233" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="at">n_reads_raw =</span> <span class="fu">sum</span>(n_reads_raw),</span>
+<span id="cb42-1234"><a href="#cb42-1234" aria-hidden="true" tabindex="-1"></a>            <span class="at">n_reads_viral =</span> <span class="fu">sum</span>(n_reads_viral), <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-1235"><a href="#cb42-1235" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample=</span><span class="st">"All samples"</span>, <span class="at">date=</span><span class="st">"All dates"</span>,</span>
+<span id="cb42-1236"><a href="#cb42-1236" aria-hidden="true" tabindex="-1"></a>         <span class="at">p_reads_viral =</span> n_reads_viral<span class="sc">/</span>n_reads_raw)</span>
+<span id="cb42-1237"><a href="#cb42-1237" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1238"><a href="#cb42-1238" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
+<span id="cb42-1239"><a href="#cb42-1239" aria-hidden="true" tabindex="-1"></a>g_path_genera <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(n_path_genera_stype,</span>
+<span id="cb42-1240"><a href="#cb42-1240" aria-hidden="true" tabindex="-1"></a>                        <span class="fu">aes</span>(<span class="at">y=</span>name, <span class="at">x=</span>p_reads_viral, <span class="at">color=</span>na_type)) <span class="sc">+</span></span>
+<span id="cb42-1241"><a href="#cb42-1241" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>() <span class="sc">+</span></span>
+<span id="cb42-1242"><a href="#cb42-1242" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_log10</span>(<span class="at">name=</span><span class="st">"Relative abundance"</span>) <span class="sc">+</span></span>
+<span id="cb42-1243"><a href="#cb42-1243" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_na</span>() <span class="sc">+</span></span>
+<span id="cb42-1244"><a href="#cb42-1244" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(genome_type<span class="sc">~</span>ctrl, <span class="at">scales=</span><span class="st">"free_y"</span>) <span class="sc">+</span></span>
+<span id="cb42-1245"><a href="#cb42-1245" aria-hidden="true" tabindex="-1"></a>  theme_base <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.title.y =</span> <span class="fu">element_blank</span>())</span>
+<span id="cb42-1246"><a href="#cb42-1246" aria-hidden="true" tabindex="-1"></a>g_path_genera</span>
+<span id="cb42-1247"><a href="#cb42-1247" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb42-1248"><a href="#cb42-1248" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1249"><a href="#cb42-1249" aria-hidden="true" tabindex="-1"></a><span class="fu"># Conclusion</span></span>
+<span id="cb42-1250"><a href="#cb42-1250" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1251"><a href="#cb42-1251" aria-hidden="true" tabindex="-1"></a>This is the first air-sampling dataset I've analyzed using this pipeline, and it was interesting to see the differences from the wastewater datasets I've been analyzing so far. Among the more striking differences were:</span>
+<span id="cb42-1252"><a href="#cb42-1252" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1253"><a href="#cb42-1253" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>A much higher total fraction of human reads;</span>
+<span id="cb42-1254"><a href="#cb42-1254" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1255"><a href="#cb42-1255" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>A lower total fraction of viral reads;</span>
+<span id="cb42-1256"><a href="#cb42-1256" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1257"><a href="#cb42-1257" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Near-total absence of enteric viruses and *Tobamovirus*;</span>
+<span id="cb42-1258"><a href="#cb42-1258" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1259"><a href="#cb42-1259" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Much higher relative abundance of herpesviruses, particularly HCMV, and papillomaviruses among human-infecting virus reads.</span>
+<span id="cb42-1260"><a href="#cb42-1260" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1261"><a href="#cb42-1261" aria-hidden="true" tabindex="-1"></a>Conversely, one thing that was not notably different, at least in RNA viruses, was the total relative abundance of human-infecting viruses as a whole. Given the lower fraction of reads made up of all viruses, this means that the fraction of total viruses arising from human-infecting viruses is much higher here than we've historically seen with wastewater data. In particular, HCMV represents a nontrivial fraction of total viruses for many DNA libraries, the first time I've seen a human pathogen show up significantly in the total virus composition data.</span>
+<span id="cb42-1262"><a href="#cb42-1262" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb42-1263"><a href="#cb42-1263" aria-hidden="true" tabindex="-1"></a>Going forward, I have two more air sampling datasets to analyze, <span class="co">[</span><span class="ot">Rosario et al. 2018</span><span class="co">](https://doi.org/10.1186/s40168-021-01044-7)</span> and <span class="co">[</span><span class="ot">Leung et al. 2021</span><span class="co">](https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-021-01044-7)</span>. It will be interesting to see whether the findings from this dataset generalize to other air sampling contexts.</span>
+</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div></div></div></div></div>
+</div> <!-- /content -->
+
+
+
+</body></html>
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index e2639fc..c48b1c3 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -32,7 +32,7 @@
     "href": "index.html",
     "title": "Will's Public NAO Notebook",
     "section": "",
-    "text": "Workflow analysis of Brumfield et al. (2022)\n\n\nWastewater from a manhole in Maryland.\n\n\n\n\n\nApr 8, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Spurbeck et al. (2023)\n\n\nCave carpa.\n\n\n\n\n\nApr 1, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nFollowup analysis of Yang et al. (2020)\n\n\nDigging into deduplication.\n\n\n\n\n\nMar 19, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Yang et al. (2020)\n\n\nWastewater from Xinjiang.\n\n\n\n\n\nMar 16, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nImproving read deduplication in the MGS workflow\n\n\nRemoving reverse-complement duplicates of human-viral reads.\n\n\n\n\n\nMar 1, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Rothman et al. (2021), part 2\n\n\nPanel-enriched samples.\n\n\n\n\n\nFeb 29, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Rothman et al. (2021), part 1\n\n\nUnenriched samples.\n\n\n\n\n\nFeb 27, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Crits-Christoph et al. (2021), part 3\n\n\nFixing the virus pipeline.\n\n\n\n\n\nFeb 15, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Crits-Christoph et al. (2021), part 2\n\n\nAbundance and composition of human-infecting viruses.\n\n\n\n\n\nFeb 8, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Crits-Christoph et al. (2021), part 1\n\n\nPreprocessing and composition.\n\n\n\n\n\nFeb 4, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nAutomating BLAST validation of human viral read assignment\n\n\nExperiments with BLASTN remote mode\n\n\n\n\n\nJan 30, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nProject Runway RNA-seq testing data: removing livestock reads\n\n\n\n\n\n\n\n\nDec 22, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Project Runway RNA-seq testing data\n\n\nApplying a new workflow to some oldish data.\n\n\n\n\n\nDec 19, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nEstimating the effect of read depth on duplication rate for Project Runway DNA data\n\n\nHow deep can we go?\n\n\n\n\n\nNov 8, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nComparing viral read assignments between pipelines on Project Runway data\n\n\n\n\n\n\n\n\nNov 2, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nInitial analysis of Project Runway protocol testing data\n\n\n\n\n\n\n\n\nOct 31, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nComparing options for read deduplication\n\n\nClumpify vs fastp\n\n\n\n\n\nOct 19, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nComparing Ribodetector and bbduk for rRNA detection\n\n\nIn search of quick rRNA filtering.\n\n\n\n\n\nOct 16, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nComparing FASTP and AdapterRemoval for MGS pre-processing\n\n\nTwo tools – how do they perform?\n\n\n\n\n\nOct 12, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nHow does Element AVITI sequencing work?\n\n\nFindings of a shallow investigation\n\n\n\n\n\nOct 11, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nExtraction experiment 2: high-level results & interpretation\n\n\nComparing RNA yields and quality across extraction kits for settled solids\n\n\n\n\n\nSep 21, 2023\n\n\n\n\n\n\nNo matching items"
+    "text": "Workflow analysis of Prussin et al. (2019)\n\n\nWastewater from a manhole in Maryland.\n\n\n\n\n\nApr 12, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Brumfield et al. (2022)\n\n\nWastewater from a manhole in Maryland.\n\n\n\n\n\nApr 8, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Spurbeck et al. (2023)\n\n\nCave carpa.\n\n\n\n\n\nApr 1, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nFollowup analysis of Yang et al. (2020)\n\n\nDigging into deduplication.\n\n\n\n\n\nMar 19, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Yang et al. (2020)\n\n\nWastewater from Xinjiang.\n\n\n\n\n\nMar 16, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nImproving read deduplication in the MGS workflow\n\n\nRemoving reverse-complement duplicates of human-viral reads.\n\n\n\n\n\nMar 1, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Rothman et al. (2021), part 2\n\n\nPanel-enriched samples.\n\n\n\n\n\nFeb 29, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Rothman et al. (2021), part 1\n\n\nUnenriched samples.\n\n\n\n\n\nFeb 27, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Crits-Christoph et al. (2021), part 3\n\n\nFixing the virus pipeline.\n\n\n\n\n\nFeb 15, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Crits-Christoph et al. (2021), part 2\n\n\nAbundance and composition of human-infecting viruses.\n\n\n\n\n\nFeb 8, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Crits-Christoph et al. (2021), part 1\n\n\nPreprocessing and composition.\n\n\n\n\n\nFeb 4, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nAutomating BLAST validation of human viral read assignment\n\n\nExperiments with BLASTN remote mode\n\n\n\n\n\nJan 30, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nProject Runway RNA-seq testing data: removing livestock reads\n\n\n\n\n\n\n\n\nDec 22, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nWorkflow analysis of Project Runway RNA-seq testing data\n\n\nApplying a new workflow to some oldish data.\n\n\n\n\n\nDec 19, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nEstimating the effect of read depth on duplication rate for Project Runway DNA data\n\n\nHow deep can we go?\n\n\n\n\n\nNov 8, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nComparing viral read assignments between pipelines on Project Runway data\n\n\n\n\n\n\n\n\nNov 2, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nInitial analysis of Project Runway protocol testing data\n\n\n\n\n\n\n\n\nOct 31, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nComparing options for read deduplication\n\n\nClumpify vs fastp\n\n\n\n\n\nOct 19, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nComparing Ribodetector and bbduk for rRNA detection\n\n\nIn search of quick rRNA filtering.\n\n\n\n\n\nOct 16, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nComparing FASTP and AdapterRemoval for MGS pre-processing\n\n\nTwo tools – how do they perform?\n\n\n\n\n\nOct 12, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nHow does Element AVITI sequencing work?\n\n\nFindings of a shallow investigation\n\n\n\n\n\nOct 11, 2023\n\n\n\n\n\n\n\n\n\n\n\n\nExtraction experiment 2: high-level results & interpretation\n\n\nComparing RNA yields and quality across extraction kits for settled solids\n\n\n\n\n\nSep 21, 2023\n\n\n\n\n\n\nNo matching items"
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@@ -278,5 +278,12 @@
     "title": "Workflow analysis of Brumfield et al. (2022)",
     "section": "",
     "text": "Continuing my analysis of datasets from the P2RA preprint, I analyzed the data from Brumfield et al. (2022). This study conducted longitudinal monitoring of SARS-CoV-2 via qPCR from a single manhole in Maryland, combined with comprehensive microbiome analysis using metagenomics during a major COVID spike in early 2021. In total, they sequenced six samples from February to April 2021.\nOne unusual feature that makes this study interesting is that they conducted both RNA and DNA sequencing on each study. Prior to sequencing, samples underwent concentration with the InnovaPrep Concentrating Pipette, followed by separate DNA & RNA extraction using different kits. RNA samples underwent rRNA depletion prior to library prep. All samples were sequenced on an Illumina HiSeq4000 with 2x150bp reads.\nThe raw data\nThe 6 samples from the Brumfield dataset yielded 24M-45M (mean 33M) DNA-sequencing reads and 29M-64M (mean 46M) RNA-sequencing reads per sample, for a total of 196M DNA read pairs and 276M RNA read pairs (59 and 83 gigabases of sequence, respectively). Read qualities were mostly high but tailed off at the 3’ end in some samples, suggesting the need for trimming. Adapter levels were very high. Inferred duplication levels were 44-58% in DNA reads and 90-96% in RNA reads, i.e. very high.\n\nCode# Data input paths\ndata_dir &lt;- \"../data/2024-03-19_brumfield/\"\nlibraries_path &lt;- file.path(data_dir, \"sample-metadata.csv\")\nbasic_stats_path &lt;- file.path(data_dir, \"qc_basic_stats.tsv\")\nadapter_stats_path &lt;- file.path(data_dir, \"qc_adapter_stats.tsv\")\nquality_base_stats_path &lt;- file.path(data_dir, \"qc_quality_base_stats.tsv\")\nquality_seq_stats_path &lt;- file.path(data_dir, \"qc_quality_sequence_stats.tsv\")\n\n# Import libraries and extract metadata from sample names\nlibraries &lt;- read_csv(libraries_path, show_col_types = FALSE) %&gt;%\n  arrange(date) %&gt;% mutate(date = fct_inorder(as.character(date))) %&gt;%\n  arrange(desc(na_type)) %&gt;% mutate(na_type = fct_inorder(na_type))\n\n# Import QC data\nstages &lt;- c(\"raw_concat\", \"cleaned\", \"dedup\", \"ribo_initial\", \"ribo_secondary\")\nbasic_stats &lt;- read_tsv(basic_stats_path, show_col_types = FALSE) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;% \n  mutate(stage = factor(stage, levels = stages),\n         sample = fct_inorder(sample))\nadapter_stats &lt;- read_tsv(adapter_stats_path, show_col_types = FALSE) %&gt;%\n    mutate(sample = sub(\"_2$\", \"\", sample)) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;%\n  mutate(stage = factor(stage, levels = stages),\n         read_pair = fct_inorder(as.character(read_pair)))\nquality_base_stats &lt;- read_tsv(quality_base_stats_path, show_col_types = FALSE) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;%\n  mutate(stage = factor(stage, levels = stages),\n         read_pair = fct_inorder(as.character(read_pair)))\nquality_seq_stats &lt;- read_tsv(quality_seq_stats_path, show_col_types = FALSE) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;%\n  mutate(stage = factor(stage, levels = stages),\n         read_pair = fct_inorder(as.character(read_pair)))\n\n# Filter to raw data\nbasic_stats_raw &lt;- basic_stats %&gt;% filter(stage == \"raw_concat\")\nadapter_stats_raw &lt;- adapter_stats %&gt;% filter(stage == \"raw_concat\")\nquality_base_stats_raw &lt;- quality_base_stats %&gt;% filter(stage == \"raw_concat\")\nquality_seq_stats_raw &lt;- quality_seq_stats %&gt;% filter(stage == \"raw_concat\")\n\n\n\nCode# Visualize basic stats\nscale_fill_na &lt;- purrr::partial(scale_fill_brewer, palette=\"Set1\", name=\"Nucleic acid type\")\ng_basic &lt;- ggplot(basic_stats_raw, aes(x=date, fill=na_type, group=sample)) +\n  scale_fill_na() + theme_kit\ng_nreads_raw &lt;- g_basic + geom_col(aes(y=n_read_pairs), position = \"dodge\") +\n  scale_y_continuous(name=\"# Read pairs\", expand=c(0,0))\ng_nreads_raw_2 &lt;- g_nreads_raw + theme(legend.position = \"none\")\nlegend_group &lt;- get_legend(g_nreads_raw)\n\ng_nbases_raw &lt;- g_basic + geom_col(aes(y=n_bases_approx), position = \"dodge\") +\n  scale_y_continuous(name=\"Total base pairs (approx)\", expand=c(0,0)) + \n  theme(legend.position = \"none\")\ng_ndup_raw &lt;- g_basic + geom_col(aes(y=percent_duplicates), position = \"dodge\") +\n  scale_y_continuous(name=\"% Duplicates (FASTQC)\", expand=c(0,0), limits = c(0,100), breaks = seq(0,100,20)) +\n  theme(legend.position = \"none\")\n\ng_basic_raw &lt;- plot_grid(g_nreads_raw_2 + g_nbases_raw + g_ndup_raw, legend_group, ncol = 1, rel_heights = c(1,0.1))\ng_basic_raw\n\n\n\n\n\n\n\n\nCodescale_color_na &lt;- purrr::partial(scale_color_brewer,palette=\"Set1\",name=\"Nucleic acid type\")\ng_qual_raw &lt;- ggplot(mapping=aes(color=na_type, linetype=read_pair, \n                         group=interaction(sample,read_pair))) + \n  scale_color_na() + scale_linetype_discrete(name = \"Read Pair\") +\n  guides(color=guide_legend(nrow=2,byrow=TRUE),\n         linetype = guide_legend(nrow=2,byrow=TRUE)) +\n  theme_base\n\n# Visualize adapters\ng_adapters_raw &lt;- g_qual_raw + \n  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats_raw) +\n  scale_y_continuous(name=\"% Adapters\", limits=c(0,NA),\n                     breaks = seq(0,100,10), expand=c(0,0)) +\n  scale_x_continuous(name=\"Position\", limits=c(0,NA),\n                     breaks=seq(0,140,20), expand=c(0,0)) +\n  facet_wrap(~adapter)\ng_adapters_raw\n\n\n\n\n\n\nCode# Visualize quality\ng_quality_base_raw &lt;- g_qual_raw +\n  geom_hline(yintercept=25, linetype=\"dashed\", color=\"red\") +\n  geom_hline(yintercept=30, linetype=\"dashed\", color=\"red\") +\n  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats_raw) +\n  scale_y_continuous(name=\"Mean Phred score\", expand=c(0,0), limits=c(10,45)) +\n  scale_x_continuous(name=\"Position\", limits=c(0,NA),\n                     breaks=seq(0,140,20), expand=c(0,0))\ng_quality_base_raw\n\n\n\n\n\n\nCodeg_quality_seq_raw &lt;- g_qual_raw +\n  geom_vline(xintercept=25, linetype=\"dashed\", color=\"red\") +\n  geom_vline(xintercept=30, linetype=\"dashed\", color=\"red\") +\n  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats_raw) +\n  scale_x_continuous(name=\"Mean Phred score\", expand=c(0,0)) +\n  scale_y_continuous(name=\"# Sequences\", expand=c(0,0))\ng_quality_seq_raw\n\n\n\n\n\n\n\nPreprocessing\nThe average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. On average, cleaning & deduplication together removed about 50% of total reads from DNA libraries and about 92% from RNA libraries, primarily during deduplication. Only a few reads (low single-digit percentages at most) were lost during subsequent ribodepletion, even in RNA reads, consistent with the samples having undergone rRNA depletion prior to sequencing.\n\nCoden_reads_rel &lt;- basic_stats %&gt;% \n  select(sample, na_type, stage, percent_duplicates, n_read_pairs) %&gt;%\n  group_by(sample, na_type) %&gt;% arrange(sample, na_type, stage) %&gt;%\n  mutate(p_reads_retained = n_read_pairs / lag(n_read_pairs),\n         p_reads_lost = 1 - p_reads_retained,\n         p_reads_retained_abs = n_read_pairs / n_read_pairs[1],\n         p_reads_lost_abs = 1-p_reads_retained_abs,\n         p_reads_lost_abs_marginal = p_reads_lost_abs - lag(p_reads_lost_abs))\nn_reads_rel_display &lt;- n_reads_rel %&gt;% rename(Stage=stage, `NA Type`=na_type) %&gt;% \n  group_by(`NA Type`, Stage) %&gt;% \n  summarize(`% Total Reads Lost (Cumulative)` = paste0(round(min(p_reads_lost_abs*100),1), \"-\", round(max(p_reads_lost_abs*100),1), \" (mean \", round(mean(p_reads_lost_abs*100),1), \")\"),\n            `% Total Reads Lost (Marginal)` = paste0(round(min(p_reads_lost_abs_marginal*100),1), \"-\", round(max(p_reads_lost_abs_marginal*100),1), \" (mean \", round(mean(p_reads_lost_abs_marginal*100),1), \")\"), .groups=\"drop\") %&gt;% \n  filter(Stage != \"raw_concat\") %&gt;%\n  mutate(Stage = Stage %&gt;% as.numeric %&gt;% factor(labels=c(\"Trimming & filtering\", \"Deduplication\", \"Initial ribodepletion\", \"Secondary ribodepletion\")))\nn_reads_rel_display\n\n\n  \n\n\n\n\nCode# Plot reads over preprocessing\ng_reads_stages &lt;- ggplot(basic_stats, aes(x=stage, y=n_read_pairs,fill=na_type,group=sample)) +\n  geom_col(position=\"dodge\") + scale_fill_na() +\n  scale_y_continuous(\"# Read pairs\", expand=c(0,0)) +\n  theme_kit\ng_reads_stages\n\n\n\n\n\n\nCode# Plot relative read losses during preprocessing\ng_reads_rel &lt;- ggplot(n_reads_rel, aes(x=stage, y=p_reads_lost_abs_marginal,fill=na_type,group=sample)) +\n  geom_col(position=\"dodge\") + scale_fill_na() +\n  scale_y_continuous(\"% Total Reads Lost\", expand=c(0,0), labels = function(x) x*100) +\n  theme_kit\ng_reads_rel\n\n\n\n\n\n\n\nData cleaning with FASTP was very successful at removing adapters, with very few adapter sequences found by FASTQC at any stage after the raw data. FASTP was also successful at improving read quality.\n\nCodeg_qual &lt;- ggplot(mapping=aes(color=na_type, linetype=read_pair, \n                         group=interaction(sample,read_pair))) + \n  scale_color_na() + scale_linetype_discrete(name = \"Read Pair\") +\n  guides(color=guide_legend(nrow=2,byrow=TRUE),\n         linetype = guide_legend(nrow=2,byrow=TRUE)) +\n  theme_base\n\n# Visualize adapters\ng_adapters &lt;- g_qual + \n  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats) +\n  scale_y_continuous(name=\"% Adapters\", limits=c(0,20),\n                     breaks = seq(0,50,10), expand=c(0,0)) +\n  scale_x_continuous(name=\"Position\", limits=c(0,NA),\n                     breaks=seq(0,140,20), expand=c(0,0)) +\n  facet_grid(stage~adapter)\ng_adapters\n\n\n\n\n\n\nCode# Visualize quality\ng_quality_base &lt;- g_qual +\n  geom_hline(yintercept=25, linetype=\"dashed\", color=\"red\") +\n  geom_hline(yintercept=30, linetype=\"dashed\", color=\"red\") +\n  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats) +\n  scale_y_continuous(name=\"Mean Phred score\", expand=c(0,0), limits=c(10,45)) +\n  scale_x_continuous(name=\"Position\", limits=c(0,NA),\n                     breaks=seq(0,140,20), expand=c(0,0)) +\n  facet_grid(stage~.)\ng_quality_base\n\n\n\n\n\n\nCodeg_quality_seq &lt;- g_qual +\n  geom_vline(xintercept=25, linetype=\"dashed\", color=\"red\") +\n  geom_vline(xintercept=30, linetype=\"dashed\", color=\"red\") +\n  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats) +\n  scale_x_continuous(name=\"Mean Phred score\", expand=c(0,0)) +\n  scale_y_continuous(name=\"# Sequences\", expand=c(0,0)) +\n  facet_grid(stage~.)\ng_quality_seq\n\n\n\n\n\n\n\nAccording to FASTQC, deduplication was moderately effective at reducing measured duplicate levels, with FASTQC-measured levels falling from an average of 50% to one of 16% for DNA reads, and from 93% to 42% for RNA reads:\n\nCodeg_dup_stages &lt;- ggplot(basic_stats, aes(x=stage, y=percent_duplicates, fill=na_type, group=sample)) +\n  geom_col(position=\"dodge\") + scale_fill_na() +\n  scale_y_continuous(\"% Duplicates\", limits=c(0,100), breaks=seq(0,100,20), expand=c(0,0)) +\n  theme_kit\ng_readlen_stages &lt;- ggplot(basic_stats, aes(x=stage, y=mean_seq_len, fill=na_type, group=sample)) +\n  geom_col(position=\"dodge\") + scale_fill_na() +\n  scale_y_continuous(\"Mean read length (nt)\", expand=c(0,0)) +\n  theme_kit\nlegend_loc &lt;- get_legend(g_dup_stages)\ng_dup_stages\n\n\n\n\n\n\nCodeg_readlen_stages\n\n\n\n\n\n\n\nHigh-level composition\nAs before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:\n\nCode# Import Bracken data\nbracken_path &lt;- file.path(data_dir, \"bracken_counts.tsv\")\nbracken &lt;- read_tsv(bracken_path, show_col_types = FALSE)\ntotal_assigned &lt;- bracken %&gt;% group_by(sample) %&gt;% summarize(\n  name = \"Total\",\n  kraken_assigned_reads = sum(kraken_assigned_reads),\n  added_reads = sum(added_reads),\n  new_est_reads = sum(new_est_reads),\n  fraction_total_reads = sum(fraction_total_reads)\n)\nbracken_spread &lt;- bracken %&gt;% select(name, sample, new_est_reads) %&gt;%\n  mutate(name = tolower(name)) %&gt;%\n  pivot_wider(id_cols = \"sample\", names_from = \"name\", values_from = \"new_est_reads\")\n# Count reads\nread_counts_preproc &lt;- basic_stats %&gt;% \n  select(sample, na_type, date, stage, n_read_pairs) %&gt;%\n  pivot_wider(id_cols = c(\"sample\", \"na_type\", \"date\"), names_from=\"stage\", values_from=\"n_read_pairs\")\nread_counts &lt;- read_counts_preproc %&gt;%\n  inner_join(total_assigned %&gt;% select(sample, new_est_reads), by = \"sample\") %&gt;%\n  rename(assigned = new_est_reads) %&gt;%\n  inner_join(bracken_spread, by=\"sample\")\n# Assess composition\nread_comp &lt;- transmute(read_counts, sample=sample, na_type=na_type, date=date,\n                       n_filtered = raw_concat-cleaned,\n                       n_duplicate = cleaned-dedup,\n                       n_ribosomal = (dedup-ribo_initial) + (ribo_initial-ribo_secondary),\n                       n_unassigned = ribo_secondary-assigned,\n                       n_bacterial = bacteria,\n                       n_archaeal = archaea,\n                       n_viral = viruses,\n                       n_human = eukaryota)\nread_comp_long &lt;- pivot_longer(read_comp, -(sample:date), names_to = \"classification\",\n                               names_prefix = \"n_\", values_to = \"n_reads\") %&gt;%\n  mutate(classification = fct_inorder(str_to_sentence(classification))) %&gt;%\n  group_by(sample) %&gt;% mutate(p_reads = n_reads/sum(n_reads))\nread_comp_summ &lt;- read_comp_long %&gt;% \n  group_by(na_type, classification) %&gt;%\n  summarize(n_reads = sum(n_reads), .groups = \"drop_last\") %&gt;%\n  mutate(n_reads = replace_na(n_reads,0),\n    p_reads = n_reads/sum(n_reads),\n    pc_reads = p_reads*100)\n  \n# Plot overall composition\ng_comp &lt;- ggplot(read_comp_long, aes(x=sample, y=p_reads, fill=classification)) +\n  geom_col(position = \"stack\") +\n  scale_y_continuous(name = \"% Reads\", limits = c(0,1.01), breaks = seq(0,1,0.2),\n                     expand = c(0,0), labels = function(x) x*100) +\n  scale_fill_brewer(palette = \"Set1\", name = \"Classification\") +\n  facet_wrap(.~na_type, scales=\"free\", ncol=5) +\n  theme_kit\ng_comp\n\n\n\n\n\n\nCode# Plot composition of minor components\nread_comp_minor &lt;- read_comp_long %&gt;% filter(classification %in% c(\"Archaeal\", \"Viral\", \"Human\", \"Other\"))\npalette_minor &lt;- brewer.pal(9, \"Set1\")[6:9]\ng_comp_minor &lt;- ggplot(read_comp_minor, aes(x=sample, y=p_reads, fill=classification)) +\n  geom_col(position = \"stack\") +\n  scale_y_continuous(name = \"% Reads\",\n                     expand = c(0,0), labels = function(x) x*100) +\n  scale_fill_manual(values=palette_minor, name = \"Classification\") +\n  facet_wrap(.~na_type, scales = \"free_x\", ncol=5) +\n  theme_kit\ng_comp_minor\n\n\n\n\n\n\n\n\nCodep_reads_summ_group &lt;- read_comp_long %&gt;%\n  mutate(classification = ifelse(classification %in% c(\"Filtered\", \"Duplicate\", \"Unassigned\"), \"Excluded\", as.character(classification)),\n         classification = fct_inorder(classification)) %&gt;%\n  group_by(classification, sample, na_type) %&gt;%\n  summarize(p_reads = sum(p_reads), .groups = \"drop\") %&gt;%\n  group_by(classification, na_type) %&gt;%\n  summarize(pc_min = min(p_reads)*100, pc_max = max(p_reads)*100, \n            pc_mean = mean(p_reads)*100, .groups = \"drop\")\np_reads_summ_prep &lt;- p_reads_summ_group %&gt;%\n  mutate(classification = fct_inorder(classification),\n         pc_min = pc_min %&gt;% signif(digits=2) %&gt;% sapply(format, scientific=FALSE, trim=TRUE, digits=2),\n         pc_max = pc_max %&gt;% signif(digits=2) %&gt;% sapply(format, scientific=FALSE, trim=TRUE, digits=2),\n         pc_mean = pc_mean %&gt;% signif(digits=2) %&gt;% sapply(format, scientific=FALSE, trim=TRUE, digits=2),\n         display = paste0(pc_min, \"-\", pc_max, \"% (mean \", pc_mean, \"%)\"))\np_reads_summ &lt;- p_reads_summ_prep %&gt;%\n  select(classification, na_type, display) %&gt;%\n  pivot_wider(names_from=na_type, values_from = display)\np_reads_summ\n\n\n  \n\n\n\nAs previously noted, RNA samples were overwhelmingly composed of duplicates. Despite this, the RNA samples achieved a decently high level of total viral reads, with an average of 0.55% of reads mapping to viruses (higher than Crits-Christoph). Levels of total viral reads were much lower in DNA libraries, which were dominated by unassigned and (non-ribosomal) bacterial sequences.\nWithin viral reads, RNA libraries were (as usual) dominated by plant viruses, particularly Virgaviridae – though one sample, unusually, has a substantial minority of reads from Fiersviridae, a family of RNA phages. Detected DNA viruses come from a wider range of families, but the most abundant families (Suoliviridae, Intestiviridae, Autographiviridae, Myoviridae) are all DNA phages.\n\nCode# Get viral taxonomy\nviral_taxa_path &lt;- file.path(data_dir, \"viral-taxids.tsv.gz\")\nviral_taxa &lt;- read_tsv(viral_taxa_path, show_col_types = FALSE)\n\n# Import Kraken reports & extract & count viral families\nsamples &lt;- as.character(basic_stats_raw$sample)\ncol_names &lt;- c(\"pc_reads_total\", \"n_reads_clade\", \"n_reads_direct\", \"rank\", \"taxid\", \"name\")\nreport_paths &lt;- paste0(data_dir, \"kraken/\", samples, \".report.gz\")\nkraken_reports &lt;- lapply(1:length(samples), \n                         function(n) read_tsv(report_paths[n], col_names = col_names,\n                                              show_col_types = FALSE) %&gt;%\n                           mutate(sample = samples[n])) %&gt;% bind_rows\nkraken_reports_viral &lt;- filter(kraken_reports, taxid %in% viral_taxa$taxid) %&gt;%\n  group_by(sample) %&gt;%\n  mutate(p_reads_viral = n_reads_clade/n_reads_clade[1])\nviral_families &lt;- kraken_reports_viral %&gt;% filter(rank == \"F\") %&gt;%\n  inner_join(libraries, by=\"sample\")\n\n# Identify major viral families\nviral_families_major_tab &lt;- viral_families %&gt;% group_by(name, taxid, na_type) %&gt;%\n  summarize(p_reads_viral_max = max(p_reads_viral), .groups=\"drop\") %&gt;%\n  filter(p_reads_viral_max &gt;= 0.04)\nviral_families_major_list &lt;- viral_families_major_tab %&gt;% pull(name)\nviral_families_major &lt;- viral_families %&gt;% filter(name %in% viral_families_major_list) %&gt;%\n  select(name, taxid, sample, na_type, date, p_reads_viral)\nviral_families_minor &lt;- viral_families_major %&gt;% group_by(sample, date, na_type) %&gt;%\n  summarize(p_reads_viral_major = sum(p_reads_viral), .groups = \"drop\") %&gt;%\n  mutate(name = \"Other\", taxid=NA, p_reads_viral = 1-p_reads_viral_major) %&gt;%\n  select(name, taxid, sample, na_type, date, p_reads_viral)\nviral_families_display &lt;- bind_rows(viral_families_major, viral_families_minor) %&gt;%\n  arrange(desc(p_reads_viral)) %&gt;% mutate(name = factor(name, levels=c(viral_families_major_list, \"Other\")))\ng_families &lt;- ggplot(viral_families_display, aes(x=date, y=p_reads_viral, fill=name)) +\n  geom_col(position=\"stack\") +\n  scale_fill_brewer(palette = \"Set3\", name = \"Viral family\") +\n  scale_y_continuous(name=\"% Viral Reads\", limits=c(0,1), breaks = seq(0,1,0.2),\n                     expand=c(0,0), labels = function(y) y*100) +\n  facet_wrap(~na_type) +\n  theme_kit\ng_families\n\n\n\n\n\n\n\nGiven the very high level of duplicates in the RNA data, I also repeated the analysis from my second Yang et al. entry, re-running taxonomic composition analysis on pre-deduplication data and comparing the effects of deduplication on composition:\n\nCodeclass_levels &lt;- c(\"Unassigned\", \"Bacterial\", \"Archaeal\", \"Viral\", \"Human\")\nsubset_factor &lt;- 0.05\n\n# 1. Remove filtered & duplicate reads from original Bracken output & renormalize\nread_comp_renorm &lt;- read_comp_long %&gt;%\n  filter(! classification %in% c(\"Filtered\", \"Duplicate\")) %&gt;%\n  group_by(sample) %&gt;%\n  mutate(p_reads = n_reads/sum(n_reads),\n         classification = classification %&gt;% as.character %&gt;%\n           ifelse(. == \"Ribosomal\", \"Bacterial\", .)) %&gt;%\n  group_by(sample, na_type, date, classification) %&gt;%\n  summarize(n_reads = sum(n_reads), p_reads = sum(p_reads), .groups = \"drop\") %&gt;%\n  mutate(classification = factor(classification, levels = class_levels))\n  \n# 2. Import pre-deduplicated \nbracken_path_predup &lt;- file.path(data_dir, \"bracken_counts_subset.tsv\")\nbracken_predup &lt;- read_tsv(bracken_path_predup, show_col_types = FALSE)\ntotal_assigned_predup &lt;- bracken_predup %&gt;% group_by(sample) %&gt;% summarize(\n  name = \"Total\",\n  kraken_assigned_reads = sum(kraken_assigned_reads),\n  added_reads = sum(added_reads),\n  new_est_reads = sum(new_est_reads),\n  fraction_total_reads = sum(fraction_total_reads)\n)\nbracken_spread_predup &lt;- bracken_predup %&gt;% select(name, sample, new_est_reads) %&gt;%\n  mutate(name = tolower(name)) %&gt;%\n  pivot_wider(id_cols = \"sample\", names_from = \"name\", values_from = \"new_est_reads\")\n# Count reads\nread_counts_predup &lt;- read_counts_preproc %&gt;%\n  select(sample, na_type, date, raw_concat, cleaned) %&gt;%\n  mutate(raw_concat = raw_concat * subset_factor, cleaned = cleaned * subset_factor) %&gt;%\n  inner_join(total_assigned_predup %&gt;% select(sample, new_est_reads), by = \"sample\") %&gt;%\n  rename(assigned = new_est_reads) %&gt;%\n  inner_join(bracken_spread_predup, by=\"sample\")\n# Assess composition\nread_comp_predup &lt;- transmute(read_counts_predup, sample=sample, na_type = na_type,\n                              date=date,\n                       n_filtered = raw_concat-cleaned,\n                       n_unassigned = cleaned-assigned,\n                       n_bacterial = bacteria,\n                       n_archaeal = archaea,\n                       n_viral = viruses,\n                       n_human = eukaryota)\nread_comp_predup_long &lt;- pivot_longer(read_comp_predup, -(sample:date), \n                                      names_to = \"classification\",\n                                      names_prefix = \"n_\", values_to = \"n_reads\") %&gt;%\n  mutate(classification = fct_inorder(str_to_sentence(classification))) %&gt;%\n  group_by(sample) %&gt;% mutate(p_reads = n_reads/sum(n_reads)) %&gt;%\n  filter(! classification %in% c(\"Filtered\", \"Duplicate\")) %&gt;%\n  group_by(sample) %&gt;%\n  mutate(p_reads = n_reads/sum(n_reads))\n\n# 3. Combine\nread_comp_comb &lt;- bind_rows(read_comp_predup_long %&gt;% mutate(deduplicated = FALSE),\n                            read_comp_renorm %&gt;% mutate(deduplicated = TRUE)) %&gt;%\n  mutate(label = ifelse(deduplicated, \"Post-dedup\", \"Pre-dedup\") %&gt;% fct_inorder)\n\n# Plot overall composition\ng_comp_predup &lt;- ggplot(read_comp_comb, aes(x=label, y=p_reads, fill=classification)) +\n  geom_col(position = \"stack\") +\n  scale_y_continuous(name = \"% Reads\", limits = c(0,1.01), breaks = seq(0,1,0.2),\n                     expand = c(0,0), labels = function(x) x*100) +\n  scale_fill_brewer(palette = \"Set1\", name = \"Classification\") +\n  facet_grid(na_type~date) +\n  theme_kit\ng_comp_predup\n\n\n\n\n\n\n\nIn general, deduplication has little effect on the composition of DNA samples, which remain primarily bacterial and unassigned. A few RNA samples show a reduction in the bacterial (more precisely, bacterial + rRNA) fraction after deduplication, consistent with the presence of some true biological duplicate sequences (most likely rRNA) that are being collapsed. Surprisingly, however, the largest effect is in the opposite direction, with several samples showing large increases in bacterial sequences following deduplication. This suggests that some highly abundant non-bacterial sequence is being collapsed in these samples, increasing the apparent abundance of bacteria.\nThe increase in bacterial reads in these samples comes at the expense of (1) human reads and (2) the unassigned fraction. The former suggests the presence of human rRNA sequences that are being erroneously incorporated into the post-deduplication “bacterial” fraction; however, this effect is smaller than the decrease in unassigned reads. One possibility might be other non-human eukaryotic rRNA sequences, which Kraken is unable to assign using our current database.\nHuman-infecting virus reads: validation\nNext, I investigated the human-infecting virus read content of these unenriched samples. To get good results here, I needed to make some changes to the pipeline, including adding Trimmomatic as an additional primer-trimming step to prevent further primer-contamination-based false positives. However, for the sake of time I’m not describing them in detail here; if you want to see more I encourage you to check the commit log at the workflow repo.\nHaving made these changes, the workflow identified a total of 14,073 RNA read pairs and 70 DNA read pairs across all samples (0.09% and 0.00009% of surviving reads, respectively).\n\nCodehv_reads_filtered_path &lt;- file.path(data_dir, \"hv_hits_putative_filtered.tsv.gz\")\nhv_reads_filtered &lt;- read_tsv(hv_reads_filtered_path, show_col_types = FALSE) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;% \n  arrange(date, na_type)\nn_hv_filtered &lt;- hv_reads_filtered %&gt;% group_by(sample, date, na_type) %&gt;% count %&gt;% inner_join(basic_stats %&gt;% filter(stage == \"ribo_initial\") %&gt;% select(sample, n_read_pairs), by=\"sample\") %&gt;% rename(n_putative = n, n_total = n_read_pairs) %&gt;% mutate(p_reads = n_putative/n_total, pc_reads = p_reads * 100)\nn_hv_filtered_summ &lt;- n_hv_filtered %&gt;% group_by(na_type) %&gt;% summarize(n_putative = sum(n_putative), n_total = sum(n_total)) %&gt;% mutate(p_reads = n_putative/n_total, pc_reads = p_reads*100)\n\n\n\nCodemrg &lt;- hv_reads_filtered %&gt;%\n  mutate(kraken_label = ifelse(assigned_hv, \"Kraken2 HV\\nassignment\",\n                               ifelse(hit_hv, \"Kraken2 HV\\nhit\",\n                                      \"No hit or\\nassignment\"))) %&gt;%\n  group_by(sample, na_type) %&gt;% arrange(desc(adj_score_fwd), desc(adj_score_rev)) %&gt;%\n  mutate(seq_num = row_number(),\n         adj_score_max = pmax(adj_score_fwd, adj_score_rev))\n# Import Bowtie2/Kraken data and perform filtering steps\ng_mrg &lt;- ggplot(mrg, aes(x=adj_score_fwd, y=adj_score_rev)) +\n  geom_point(alpha=0.5, shape=16) + \n  scale_x_continuous(name=\"S(forward read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_y_continuous(name=\"S(reverse read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  facet_grid(na_type~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +\n  theme_base + theme(aspect.ratio=1)\ng_mrg\n\n\n\n\n\n\nCodeg_hist_0 &lt;- ggplot(mrg, aes(x=adj_score_max)) + geom_histogram(binwidth=5,boundary=0,position=\"dodge\") + facet_grid(na_type~kraken_label, scales=\"free_y\") + scale_x_continuous(name = \"Maximum adjusted alignment score\") + scale_y_continuous(name=\"# Read pairs\") + scale_fill_brewer(palette = \"Dark2\") + theme_base + geom_vline(xintercept=20, linetype=\"dashed\", color=\"red\")\ng_hist_0\n\n\n\n\n\n\n\nAs previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I’ve used for previous datasets.\n\nCodemrg_fasta &lt;-  mrg %&gt;%\n  mutate(seq_head = paste0(\"&gt;\", seq_id)) %&gt;%\n  ungroup %&gt;%\n  select(header1=seq_head, seq1=query_seq_fwd, \n         header2=seq_head, seq2=query_seq_rev) %&gt;%\n  mutate(header1=paste0(header1, \"_1\"), header2=paste0(header2, \"_2\"))\nmrg_fasta_out &lt;- do.call(paste, c(mrg_fasta, sep=\"\\n\")) %&gt;% paste(collapse=\"\\n\")\nwrite(mrg_fasta_out, file.path(data_dir, \"brumfield-putative-viral.fasta\"))\n\n\n\nCode# Import BLAST results\nblast_results_path &lt;- file.path(data_dir, \"blast/brumfield-putative-viral.blast.gz\")\nblast_cols &lt;- c(\"qseqid\", \"sseqid\", \"sgi\", \"staxid\", \"qlen\", \"evalue\", \"bitscore\", \"qcovs\", \"length\", \"pident\", \"mismatch\", \"gapopen\", \"sstrand\", \"qstart\", \"qend\", \"sstart\", \"send\")\nblast_results &lt;- read_tsv(blast_results_path, show_col_types = FALSE, \n                          col_names = blast_cols, col_types = cols(.default=\"c\"))\n\n# Add viral status\nblast_results_viral &lt;- mutate(blast_results, viral = staxid %in% viral_taxa$taxid)\n\n# Filter for best hit for each query/subject, then rank hits for each query\nblast_results_best &lt;- blast_results_viral %&gt;% group_by(qseqid, staxid) %&gt;% \n  filter(bitscore == max(bitscore)) %&gt;%\n  filter(length == max(length)) %&gt;% filter(row_number() == 1)\nblast_results_ranked &lt;- blast_results_best %&gt;% \n  group_by(qseqid) %&gt;% mutate(rank = dense_rank(desc(bitscore)))\nblast_results_highrank &lt;- blast_results_ranked %&gt;% filter(rank &lt;= 5) %&gt;%\n    mutate(read_pair = str_split(qseqid, \"_\") %&gt;% sapply(nth, n=-1), \n         seq_id = str_split(qseqid, \"_\") %&gt;% sapply(nth, n=1)) %&gt;%\n    mutate(bitscore = as.numeric(bitscore))\n\n# Summarize by read pair and taxid\nblast_results_paired &lt;- blast_results_highrank %&gt;%\n  group_by(seq_id, staxid, viral) %&gt;%\n  summarize(bitscore_max = max(bitscore), bitscore_min = min(bitscore),\n            n_reads = n(), .groups = \"drop\") %&gt;%\n  mutate(viral_full = viral & n_reads == 2)\n\n# Compare to Kraken & Bowtie assignments\nmrg_assign &lt;- mrg %&gt;% select(sample, seq_id, taxid, assigned_taxid, adj_score_max, na_type)\nblast_results_assign &lt;- left_join(blast_results_paired, mrg_assign, by=\"seq_id\") %&gt;%\n    mutate(taxid_match_bowtie = (staxid == taxid),\n           taxid_match_kraken = (staxid == assigned_taxid),\n           taxid_match_any = taxid_match_bowtie | taxid_match_kraken)\nblast_results_out &lt;- blast_results_assign %&gt;%\n  group_by(seq_id) %&gt;%\n  summarize(viral_status = ifelse(any(viral_full), 2,\n                                  ifelse(any(taxid_match_any), 2,\n                                             ifelse(any(viral), 1, 0))),\n            .groups = \"drop\")\n\n\n\nCode# Merge BLAST results with unenriched read data\nmrg_blast &lt;- full_join(mrg, blast_results_out, by=\"seq_id\") %&gt;%\n  mutate(viral_status = replace_na(viral_status, 0),\n         viral_status_out = ifelse(viral_status == 0, FALSE, TRUE))\nmrg_blast_rna &lt;- mrg_blast %&gt;% filter(na_type == \"RNA\")\nmrg_blast_dna &lt;- mrg_blast %&gt;% filter(na_type == \"DNA\")\n\n# Plot RNA\ng_mrg_blast_rna &lt;- mrg_blast_rna %&gt;%\n  ggplot(aes(x=adj_score_fwd, y=adj_score_rev, color=viral_status_out)) +\n  geom_point(alpha=0.5, shape=16) +\n  scale_x_continuous(name=\"S(forward read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_y_continuous(name=\"S(reverse read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_color_brewer(palette = \"Set1\", name = \"Viral status\") +\n  facet_grid(viral_status_out~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +\n  theme_base + labs(title=\"RNA\") + \n  theme(aspect.ratio=1, plot.title = element_text(size=rel(2), hjust=0))\ng_mrg_blast_rna\n\n\n\n\n\n\nCode# Plot DNA\ng_mrg_blast_dna &lt;- mrg_blast_dna %&gt;%\n  ggplot(aes(x=adj_score_fwd, y=adj_score_rev, color=viral_status_out)) +\n  geom_point(alpha=0.5, shape=16) +\n  scale_x_continuous(name=\"S(forward read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_y_continuous(name=\"S(reverse read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_color_brewer(palette = \"Set1\", name = \"Viral status\") +\n  facet_grid(viral_status_out~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +\n  theme_base + labs(title=\"DNA\") + \n  theme(aspect.ratio=1, plot.title = element_text(size=rel(2), hjust=0))\ng_mrg_blast_dna\n\n\n\n\n\n\n\n\nCodeg_hist_1 &lt;- ggplot(mrg_blast, aes(x=adj_score_max)) + geom_histogram(binwidth=5,boundary=0,position=\"dodge\") + facet_grid(na_type~viral_status_out, scales = \"free_y\") + scale_x_continuous(name = \"Maximum adjusted alignment score\") + scale_y_continuous(name=\"# Read pairs\") + scale_fill_brewer(palette = \"Dark2\") + theme_base + geom_vline(xintercept=20, linetype=\"dashed\", color=\"red\")\ng_hist_1\n\n\n\n\n\n\n\nThese results look very good on visual inspection, and indeed precision and sensitivity are both very high. For a disjunctive score threshold of 20, my updated workflow achieves an F1 score of 99.0% for RNA sequences and 99.2% for DNA sequences; more than high enough to continue on to human viral analysis.\n\nCodetest_sens_spec &lt;- function(tab, score_threshold, conjunctive, include_special){\n  if (!include_special) tab &lt;- filter(tab, viral_status_out %in% c(\"TRUE\", \"FALSE\"))\n  tab_retained &lt;- tab %&gt;% mutate(\n    conjunctive = conjunctive,\n    retain_score_conjunctive = (adj_score_fwd &gt; score_threshold & adj_score_rev &gt; score_threshold), \n    retain_score_disjunctive = (adj_score_fwd &gt; score_threshold | adj_score_rev &gt; score_threshold),\n    retain_score = ifelse(conjunctive, retain_score_conjunctive, retain_score_disjunctive),\n    retain = assigned_hv | hit_hv | retain_score) %&gt;%\n    group_by(viral_status_out, retain) %&gt;% count\n  pos_tru &lt;- tab_retained %&gt;% filter(viral_status_out == \"TRUE\", retain) %&gt;% pull(n) %&gt;% sum\n  pos_fls &lt;- tab_retained %&gt;% filter(viral_status_out != \"TRUE\", retain) %&gt;% pull(n) %&gt;% sum\n  neg_tru &lt;- tab_retained %&gt;% filter(viral_status_out != \"TRUE\", !retain) %&gt;% pull(n) %&gt;% sum\n  neg_fls &lt;- tab_retained %&gt;% filter(viral_status_out == \"TRUE\", !retain) %&gt;% pull(n) %&gt;% sum\n  sensitivity &lt;- pos_tru / (pos_tru + neg_fls)\n  specificity &lt;- neg_tru / (neg_tru + pos_fls)\n  precision   &lt;- pos_tru / (pos_tru + pos_fls)\n  f1 &lt;- 2 * precision * sensitivity / (precision + sensitivity)\n  out &lt;- tibble(threshold=score_threshold, include_special = include_special, \n                conjunctive = conjunctive, sensitivity=sensitivity, \n                specificity=specificity, precision=precision, f1=f1)\n  return(out)\n}\nrange_f1 &lt;- function(intab, inc_special, inrange=15:45){\n  tss &lt;- purrr::partial(test_sens_spec, tab=intab, include_special=inc_special)\n  stats_conj &lt;- lapply(inrange, tss, conjunctive=TRUE) %&gt;% bind_rows\n  stats_disj &lt;- lapply(inrange, tss, conjunctive=FALSE) %&gt;% bind_rows\n  stats_all &lt;- bind_rows(stats_conj, stats_disj) %&gt;%\n    pivot_longer(!(threshold:conjunctive), names_to=\"metric\", values_to=\"value\") %&gt;%\n    mutate(conj_label = ifelse(conjunctive, \"Conjunctive\", \"Disjunctive\"))\n  return(stats_all)\n}\ninc_special &lt;- FALSE\nstats_rna &lt;- range_f1(mrg_blast_rna, inc_special) %&gt;% mutate(na_type = \"RNA\")\nstats_dna &lt;- range_f1(mrg_blast_dna, inc_special) %&gt;% mutate(na_type = \"DNA\")\nstats_0 &lt;- bind_rows(stats_rna, stats_dna) %&gt;% mutate(attempt=0)\nthreshold_opt_0 &lt;- stats_0 %&gt;% group_by(conj_label,attempt,na_type) %&gt;% \n  filter(metric == \"f1\") %&gt;%\n  filter(value == max(value)) %&gt;% filter(threshold == min(threshold))\ng_stats_0 &lt;- ggplot(stats_0, aes(x=threshold, y=value, color=metric)) +\n  geom_vline(data = threshold_opt_0, mapping = aes(xintercept=threshold),\n             color = \"red\", linetype = \"dashed\") +\n  geom_line() +\n  scale_y_continuous(name = \"Value\", limits=c(0,1), breaks = seq(0,1,0.2), expand = c(0,0)) +\n  scale_x_continuous(name = \"Threshold\", expand = c(0,0)) +\n  scale_color_brewer(palette=\"Set3\") +\n  facet_grid(na_type~conj_label) +\n  theme_base\ng_stats_0\n\n\n\n\n\n\n\nHuman-infecting virus reads: analysis\n\nCode# Get raw read counts\nread_counts_raw &lt;- basic_stats_raw %&gt;%\n  select(sample, date, na_type, n_reads_raw = n_read_pairs)\n\n# Get HV read counts\nmrg_hv &lt;- mrg %&gt;% mutate(hv_status = assigned_hv | hit_hv | adj_score_max &gt;= 20)\nread_counts_hv &lt;- mrg_hv %&gt;% filter(hv_status) %&gt;% group_by(sample) %&gt;% count(name=\"n_reads_hv\")\nread_counts &lt;- read_counts_raw %&gt;% left_join(read_counts_hv, by=\"sample\") %&gt;%\n  mutate(n_reads_hv = replace_na(n_reads_hv, 0))\n\n# Aggregate\nread_counts_total &lt;- read_counts %&gt;% group_by(na_type) %&gt;%\n  summarize(n_reads_raw = sum(n_reads_raw),\n            n_reads_hv = sum(n_reads_hv)) %&gt;%\n  mutate(sample= \"All samples\", date = \"All dates\")\nread_counts_agg &lt;- read_counts %&gt;% arrange(date) %&gt;% \n  mutate(date = as.character(date)) %&gt;% arrange(sample) %&gt;%\n  bind_rows(read_counts_total) %&gt;% \n  mutate(sample = fct_inorder(sample),\n         date = fct_inorder(date),\n         p_reads_hv = n_reads_hv/n_reads_raw)\n\n# Get old counts\nn_hv_reads_old &lt;- c(691, 121, 18, 224, 2, 26, 6, 21, 4, 29, 12, 22)\nn_hv_reads_old[13] &lt;- sum(n_hv_reads_old[which(read_counts$na_type==\"RNA\")])\nn_hv_reads_old[14] &lt;- sum(n_hv_reads_old[which(read_counts$na_type==\"DNA\")])\n\nread_counts_comp &lt;- read_counts_agg %&gt;%\n  mutate(n_reads_hv_old = n_hv_reads_old,\n         p_reads_hv_old = n_reads_hv_old/n_reads_raw)\n\n\nApplying a disjunctive cutoff at S=20 identifies 13,809 RNA reads and 66 DNA reads as human-viral. This gives an overall relative HV abundance of \\(5.00 \\times 10^{-5}\\) for RNA reads and \\(3.66 \\times 10^{-7}\\) for DNA reads. In contrast, the overall HV in the public dashboard is \\(3.93 \\times 10^{-6}\\) for RNA reads and \\(4.54 \\times 10^{-7}\\); the measured HV has thus increased 12.7x for RNA reads, but decreased slightly for DNA reads.\n\nCode# Visualize\ng_phv_agg &lt;- ggplot(read_counts_agg, aes(x=date, color=na_type)) +\n  geom_point(aes(y=p_reads_hv)) +\n  scale_y_log10(\"Relative abundance of human virus reads\") +\n  scale_color_na() + theme_kit\ng_phv_agg\n\n\n\n\n\n\n\nDigging into individual viruses, we see that fecal-oral viruses predominate, especially Mamastrovirus, Rotavirus, Salivirus, and fecal-oral Enterovirus species (especially Enterovirus C, which includes poliovirus):\n\nCode# Get viral taxon names for putative HV reads\nviral_taxa$name[viral_taxa$taxid == 249588] &lt;- \"Mamastrovirus\"\nviral_taxa$name[viral_taxa$taxid == 194960] &lt;- \"Kobuvirus\"\nviral_taxa$name[viral_taxa$taxid == 688449] &lt;- \"Salivirus\"\nviral_taxa$name[viral_taxa$taxid == 585893] &lt;- \"Picobirnaviridae\"\nviral_taxa$name[viral_taxa$taxid == 333922] &lt;- \"Betapapillomavirus\"\n\nviral_taxa$name[viral_taxa$taxid == 334207] &lt;- \"Betapapillomavirus 3\"\nviral_taxa$name[viral_taxa$taxid == 369960] &lt;- \"Porcine type-C oncovirus\"\nviral_taxa$name[viral_taxa$taxid == 333924] &lt;- \"Betapapillomavirus 2\"\n\nmrg_hv_named &lt;- mrg_hv %&gt;% left_join(viral_taxa, by=\"taxid\")\n\n# Discover viral species & genera for HV reads\nraise_rank &lt;- function(read_db, taxid_db, out_rank = \"species\", verbose = FALSE){\n  # Get higher ranks than search rank\n  ranks &lt;- c(\"subspecies\", \"species\", \"subgenus\", \"genus\", \"subfamily\", \"family\", \"suborder\", \"order\", \"class\", \"subphylum\", \"phylum\", \"kingdom\", \"superkingdom\")\n  rank_match &lt;- which.max(ranks == out_rank)\n  high_ranks &lt;- ranks[rank_match:length(ranks)]\n  # Merge read DB and taxid DB\n  reads &lt;- read_db %&gt;% select(-parent_taxid, -rank, -name) %&gt;%\n    left_join(taxid_db, by=\"taxid\")\n  # Extract sequences that are already at appropriate rank\n  reads_rank &lt;- filter(reads, rank == out_rank)\n  # Drop sequences at a higher rank and return unclassified sequences\n  reads_norank &lt;- reads %&gt;% filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))\n  while(nrow(reads_norank) &gt; 0){ # As long as there are unclassified sequences...\n    # Promote read taxids and re-merge with taxid DB, then re-classify and filter\n    reads_remaining &lt;- reads_norank %&gt;% mutate(taxid = parent_taxid) %&gt;%\n      select(-parent_taxid, -rank, -name) %&gt;%\n      left_join(taxid_db, by=\"taxid\")\n    reads_rank &lt;- reads_remaining %&gt;% filter(rank == out_rank) %&gt;%\n      bind_rows(reads_rank)\n    reads_norank &lt;- reads_remaining %&gt;%\n      filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))\n  }\n  # Finally, extract and append reads that were excluded during the process\n  reads_dropped &lt;- reads %&gt;% filter(!seq_id %in% reads_rank$seq_id)\n  reads_out &lt;- reads_rank %&gt;% bind_rows(reads_dropped) %&gt;%\n    select(-parent_taxid, -rank, -name) %&gt;%\n    left_join(taxid_db, by=\"taxid\")\n  return(reads_out)\n}\nhv_reads_species &lt;- raise_rank(mrg_hv_named, viral_taxa, \"species\")\nhv_reads_genus &lt;- raise_rank(mrg_hv_named, viral_taxa, \"genus\")\nhv_reads_family &lt;- raise_rank(mrg_hv_named, viral_taxa, \"family\")\n\n\n\nCode# Count reads for each human-viral family\nhv_family_counts &lt;- hv_reads_family %&gt;% \n  group_by(sample, date, na_type, name, taxid) %&gt;%\n  count(name = \"n_reads_hv\") %&gt;%\n  group_by(sample, date, na_type) %&gt;%\n  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))\n\n# Identify high-ranking families and group others\nhv_family_major_tab &lt;- hv_family_counts %&gt;% group_by(name) %&gt;% \n  filter(p_reads_hv == max(p_reads_hv)) %&gt;% filter(row_number() == 1) %&gt;%\n  arrange(desc(p_reads_hv)) %&gt;% filter(p_reads_hv &gt; 0.01)\nhv_family_counts_major &lt;- hv_family_counts %&gt;%\n  mutate(name_display = ifelse(name %in% hv_family_major_tab$name, name, \"Other\")) %&gt;%\n  group_by(sample, date, na_type, name_display) %&gt;%\n  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), \n            .groups=\"drop\") %&gt;%\n  mutate(name_display = factor(name_display, \n                               levels = c(hv_family_major_tab$name, \"Other\")))\n\n# Plot family composition\npalette &lt;- c(brewer.pal(12, \"Set3\"), \"#AAAAAA\")\ng_hv_families &lt;- ggplot(hv_family_counts_major, \n                        aes(x=date, y=p_reads_hv, fill=name_display)) +\n  geom_col(position=\"stack\") +\n  scale_fill_manual(name=\"Viral\\nfamily\", values=palette) +\n  scale_y_continuous(name=\"% HV Reads\", limits=c(0,1), breaks=seq(0,1,0.2)) +\n  facet_grid(.~na_type) +\n  guides(fill=guide_legend(ncol=3)) +\n  theme_kit\ng_hv_families\n\n\n\n\n\n\n\n\nCode# Count reads for each human-viral genus\nhv_genus_counts &lt;- hv_reads_genus %&gt;% \n  group_by(sample, date, na_type, name, taxid) %&gt;%\n  count(name = \"n_reads_hv\") %&gt;%\n  group_by(sample, date, na_type) %&gt;%\n  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))\n\n# Identify high-ranking genera and group others\nhv_genus_major_tab &lt;- hv_genus_counts %&gt;% group_by(name) %&gt;% \n  filter(p_reads_hv == max(p_reads_hv)) %&gt;% filter(row_number() == 1) %&gt;%\n  arrange(desc(p_reads_hv)) %&gt;% filter(p_reads_hv &gt; 0.02)\nhv_genus_counts_major &lt;- hv_genus_counts %&gt;%\n  mutate(name_display = ifelse(name %in% hv_genus_major_tab$name, name, \"Other\")) %&gt;%\n  group_by(sample, date, na_type, name_display) %&gt;%\n  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), \n            .groups=\"drop\") %&gt;%\n  mutate(name_display = factor(name_display, \n                               levels = c(hv_genus_major_tab$name, \"Other\")))\n\n# Plot genus composition\npalette &lt;- c(brewer.pal(12, \"Set3\"), \"#AAAAAA\")\ng_hv_genera &lt;- ggplot(hv_genus_counts_major, \n                        aes(x=date, y=p_reads_hv, fill=name_display)) +\n  geom_col(position=\"stack\") +\n  scale_fill_manual(name=\"Viral\\ngenus\", values=palette) +\n  scale_y_continuous(name=\"% HV Reads\", limits=c(0,1), breaks=seq(0,1,0.2)) +\n  facet_grid(.~na_type) +\n  guides(fill=guide_legend(ncol=3)) +\n  theme_kit\ng_hv_genera\n\n\n\n\n\n\n\n\nCode# Count reads for each human-viral species\nhv_species_counts &lt;- hv_reads_species %&gt;% \n  group_by(sample, date, na_type, name, taxid) %&gt;%\n  count(name = \"n_reads_hv\") %&gt;%\n  group_by(sample, date, na_type) %&gt;%\n  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))\n\n# Identify high-ranking species and group others\nhv_species_major_tab &lt;- hv_species_counts %&gt;% group_by(name, taxid) %&gt;% \n  filter(p_reads_hv == max(p_reads_hv)) %&gt;% filter(row_number() == 1) %&gt;%\n  arrange(desc(p_reads_hv)) %&gt;% filter(p_reads_hv &gt; 0.05)\nhv_species_counts_major &lt;- hv_species_counts %&gt;%\n  mutate(name_display = ifelse(name %in% hv_species_major_tab$name, name, \"Other\")) %&gt;%\n  group_by(sample, date, na_type, name_display) %&gt;%\n  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), \n            .groups=\"drop\") %&gt;%\n  mutate(name_display = factor(name_display, \n                               levels = c(hv_species_major_tab$name, \"Other\")))\n\n# Plot species composition\npalette &lt;- c(brewer.pal(12, \"Set3\"), \"#AAAAAA\")\ng_hv_species &lt;- ggplot(hv_species_counts_major, \n                      aes(x=date, y=p_reads_hv, fill=name_display)) +\n  geom_col(position=\"stack\") +\n  scale_fill_manual(name=\"Viral\\nspecies\", values=palette) +\n  scale_y_continuous(name=\"% HV Reads\", limits=c(0,1), breaks=seq(0,1,0.2)) +\n  facet_grid(.~na_type) +\n  guides(fill=guide_legend(ncol=3)) +\n  theme_kit\ng_hv_species\n\n\n\n\n\n\n\nBy far the most common virus according to my pipeline (with over 91% of human-viral reads) is Rotavirus A; second (with 8%) is Orthopicobirnavirus hominis. Together, these two enteric RNA viruses dominate HV RNA reads in all samples. Both appear to be real; at least, BLASTN primarily maps these reads to the same or closely-related viruses to that identified by Bowtie2:\n\nCode# Import names db\ntax_names_path &lt;- file.path(data_dir, \"taxid-names.tsv.gz\")\ntax_names &lt;- read_tsv(tax_names_path, show_col_types = FALSE)\n\n# Add missing names\ntax_names_new &lt;- tibble(\n  staxid = c(557241, 557245, 557232, 557247, 557242, 557245),\n  name = c(\"Canine rotavirus A79-10/G3P[3]\", \"Human rotavirus Ro1845\", \"Canine rotavirus K9\",\n           \"Human rotavirus HCR3A\", \"Feline rotavirus Cat97/G3P[3]\", \"Feline rotavirus Cat2/G3P[9]\")\n)\ntax_names &lt;- bind_rows(tax_names, tax_names_new)\n\nrota_ids &lt;- hv_reads_species %&gt;% filter(taxid==28875) %&gt;% pull(seq_id)\nrota_hits &lt;- blast_results_paired %&gt;%\n  filter(seq_id %in% rota_ids) %&gt;%\n  mutate(staxid = as.integer(staxid))\nrota_hits_sorted &lt;- rota_hits %&gt;% group_by(staxid) %&gt;% count %&gt;% arrange(desc(n)) %&gt;%\n  left_join(tax_names, by=\"staxid\") %&gt;%\n  mutate(name = fct_inorder(name))\nrota_hits_sorted_head &lt;- rota_hits_sorted %&gt;% head(10) %&gt;%\n  mutate(name=fct_inorder(as.character(name)))\ng_rota &lt;- ggplot(rota_hits_sorted_head,\n                  aes(x=n, y=fct_inorder(name))) + geom_col() +\n  scale_x_continuous(name = \"# Mapped read pairs\") + theme_base +\n  theme(axis.title.y = element_blank())\ng_rota\n\n\n\n\n\n\n\n\nCode# Add missing names\ntax_names_new &lt;- tibble(\n  staxid = c(442302, 3003954, 585895),\n  name = c(\"Porcine picobirnavirus\", \"ticpantry virus 7\", \"uncultured picobirnavirus\")\n)\ntax_names &lt;- bind_rows(tax_names, tax_names_new)\n\npico_ids &lt;- hv_reads_species %&gt;% filter(taxid==2956252) %&gt;% pull(seq_id)\npico_hits &lt;- blast_results_paired %&gt;%\n  filter(seq_id %in% pico_ids) %&gt;%\n  mutate(staxid = as.integer(staxid))\npico_hits_sorted &lt;- pico_hits %&gt;% group_by(staxid) %&gt;% count %&gt;% arrange(desc(n)) %&gt;%\n  left_join(tax_names, by=\"staxid\") %&gt;%\n  mutate(name = fct_inorder(name))\npico_hits_sorted_head &lt;- pico_hits_sorted %&gt;% head(10) %&gt;%\n  mutate(name=fct_inorder(as.character(name)))\ng_pico &lt;- ggplot(pico_hits_sorted_head,\n                 aes(x=n, y=fct_inorder(name))) + geom_col() +\n  scale_x_continuous(name = \"# Mapped read pairs\") + theme_base +\n  theme(axis.title.y = element_blank())\ng_pico\n\n\n\n\n\n\n\nConclusion\nI’m quite happy with the quality of the human-viral selection process for this dataset, which ended up achieving very high precision and sensitivity. The most striking result coming out of this analysis is probably the drastic difference in total HV abundance between RNA and DNA reads, with the former &gt;100x higher despite very similar processing methods. In the past we’ve attributed low HV abundance in the DNA datasets we’ve analyzed to the lack of viral enrichment in available DNA datasets; in this case, however, there is very little difference between the DNA and RNA processing methods, so this explanation doesn’t really hold. This makes me more pessimistic about achieving good HV relative abundance with DNA sequencing, even with improved viral enrichment methods."
+  },
+  {
+    "objectID": "notebooks/2024-04-12_prussin.html",
+    "href": "notebooks/2024-04-12_prussin.html",
+    "title": "Workflow analysis of Prussin et al. (2019)",
+    "section": "",
+    "text": "Taking a break from working on P2RA datasets, we’re also working on a review of air sampling for viral pathogen detection. For that study, we’re collecting and analyzing air MGS data that could give us a high-level idea of the likely viral composition of such samples.\nThe first dataset I’m looking at for this work is Prussin et al. (2019), a study of HVAC filter samples in a Virginia daycare center between 2014 and 2015. Samples were eluted from MERV-14 air filters collected every two weeks, with pairs of successive samples combined into four-week sampling periods. Like Brumfield et al, this study conducted both RNA and DNA sequencing; all samples were sequenced on an Illumina NextSeq500 with 2x150bp reads.\nThe raw data\nThe Prussin dataset comprised sequencing data from 14 timepoints spread across the year, from 20th January 2014 to 2nd February 2015. Each sample represents a four-week sampling period. In addition to the 14 on-site samples, there were also two control samples, a negative control (NC) and an “unexposed filter” control (UFC), which were collected on December 23rd 2014.\n\nCode# Data input paths\ndata_dir &lt;- \"../data/2024-04-11_prussin/\"\nlibraries_path &lt;- file.path(data_dir, \"sample-metadata.csv\")\nbasic_stats_path &lt;- file.path(data_dir, \"qc_basic_stats.tsv\")\nadapter_stats_path &lt;- file.path(data_dir, \"qc_adapter_stats.tsv\")\nquality_base_stats_path &lt;- file.path(data_dir, \"qc_quality_base_stats.tsv\")\nquality_seq_stats_path &lt;- file.path(data_dir, \"qc_quality_sequence_stats.tsv\")\n\n# Import libraries and extract metadata from sample names\nlibraries_raw &lt;- read_csv(libraries_path, show_col_types = FALSE)\nlibraries &lt;- libraries_raw %&gt;%\n  arrange(desc(na_type)) %&gt;% mutate(na_type = fct_inorder(na_type)) %&gt;%\n  arrange(date) %&gt;% rename(start_date = date) %&gt;%\n  mutate(end_date = start_date + 28) %&gt;%\n  mutate(date = fct_inorder(as.character(end_date)),\n         ctrl = ifelse(grepl(\"Negative_Control\", sample_alias), \"NC\",\n                       ifelse(grepl(\"Unexposed_Filter\", sample_alias),\n                              \"UFC\", \"Non-Control\")),\n         ctrl = factor(ctrl, levels = c(\"Non-Control\",\n                                        \"NC\",\n                                        \"UFC\")),\n         open = (season != \"Closed\") & (ctrl == \"On-Site\"))\n\n# Import QC data\nstages &lt;- c(\"raw_concat\", \"cleaned\", \"dedup\", \"ribo_initial\", \"ribo_secondary\")\nbasic_stats &lt;- read_tsv(basic_stats_path, show_col_types = FALSE) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;% \n  mutate(stage = factor(stage, levels = stages),\n         sample = fct_inorder(sample))\nadapter_stats &lt;- read_tsv(adapter_stats_path, show_col_types = FALSE) %&gt;%\n    mutate(sample = sub(\"_2$\", \"\", sample)) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;%\n  mutate(stage = factor(stage, levels = stages),\n         read_pair = fct_inorder(as.character(read_pair)))\nquality_base_stats &lt;- read_tsv(quality_base_stats_path, show_col_types = FALSE) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;%\n  mutate(stage = factor(stage, levels = stages),\n         read_pair = fct_inorder(as.character(read_pair)))\nquality_seq_stats &lt;- read_tsv(quality_seq_stats_path, show_col_types = FALSE) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;%\n  mutate(stage = factor(stage, levels = stages),\n         read_pair = fct_inorder(as.character(read_pair)))\n\n# Filter to raw data\nbasic_stats_raw &lt;- basic_stats %&gt;% filter(stage == \"raw_concat\")\nadapter_stats_raw &lt;- adapter_stats %&gt;% filter(stage == \"raw_concat\")\nquality_base_stats_raw &lt;- quality_base_stats %&gt;% filter(stage == \"raw_concat\")\nquality_seq_stats_raw &lt;- quality_seq_stats %&gt;% filter(stage == \"raw_concat\")\n\n# Get key values for readout\nraw_read_counts &lt;- basic_stats_raw %&gt;% group_by(na_type, ctrl) %&gt;% \n  summarize(rmin = min(n_read_pairs), rmax=max(n_read_pairs),\n            rmean=mean(n_read_pairs), .groups = \"drop\")\nraw_read_totals &lt;- basic_stats_raw %&gt;% group_by(na_type, ctrl) %&gt;% \n  summarize(n_read_pairs = sum(n_read_pairs), \n            n_bases_approx = sum(n_bases_approx), .groups = \"drop\")\nraw_dup &lt;- basic_stats_raw %&gt;% group_by(na_type, ctrl) %&gt;% \n  summarize(dmin = min(percent_duplicates), dmax=max(percent_duplicates),\n            dmean=mean(percent_duplicates), .groups = \"drop\")\n\n\nThe 14 positive samples from the dataset yielded 5M-20M (mean 11.3M) RNA-sequencing reads and 10M-42M (mean 21.0M) DNA-sequencing reads per sample, for a total of 159M RNA read pairs and 294M DNA read pairs (46.3 and 87.0 gigabases of sequence, respectively). Controls contributed an additional 1M RNA read pairs and 17.5M DNA read pairs.\nIn positive samples, read qualities were mostly high but tailed off slightly at the 3’ end in some samples, suggesting the need for trimming. Adapter levels were high. Inferred duplication levels were low (2-4%) in DNA reads and moderate (21-44%) in RNA reads. Control sample reads were more problematic, with higher duplication and adapter levels and lower quality.\n\nCode# Prepare data\nbasic_stats_raw_metrics &lt;- basic_stats_raw %&gt;%\n  select(date, na_type, ctrl,\n         `# Read pairs` = n_read_pairs,\n         `Total base pairs\\n(approx)` = n_bases_approx,\n         `% Duplicates\\n(FASTQC)` = percent_duplicates) %&gt;%\n  pivot_longer(-(date:ctrl), names_to = \"metric\", values_to = \"value\") %&gt;%\n  mutate(metric = fct_inorder(metric))\n\n# Set up plot templates\nscale_fill_na &lt;- purrr::partial(scale_fill_brewer, palette=\"Set1\", \n                                name=\"Nucleic acid type\")\ng_basic &lt;- ggplot(basic_stats_raw_metrics, \n                  aes(x=date, y=value, fill=na_type)) +\n  geom_col(position = \"dodge\") +\n  scale_x_discrete(name=\"Collection Date\") +\n  scale_y_continuous(expand=c(0,0)) +\n  expand_limits(y=c(0,100)) +\n  scale_fill_na() + \n  facet_grid(metric~ctrl, scales = \"free\", space=\"free_x\", switch=\"y\") +\n  theme_rotate + theme(\n    axis.title.y = element_blank(),\n    strip.text.y = element_text(face=\"plain\")\n  )\ng_basic\n\n\n\n\n\n\n\n\nCode# Set up plotting templates\nscale_color_na &lt;- purrr::partial(scale_color_brewer, palette=\"Set1\",\n                                 name=\"Nucleic acid type\")\ng_qual_raw &lt;- ggplot(mapping=aes(color=na_type, linetype=read_pair, \n                         group=interaction(sample,read_pair))) + \n  scale_color_na() + scale_linetype_discrete(name = \"Read Pair\") +\n  guides(color=guide_legend(nrow=2,byrow=TRUE),\n         linetype = guide_legend(nrow=2,byrow=TRUE)) +\n  theme_base\n\n# Visualize adapters\ng_adapters_raw &lt;- g_qual_raw + \n  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats_raw) +\n  scale_y_continuous(name=\"% Adapters\", limits=c(0,NA),\n                     breaks = seq(0,100,10), expand=c(0,0)) +\n  scale_x_continuous(name=\"Position\", limits=c(0,NA),\n                     breaks=seq(0,140,20), expand=c(0,0)) +\n  facet_grid(ctrl~adapter)\ng_adapters_raw\n\n\n\n\n\n\nCode# Visualize quality\ng_quality_base_raw &lt;- g_qual_raw +\n  geom_hline(yintercept=25, linetype=\"dashed\", color=\"red\") +\n  geom_hline(yintercept=30, linetype=\"dashed\", color=\"red\") +\n  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats_raw) +\n  scale_y_continuous(name=\"Mean Phred score\", expand=c(0,0), limits=c(10,45)) +\n  scale_x_continuous(name=\"Position\", limits=c(0,NA),\n                     breaks=seq(0,140,20), expand=c(0,0)) +\n  facet_grid(ctrl~.)\ng_quality_base_raw\n\n\n\n\n\n\nCodeg_quality_seq_raw &lt;- g_qual_raw +\n  geom_vline(xintercept=25, linetype=\"dashed\", color=\"red\") +\n  geom_vline(xintercept=30, linetype=\"dashed\", color=\"red\") +\n  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats_raw) +\n  scale_x_continuous(name=\"Mean Phred score\", expand=c(0,0)) +\n  scale_y_continuous(name=\"# Sequences\", expand=c(0,0)) +\n  facet_grid(ctrl~., scales = \"free_y\")\ng_quality_seq_raw\n\n\n\n\n\n\n\nPreprocessing\nThe average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. For positive samples, on average, cleaning and deduplication removed about 19% of total read pairs from RNA libraries and about 7% from DNA libraries. Subsequent ribodepletion removed a further ~32% of total read pairs on average from RNA libraries but &lt;0.5% of total read pairs from DNA libraries.\nControl samples, meanwhile, lost an average of 63% of RNA read pairs and 21% of DNA read pairs during cleaning and deduplication, consistent with the lower qualities observed above. Subsequent ribodepletion removed an additional 9% of total RNA read pairs and 0.3% of total DNA read pairs.\n\nCoden_reads_rel &lt;- basic_stats %&gt;% \n  select(sample, date, na_type, ctrl, stage, \n         percent_duplicates, n_read_pairs) %&gt;%\n  group_by(sample, na_type) %&gt;% arrange(sample, na_type, stage) %&gt;%\n  mutate(p_reads_retained = replace_na(n_read_pairs / lag(n_read_pairs), 0),\n         p_reads_lost = 1 - p_reads_retained,\n         p_reads_retained_abs = n_read_pairs / n_read_pairs[1],\n         p_reads_lost_abs = 1-p_reads_retained_abs,\n         p_reads_lost_abs_marginal = replace_na(p_reads_lost_abs - lag(p_reads_lost_abs), 0))\nn_reads_rel_display &lt;- n_reads_rel %&gt;% \n  rename(Stage=stage, `NA Type`=na_type, `Control?`=ctrl) %&gt;% \n  group_by(`Control?`, `NA Type`, Stage) %&gt;% \n  summarize(`% Total Reads Lost (Cumulative)` = paste0(round(min(p_reads_lost_abs*100),1), \"-\", round(max(p_reads_lost_abs*100),1), \" (mean \", round(mean(p_reads_lost_abs*100),1), \")\"),\n            `% Total Reads Lost (Marginal)` = paste0(round(min(p_reads_lost_abs_marginal*100),1), \"-\", round(max(p_reads_lost_abs_marginal*100),1), \" (mean \", round(mean(p_reads_lost_abs_marginal*100),1), \")\"), .groups=\"drop\") %&gt;% \n  filter(Stage != \"raw_concat\") %&gt;%\n  mutate(Stage = Stage %&gt;% as.numeric %&gt;% factor(labels=c(\"Trimming & filtering\", \"Deduplication\", \"Initial ribodepletion\", \"Secondary ribodepletion\")))\nn_reads_rel_display\n\n\n  \n\n\n\n\nCodeg_stage_trace &lt;- ggplot(basic_stats, aes(x=stage, color=na_type, group=sample)) +\n  scale_color_na() +\n  facet_wrap(ctrl~na_type, scales=\"free\", ncol=2,\n             labeller = label_wrap_gen(multi_line=FALSE)) +\n  theme_kit\n\n# Plot reads over preprocessing\ng_reads_stages &lt;- g_stage_trace +\n  geom_line(aes(y=n_read_pairs)) +\n  scale_y_continuous(\"# Read pairs\", expand=c(0,0), limits=c(0,NA))\ng_reads_stages\n\n\n\n\n\n\nCode# Plot relative read losses during preprocessing\ng_reads_rel &lt;- ggplot(n_reads_rel, aes(x=stage, color=na_type, group=sample)) +\n  geom_line(aes(y=p_reads_lost_abs_marginal)) +\n  scale_y_continuous(\"% Total Reads Lost\", expand=c(0,0), \n                     labels = function(x) x*100) +\n  scale_color_na() +\n  facet_wrap(ctrl~na_type, scales=\"free\", ncol=2,\n             labeller = label_wrap_gen(multi_line=FALSE)) +\n  theme_kit\ng_reads_rel\n\n\n\n\n\n\n\nIn both positive and control samples, data cleaning with FASTP was very successful at removing adapters, with very few adapter sequences found by FASTQC at any stage after the raw data. FASTP was also successful at improving read quality.\n\nCodeg_qual &lt;- ggplot(mapping=aes(color=na_type, linetype=read_pair, \n                         group=interaction(sample,read_pair))) + \n  scale_color_na() + scale_linetype_discrete(name = \"Read Pair\") +\n  guides(color=guide_legend(nrow=2,byrow=TRUE),\n         linetype = guide_legend(nrow=2,byrow=TRUE)) +\n  theme_base\n\n# Visualize adapters\ng_adapters &lt;- g_qual + \n  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats) +\n  scale_y_continuous(name=\"% Adapters\", limits=c(0,20),\n                     breaks = seq(0,50,10), expand=c(0,0)) +\n  scale_x_continuous(name=\"Position\", limits=c(0,NA),\n                     breaks=seq(0,140,20), expand=c(0,0)) +\n  facet_grid(stage~adapter)\ng_adapters\n\n\n\n\n\n\nCode# Visualize quality\ng_quality_base &lt;- g_qual +\n  geom_hline(yintercept=25, linetype=\"dashed\", color=\"red\") +\n  geom_hline(yintercept=30, linetype=\"dashed\", color=\"red\") +\n  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats) +\n  scale_y_continuous(name=\"Mean Phred score\", expand=c(0,0), limits=c(10,45)) +\n  scale_x_continuous(name=\"Position\", limits=c(0,NA),\n                     breaks=seq(0,140,20), expand=c(0,0)) +\n  facet_grid(stage~.)\ng_quality_base\n\n\n\n\n\n\nCodeg_quality_seq &lt;- g_qual +\n  geom_vline(xintercept=25, linetype=\"dashed\", color=\"red\") +\n  geom_vline(xintercept=30, linetype=\"dashed\", color=\"red\") +\n  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats) +\n  scale_x_continuous(name=\"Mean Phred score\", expand=c(0,0)) +\n  scale_y_continuous(name=\"# Sequences\", expand=c(0,0)) +\n  facet_grid(stage~.)\ng_quality_seq\n\n\n\n\n\n\n\nAccording to FASTQC, deduplication was moderately effective at reducing measured duplicate levels in on-site samples, with FASTQC-measured levels falling from an average of 34% to 23% for RNA reads and from 2.7% to 2.2% for DNA reads.\n\nCodestage_dup &lt;- basic_stats %&gt;% group_by(na_type, ctrl, stage) %&gt;% \n  summarize(dmin = min(percent_duplicates), dmax=max(percent_duplicates),\n            dmean=mean(percent_duplicates), .groups = \"drop\")\n\ng_dup_stages &lt;- g_stage_trace +\n  geom_line(aes(y=percent_duplicates)) +\n  scale_y_continuous(\"% Duplicates\", limits=c(0,NA), expand=c(0,0))\ng_dup_stages\n\n\n\n\n\n\nCodeg_readlen_stages &lt;- g_stage_trace + geom_line(aes(y=mean_seq_len)) +\n  scale_y_continuous(\"Mean read length (nt)\", expand=c(0,0), limits=c(0,NA))\ng_readlen_stages\n\n\n\n\n\n\n\nHigh-level composition\nAs before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:\n\nCode# Import Bracken data\nbracken_path &lt;- file.path(data_dir, \"bracken_counts.tsv\")\nbracken &lt;- read_tsv(bracken_path, show_col_types = FALSE)\ntotal_assigned &lt;- bracken %&gt;% group_by(sample) %&gt;% summarize(\n  name = \"Total\",\n  kraken_assigned_reads = sum(kraken_assigned_reads),\n  added_reads = sum(added_reads),\n  new_est_reads = sum(new_est_reads),\n  fraction_total_reads = sum(fraction_total_reads)\n)\nbracken_spread &lt;- bracken %&gt;% select(name, sample, new_est_reads) %&gt;%\n  mutate(name = tolower(name)) %&gt;%\n  pivot_wider(id_cols = \"sample\", names_from = \"name\", \n              values_from = \"new_est_reads\")\n\n# Count reads\nread_counts_preproc &lt;- basic_stats %&gt;% \n  select(sample, na_type, ctrl, date, stage, n_read_pairs) %&gt;%\n  pivot_wider(id_cols = c(\"sample\", \"na_type\", \"ctrl\", \"date\"),\n              names_from=\"stage\", values_from=\"n_read_pairs\")\nread_counts &lt;- read_counts_preproc %&gt;%\n  inner_join(total_assigned %&gt;% select(sample, new_est_reads), by = \"sample\") %&gt;%\n  rename(assigned = new_est_reads) %&gt;%\n  inner_join(bracken_spread, by=\"sample\")\n\n# Assess composition\nread_comp &lt;- transmute(read_counts, sample=sample, na_type=na_type,\n                       ctrl = ctrl, date = date,\n                       n_filtered = raw_concat-cleaned,\n                       n_duplicate = cleaned-dedup,\n                       n_ribosomal = (dedup-ribo_initial) + (ribo_initial-ribo_secondary),\n                       n_unassigned = ribo_secondary-assigned,\n                       n_bacterial = bacteria,\n                       n_archaeal = archaea,\n                       n_viral = viruses,\n                       n_human = eukaryota)\nread_comp_long &lt;- pivot_longer(read_comp, -(sample:date), \n                               names_to = \"classification\",\n                               names_prefix = \"n_\", values_to = \"n_reads\") %&gt;%\n  mutate(classification = fct_inorder(str_to_sentence(classification))) %&gt;%\n  group_by(sample) %&gt;% mutate(p_reads = n_reads/sum(n_reads))\n\n# Summarize composition\nread_comp_summ &lt;- read_comp_long %&gt;% \n  group_by(na_type, ctrl, classification) %&gt;%\n  summarize(n_reads = sum(n_reads), .groups = \"drop_last\") %&gt;%\n  mutate(n_reads = replace_na(n_reads,0),\n    p_reads = n_reads/sum(n_reads),\n    pc_reads = p_reads*100)\n\n\n\nCode# Prepare plotting templates\ng_comp_base &lt;- ggplot(mapping=aes(x=date, y=p_reads, fill=classification)) +\n  scale_x_discrete(name=\"Collection Date\") +\n  facet_grid(na_type~ctrl, scales = \"free\", space = \"free_x\") +\n  theme_rotate\nscale_y_pc_reads &lt;- purrr::partial(scale_y_continuous, name = \"% Reads\",\n                                   expand = c(0,0), labels = function(y) y*100)\n\n# Plot overall composition\ng_comp &lt;- g_comp_base + geom_col(data = read_comp_long, position = \"stack\") +\n  scale_y_pc_reads(limits = c(0,1.01), breaks = seq(0,1,0.2)) +\n  scale_fill_brewer(palette = \"Set1\", name = \"Classification\")\ng_comp\n\n\n\n\n\n\nCode# Plot composition of minor components\nread_comp_minor &lt;- read_comp_long %&gt;% \n  filter(classification %in% c(\"Archaeal\", \"Viral\", \"Other\"))\npalette_minor &lt;- brewer.pal(9, \"Set1\")[c(6,7,9)]\ng_comp_minor &lt;- g_comp_base + geom_col(data=read_comp_minor, position = \"stack\") +\n  scale_y_pc_reads() +\n  scale_fill_manual(values=palette_minor, name = \"Classification\")\ng_comp_minor\n\n\n\n\n\n\n\n\nCodep_reads_summ_group &lt;- read_comp_long %&gt;%\n  mutate(classification = ifelse(classification %in% c(\"Filtered\", \"Duplicate\", \"Unassigned\"), \"Excluded\", as.character(classification)),\n         classification = fct_inorder(classification)) %&gt;%\n  group_by(classification, sample, na_type, ctrl) %&gt;%\n  summarize(p_reads = sum(p_reads), .groups = \"drop\") %&gt;%\n  group_by(classification, na_type, ctrl) %&gt;%\n  summarize(pc_min = min(p_reads)*100, pc_max = max(p_reads)*100, \n            pc_mean = mean(p_reads)*100, .groups = \"drop\")\np_reads_summ_prep &lt;- p_reads_summ_group %&gt;%\n  mutate(classification = fct_inorder(classification),\n         pc_min = pc_min %&gt;% signif(digits=2) %&gt;% sapply(format, scientific=FALSE, trim=TRUE, digits=2),\n         pc_max = pc_max %&gt;% signif(digits=2) %&gt;% sapply(format, scientific=FALSE, trim=TRUE, digits=2),\n         pc_mean = pc_mean %&gt;% signif(digits=2) %&gt;% sapply(format, scientific=FALSE, trim=TRUE, digits=2),\n         display = paste0(pc_min, \"-\", pc_max, \"% (mean \", pc_mean, \"%)\"))\np_reads_summ &lt;- p_reads_summ_prep %&gt;%\n  select(ctrl, classification, na_type, display) %&gt;%\n  pivot_wider(names_from=na_type, values_from = display) %&gt;% \n  arrange(ctrl, classification)\np_reads_summ\n\n\n  \n\n\n\nWhat we see is a very different picture from the wastewater samples I’ve been analyzing so far. Most notably, the fraction of (non-ribosomal) human reads is much higher. Even the 2015-01-20 samples, which were taken when the daycare center was closed for the winter holidays, showed human read fractions (for both nucleic-acid types) of &gt;12%; non-control samples as a whole averaged 27% for RNA reads and 34% for DNA reads. Compare Brumfield (average 0.08% for RNA and 0.02% for DNA), Yang (mean 0.05% for RNA) or even Rothman (mean 1.8% for non-panel-enriched RNA samples).\nConversely, total viral reads are very low: mean 0.033% for RNA reads and 0.019% for DNA reads. Wastewater RNA datasets have typically had much higher total viruses: mean 0.5% for Brumfield, about the same for Crits-Christoph, 5.5% for Yang, 4.5% for Rothman. Brumfield’s DNA data contained substantially fewer viruses than their RNA data, but still more than Prussin: about 0.08% on average.\nLooking at viral families…was less informative than usual, especially for DNA reads. It turns out that these samples contain a lot of viral reads that Kraken2 was only able to classify to the class level. In DNA reads, samples were dominated by Caudoviricetes phages, though Herviviricetes (which includes herpesviruses) and Naldaviricetes (a class of arthropod-infecting viruses) also put in a respectable showing. In RNA reads, Caudoviricetes was again a major presence, but Alsuviricetes (a family of primarily plant pathogens) was often as or more prevalent, and Revtraviricetes (a class that includes Hepatitis B virus and retroviruses) was also significant.\n\nCode# Get viral taxonomy\nviral_taxa_path &lt;- file.path(data_dir, \"viral-taxids.tsv.gz\")\nviral_taxa &lt;- read_tsv(viral_taxa_path, show_col_types = FALSE)\n\n# Import Kraken reports & extract viral taxa\nsamples &lt;- as.character(basic_stats_raw$sample)\ncol_names &lt;- c(\"pc_reads_total\", \"n_reads_clade\", \"n_reads_direct\",\n               \"rank\", \"taxid\", \"name\")\nreport_paths &lt;- paste0(data_dir, \"kraken/\", samples, \".report.gz\")\nkraken_reports &lt;- lapply(1:length(samples), function(n) \n  read_tsv(report_paths[n], col_names = col_names, show_col_types = FALSE) %&gt;%\n    mutate(sample = samples[n])) %&gt;% bind_rows\nkraken_reports_viral &lt;- filter(kraken_reports, taxid %in% viral_taxa$taxid) %&gt;%\n  group_by(sample) %&gt;%\n  mutate(p_reads_viral = n_reads_clade/n_reads_clade[1])\nkraken_reports_viral_cleaned &lt;- kraken_reports_viral %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;%\n  select(-pc_reads_total, -n_reads_direct) %&gt;%\n  select(name, taxid, p_reads_viral, n_reads_clade, everything())\n\nviral_classes &lt;- kraken_reports_viral_cleaned %&gt;% filter(rank == \"C\")\nviral_families &lt;- kraken_reports_viral_cleaned %&gt;% filter(rank == \"F\")\n\n\n\nCode# Identify major viral classes\nviral_classes_major_tab &lt;- viral_classes %&gt;% \n  group_by(name, taxid) %&gt;%\n  summarize(p_reads_viral_max = max(p_reads_viral), .groups=\"drop\") %&gt;%\n  filter(p_reads_viral_max &gt;= 0.04)\nviral_classes_major_list &lt;- viral_classes_major_tab %&gt;% pull(name)\nviral_classes_major &lt;- viral_classes %&gt;% \n  filter(name %in% viral_classes_major_list) %&gt;%\n  select(name, taxid, sample, na_type, ctrl, date, p_reads_viral)\nviral_classes_minor &lt;- viral_classes_major %&gt;% \n  group_by(sample, na_type, ctrl, date) %&gt;%\n  summarize(p_reads_viral_major = sum(p_reads_viral), .groups = \"drop\") %&gt;%\n  mutate(name = \"Other\", taxid=NA, p_reads_viral = 1-p_reads_viral_major) %&gt;%\n  select(name, taxid, sample, na_type, ctrl, date, p_reads_viral)\nviral_classes_display &lt;- bind_rows(viral_classes_major, viral_classes_minor) %&gt;%\n  arrange(desc(p_reads_viral)) %&gt;% \n  mutate(name = factor(name, levels=c(viral_classes_major_list, \"Other\"))) %&gt;%\n  rename(p_reads = p_reads_viral, classification=name)\n\npalette_viral &lt;- c(brewer.pal(12, \"Set3\"), brewer.pal(8, \"Set2\"))\ng_classes &lt;- g_comp_base + \n  geom_col(data=viral_classes_display, position = \"stack\") +\n  scale_y_continuous(name=\"% Viral Reads\", limits=c(0,1.01), breaks = seq(0,1,0.2),\n                     expand=c(0,0), labels = function(y) y*100) +\n  scale_fill_manual(values=palette_viral, name = \"Viral class\")\ng_classes\n\n\n\n\n\n\n\nOf these, the most interesting are the strong presence of Herviviricetes in the DNA reads and Revtraviricetes in the RNA reads, as both of these are families that contain important human pathogens.\nDigging into the former, it turns out these reads are composed almost exclusively of Herpesviridae at the family level. Within non-control samples, these arise overwhelmingly from Cytomegalovirus. Digging in at the species level, these in turn are primarily attributed to a single CMV, Human betaherpesvirus 5, a.k.a. human cytomegalovirus (HCMV). I was excited to see this: this is the first time a single human pathogen, or even all human pathogens combined, have constituted a significant fraction of all viral reads in a sample I’ve analyzed with this pipeline.\n\nCode# Get all read counts in class\nhervi_taxid &lt;- 2731363\nhervi_desc_taxids_old &lt;- hervi_taxid\nhervi_desc_taxids_new &lt;- unique(c(hervi_desc_taxids_old, viral_taxa %&gt;% filter(parent_taxid %in% hervi_desc_taxids_old) %&gt;% pull(taxid)))\nwhile (length(hervi_desc_taxids_new) &gt; length(hervi_desc_taxids_old)){\n  hervi_desc_taxids_old &lt;- hervi_desc_taxids_new\n  hervi_desc_taxids_new &lt;- unique(c(hervi_desc_taxids_old, viral_taxa %&gt;% filter(parent_taxid %in% hervi_desc_taxids_old) %&gt;% pull(taxid)))\n}\nhervi_counts &lt;- kraken_reports_viral_cleaned %&gt;%\n  filter(taxid %in% hervi_desc_taxids_new) %&gt;%\n  mutate(p_reads_hervi = n_reads_clade/n_reads_clade[1])\n\n# Get genus composition\nhervi_genera &lt;- hervi_counts %&gt;% filter(rank == \"S\", na_type == \"DNA\")\nhervi_genera_major_tab &lt;- hervi_genera %&gt;% \n  group_by(name, taxid) %&gt;%\n  summarize(p_reads_hervi_max = max(p_reads_hervi), .groups=\"drop\") %&gt;%\n  filter(p_reads_hervi_max &gt;= 0.04)\nhervi_genera_major_list &lt;- hervi_genera_major_tab %&gt;% pull(name)\nhervi_genera_major &lt;- hervi_genera %&gt;% \n  filter(name %in% hervi_genera_major_list) %&gt;%\n  select(name, taxid, sample, na_type, ctrl, date, p_reads_hervi)\nhervi_genera_minor &lt;- hervi_genera_major %&gt;% \n  group_by(sample, na_type, ctrl, date) %&gt;%\n  summarize(p_reads_hervi_major = sum(p_reads_hervi), .groups = \"drop\") %&gt;%\n  mutate(name = \"Other\", taxid=NA, p_reads_hervi = 1-p_reads_hervi_major) %&gt;%\n  select(name, taxid, sample, na_type, ctrl, date, p_reads_hervi)\nhervi_genera_display &lt;- bind_rows(hervi_genera_major, hervi_genera_minor) %&gt;%\n  arrange(desc(p_reads_hervi)) %&gt;% \n  mutate(name = factor(name, levels=c(hervi_genera_major_list, \"Other\"))) %&gt;%\n  rename(p_reads = p_reads_hervi, classification=name)\n\n# Plot\ng_hervi_genera &lt;- g_comp_base + \n  geom_col(data=hervi_genera_display, position = \"stack\") +\n  scale_y_continuous(name=\"% Herviviricetes Reads\", limits=c(0,1.01), \n                     breaks = seq(0,1,0.2),\n                     expand=c(0,0), labels = function(y) y*100) +\n  scale_fill_manual(values=palette_viral, name = \"Viral species\")\ng_hervi_genera\n\n\n\n\n\n\n\nRevtraviricetes reads are similarly dominated by a single viral genus, Alpharetrovirus. Digging in at the species level, we see contributions from a variety of avian oncoviruses. To my (admittedly non-expert) knowledge, none of these infect humans, and I think they are probably primarily arising from local birds (or possibly rats).\n\nCode# Get all read counts in class\nrevtra_taxid &lt;- 2732514\nrevtra_desc_taxids_old &lt;- revtra_taxid\nrevtra_desc_taxids_new &lt;- unique(c(revtra_desc_taxids_old, viral_taxa %&gt;% filter(parent_taxid %in% revtra_desc_taxids_old) %&gt;% pull(taxid)))\nwhile (length(revtra_desc_taxids_new) &gt; length(revtra_desc_taxids_old)){\n  revtra_desc_taxids_old &lt;- revtra_desc_taxids_new\n  revtra_desc_taxids_new &lt;- unique(c(revtra_desc_taxids_old, viral_taxa %&gt;% filter(parent_taxid %in% revtra_desc_taxids_old) %&gt;% pull(taxid)))\n}\nrevtra_counts &lt;- kraken_reports_viral_cleaned %&gt;%\n  filter(taxid %in% revtra_desc_taxids_new) %&gt;%\n  mutate(p_reads_revtra = n_reads_clade/n_reads_clade[1])\n\n# Get genus composition\nrevtra_species &lt;- revtra_counts %&gt;% filter(rank == \"S\", na_type == \"RNA\")\nrevtra_species_major_tab &lt;- revtra_species %&gt;% \n  group_by(name, taxid) %&gt;%\n  summarize(p_reads_revtra_max = max(p_reads_revtra), .groups=\"drop\") %&gt;%\n  filter(p_reads_revtra_max &gt;= 0.04)\nrevtra_species_major_list &lt;- revtra_species_major_tab %&gt;% pull(name)\nrevtra_species_major &lt;- revtra_species %&gt;% \n  filter(name %in% revtra_species_major_list) %&gt;%\n  select(name, taxid, sample, na_type, ctrl, date, p_reads_revtra)\nrevtra_species_minor &lt;- revtra_species_major %&gt;% \n  group_by(sample, na_type, ctrl, date) %&gt;%\n  summarize(p_reads_revtra_major = sum(p_reads_revtra), .groups = \"drop\") %&gt;%\n  mutate(name = \"Other\", taxid=NA, p_reads_revtra = 1-p_reads_revtra_major) %&gt;%\n  select(name, taxid, sample, na_type, ctrl, date, p_reads_revtra)\nrevtra_species_display &lt;- bind_rows(revtra_species_major, revtra_species_minor) %&gt;%\n  arrange(desc(p_reads_revtra)) %&gt;% \n  mutate(name = factor(name, levels=c(revtra_species_major_list, \"Other\"))) %&gt;%\n  rename(p_reads = p_reads_revtra, classification=name)\n\n# Plot\ng_revtra_species &lt;- g_comp_base + \n  geom_col(data=revtra_species_display, position = \"stack\") +\n  scale_y_continuous(name=\"% Revtraviricetes Reads\", limits=c(0,1.01), \n                     breaks = seq(0,1,0.2),\n                     expand=c(0,0), labels = function(y) y*100) +\n  scale_fill_manual(values=palette_viral, name = \"Viral species\")\ng_revtra_species\n\n\n\n\n\n\n\nHuman-infecting virus reads: validation\nNext, I investigated the human-infecting virus read content of these unenriched samples. Using the same workflow I used for Brumfield et al, I identified 2811 RNA read pairs and 12792 DNA read pairs as putatively human viral: 0.003% and 0.005% of surviving reads, respectively.\n\nCodehv_reads_filtered_path &lt;- file.path(data_dir, \"hv_hits_putative_filtered.tsv.gz\")\nhv_reads_filtered &lt;- read_tsv(hv_reads_filtered_path, show_col_types = FALSE) %&gt;%\n  inner_join(libraries, by=\"sample\") %&gt;% \n  arrange(date, na_type)\nn_hv_filtered &lt;- hv_reads_filtered %&gt;% group_by(sample, date, na_type, ctrl) %&gt;% count %&gt;% inner_join(basic_stats %&gt;% filter(stage == \"ribo_initial\") %&gt;% select(sample, n_read_pairs), by=\"sample\") %&gt;% rename(n_putative = n, n_total = n_read_pairs) %&gt;% mutate(p_reads = n_putative/n_total, pc_reads = p_reads * 100)\nn_hv_filtered_summ &lt;- n_hv_filtered %&gt;% group_by(na_type, ctrl) %&gt;% summarize(n_putative = sum(n_putative), n_total = sum(n_total), .groups=\"drop\") %&gt;% mutate(p_reads = n_putative/n_total, pc_reads = p_reads*100)\n\n\n\nCodemrg &lt;- hv_reads_filtered %&gt;%\n  mutate(kraken_label = ifelse(assigned_hv, \"Kraken2 HV\\nassignment\",\n                               ifelse(hit_hv, \"Kraken2 HV\\nhit\",\n                                      \"No hit or\\nassignment\"))) %&gt;%\n  group_by(sample, na_type) %&gt;% arrange(desc(adj_score_fwd), desc(adj_score_rev)) %&gt;%\n  mutate(seq_num = row_number(),\n         adj_score_max = pmax(adj_score_fwd, adj_score_rev))\n# Import Bowtie2/Kraken data and perform filtering steps\ng_mrg &lt;- ggplot(mrg, aes(x=adj_score_fwd, y=adj_score_rev)) +\n  geom_point(alpha=0.5, shape=16) + \n  scale_x_continuous(name=\"S(forward read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_y_continuous(name=\"S(reverse read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  facet_grid(na_type~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +\n  theme_base + theme(aspect.ratio=1)\ng_mrg\n\n\n\n\n\n\nCodeg_hist_0 &lt;- ggplot(mrg, aes(x=adj_score_max)) + geom_histogram(binwidth=5,boundary=0,position=\"dodge\") + facet_grid(na_type~kraken_label, scales=\"free_y\") + scale_x_continuous(name = \"Maximum adjusted alignment score\") + scale_y_continuous(name=\"# Read pairs\") + scale_fill_brewer(palette = \"Dark2\") + theme_base + geom_vline(xintercept=20, linetype=\"dashed\", color=\"red\")\ng_hist_0\n\n\n\n\n\n\n\nAs previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I’ve used for previous datasets.\n\nCodemrg_fasta &lt;-  mrg %&gt;%\n  mutate(seq_head = paste0(\"&gt;\", seq_id)) %&gt;%\n  ungroup %&gt;%\n  select(header1=seq_head, seq1=query_seq_fwd, \n         header2=seq_head, seq2=query_seq_rev) %&gt;%\n  mutate(header1=paste0(header1, \"_1\"), header2=paste0(header2, \"_2\"))\nmrg_fasta_out &lt;- do.call(paste, c(mrg_fasta, sep=\"\\n\")) %&gt;% paste(collapse=\"\\n\")\nwrite(mrg_fasta_out, file.path(data_dir, \"blast/putative-viral.fasta\"))\n\n\n\nCode# Import BLAST results\n# Pre-filtering BLAST results to save space\n# blast_results_path &lt;- file.path(data_dir, \"blast/putative-viral.blast.gz\")\n# blast_cols &lt;- c(\"qseqid\", \"sseqid\", \"sgi\", \"staxid\", \"qlen\", \"evalue\", \"bitscore\", \"qcovs\", \"length\", \"pident\", \"mismatch\", \"gapopen\", \"sstrand\", \"qstart\", \"qend\", \"sstart\", \"send\")\n# blast_results &lt;- read_tsv(blast_results_path, show_col_types = FALSE,\n#                           col_names = blast_cols, col_types = cols(.default=\"c\"))\nblast_results_path &lt;- file.path(data_dir, \"blast/putative-viral-best.blast.gz\")\nblast_results &lt;- read_tsv(blast_results_path, show_col_types = FALSE)\n\n# Filter for best hit for each query/subject combination\nblast_results_best &lt;- blast_results %&gt;% group_by(qseqid, staxid) %&gt;% \n  filter(bitscore == max(bitscore)) %&gt;%\n  filter(length == max(length)) %&gt;% filter(row_number() == 1)\n\n# Rank hits for each query and filter for high-ranking hits\nblast_results_ranked &lt;- blast_results_best %&gt;% \n  group_by(qseqid) %&gt;% mutate(rank = dense_rank(desc(bitscore)))\nblast_results_highrank &lt;- blast_results_ranked %&gt;% filter(rank &lt;= 5) %&gt;%\n    mutate(read_pair = str_split(qseqid, \"_\") %&gt;% sapply(nth, n=-1), \n         seq_id = str_split(qseqid, \"_\") %&gt;% sapply(nth, n=1)) %&gt;%\n    mutate(bitscore = as.numeric(bitscore))\n\n# Summarize by read pair and taxid\nblast_results_paired &lt;- blast_results_highrank %&gt;%\n  group_by(seq_id, staxid) %&gt;%\n  summarize(bitscore_max = max(bitscore), bitscore_min = min(bitscore),\n            n_reads = n(), .groups = \"drop\")\n\n# Add viral status\nblast_results_viral &lt;- mutate(blast_results_paired, viral = staxid %in% viral_taxa$taxid) %&gt;%\n  mutate(viral_full = viral & n_reads == 2)\n\n# Compare to Kraken & Bowtie assignments\nmrg_assign &lt;- mrg %&gt;% select(sample, seq_id, taxid, assigned_taxid, adj_score_max, na_type)\nblast_results_assign &lt;- left_join(blast_results_viral, mrg_assign, by=\"seq_id\") %&gt;%\n    mutate(taxid_match_bowtie = (staxid == taxid),\n           taxid_match_kraken = (staxid == assigned_taxid),\n           taxid_match_any = taxid_match_bowtie | taxid_match_kraken)\nblast_results_out &lt;- blast_results_assign %&gt;%\n  group_by(seq_id) %&gt;%\n  summarize(viral_status = ifelse(any(viral_full), 2,\n                                  ifelse(any(taxid_match_any), 2,\n                                             ifelse(any(viral), 1, 0))),\n            .groups = \"drop\")\n\n\n\nCode# Merge BLAST results with unenriched read data\nmrg_blast &lt;- full_join(mrg, blast_results_out, by=\"seq_id\") %&gt;%\n  mutate(viral_status = replace_na(viral_status, 0),\n         viral_status_out = ifelse(viral_status == 0, FALSE, TRUE))\nmrg_blast_rna &lt;- mrg_blast %&gt;% filter(na_type == \"RNA\")\nmrg_blast_dna &lt;- mrg_blast %&gt;% filter(na_type == \"DNA\")\n\n# Plot RNA\ng_mrg_blast_rna &lt;- mrg_blast_rna %&gt;%\n  ggplot(aes(x=adj_score_fwd, y=adj_score_rev, color=viral_status_out)) +\n  geom_point(alpha=0.5, shape=16) +\n  scale_x_continuous(name=\"S(forward read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_y_continuous(name=\"S(reverse read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_color_brewer(palette = \"Set1\", name = \"Viral status\") +\n  facet_grid(viral_status_out~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +\n  theme_base + labs(title=\"RNA\") + \n  theme(aspect.ratio=1, plot.title = element_text(size=rel(2), hjust=0))\ng_mrg_blast_rna\n\n\n\n\n\n\nCode# Plot DNA\ng_mrg_blast_dna &lt;- mrg_blast_dna %&gt;%\n  ggplot(aes(x=adj_score_fwd, y=adj_score_rev, color=viral_status_out)) +\n  geom_point(alpha=0.5, shape=16) +\n  scale_x_continuous(name=\"S(forward read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_y_continuous(name=\"S(reverse read)\", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +\n  scale_color_brewer(palette = \"Set1\", name = \"Viral status\") +\n  facet_grid(viral_status_out~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +\n  theme_base + labs(title=\"DNA\") + \n  theme(aspect.ratio=1, plot.title = element_text(size=rel(2), hjust=0))\ng_mrg_blast_dna\n\n\n\n\n\n\n\n\nCodeg_hist_1 &lt;- ggplot(mrg_blast, aes(x=adj_score_max)) + geom_histogram(binwidth=5,boundary=0,position=\"dodge\") + facet_grid(na_type~viral_status_out, scales = \"free_y\") + scale_x_continuous(name = \"Maximum adjusted alignment score\") + scale_y_continuous(name=\"# Read pairs\") + scale_fill_brewer(palette = \"Dark2\") + theme_base + geom_vline(xintercept=20, linetype=\"dashed\", color=\"red\")\ng_hist_1\n\n\n\n\n\n\n\nThese results look good on visual inspection, and indeed precision and sensitivity are both very high. For a disjunctive score threshold of 20, my updated workflow achieves an F1 score of 96.7% for RNA sequences and 98.2% for DNA sequences.\n\nCodetest_sens_spec &lt;- function(tab, score_threshold, conjunctive, include_special){\n  if (!include_special) tab &lt;- filter(tab, viral_status_out %in% c(\"TRUE\", \"FALSE\"))\n  tab_retained &lt;- tab %&gt;% mutate(\n    conjunctive = conjunctive,\n    retain_score_conjunctive = (adj_score_fwd &gt; score_threshold & adj_score_rev &gt; score_threshold), \n    retain_score_disjunctive = (adj_score_fwd &gt; score_threshold | adj_score_rev &gt; score_threshold),\n    retain_score = ifelse(conjunctive, retain_score_conjunctive, retain_score_disjunctive),\n    retain = assigned_hv | hit_hv | retain_score) %&gt;%\n    group_by(viral_status_out, retain) %&gt;% count\n  pos_tru &lt;- tab_retained %&gt;% filter(viral_status_out == \"TRUE\", retain) %&gt;% pull(n) %&gt;% sum\n  pos_fls &lt;- tab_retained %&gt;% filter(viral_status_out != \"TRUE\", retain) %&gt;% pull(n) %&gt;% sum\n  neg_tru &lt;- tab_retained %&gt;% filter(viral_status_out != \"TRUE\", !retain) %&gt;% pull(n) %&gt;% sum\n  neg_fls &lt;- tab_retained %&gt;% filter(viral_status_out == \"TRUE\", !retain) %&gt;% pull(n) %&gt;% sum\n  sensitivity &lt;- pos_tru / (pos_tru + neg_fls)\n  specificity &lt;- neg_tru / (neg_tru + pos_fls)\n  precision   &lt;- pos_tru / (pos_tru + pos_fls)\n  f1 &lt;- 2 * precision * sensitivity / (precision + sensitivity)\n  out &lt;- tibble(threshold=score_threshold, include_special = include_special, \n                conjunctive = conjunctive, sensitivity=sensitivity, \n                specificity=specificity, precision=precision, f1=f1)\n  return(out)\n}\nrange_f1 &lt;- function(intab, inc_special, inrange=15:45){\n  tss &lt;- purrr::partial(test_sens_spec, tab=intab, include_special=inc_special)\n  stats_conj &lt;- lapply(inrange, tss, conjunctive=TRUE) %&gt;% bind_rows\n  stats_disj &lt;- lapply(inrange, tss, conjunctive=FALSE) %&gt;% bind_rows\n  stats_all &lt;- bind_rows(stats_conj, stats_disj) %&gt;%\n    pivot_longer(!(threshold:conjunctive), names_to=\"metric\", values_to=\"value\") %&gt;%\n    mutate(conj_label = ifelse(conjunctive, \"Conjunctive\", \"Disjunctive\"))\n  return(stats_all)\n}\ninc_special &lt;- FALSE\nstats_rna &lt;- range_f1(mrg_blast_rna, inc_special) %&gt;% mutate(na_type = \"RNA\")\nstats_dna &lt;- range_f1(mrg_blast_dna, inc_special) %&gt;% mutate(na_type = \"DNA\")\nstats_0 &lt;- bind_rows(stats_rna, stats_dna) %&gt;% mutate(attempt=0)\nthreshold_opt_0 &lt;- stats_0 %&gt;% group_by(conj_label,attempt,na_type) %&gt;% \n  filter(metric == \"f1\") %&gt;%\n  filter(value == max(value)) %&gt;% filter(threshold == min(threshold))\ng_stats_0 &lt;- ggplot(stats_0, aes(x=threshold, y=value, color=metric)) +\n  geom_vline(data = threshold_opt_0, mapping = aes(xintercept=threshold),\n             color = \"red\", linetype = \"dashed\") +\n  geom_line() +\n  scale_y_continuous(name = \"Value\", limits=c(0,1), breaks = seq(0,1,0.2), expand = c(0,0)) +\n  scale_x_continuous(name = \"Threshold\", expand = c(0,0)) +\n  scale_color_brewer(palette=\"Set3\") +\n  facet_grid(na_type~conj_label) +\n  theme_base\ng_stats_0\n\n\n\n\n\n\n\nLooking into the composition of different read groups, nothing stands out except the predominance of HCMV (human betaherpesvirus 5), which is consistent with the Kraken results above and borne out by the BLASTN alignments:\n\nCodeviral_taxa$name[viral_taxa$taxid == 211787] &lt;- \"Human papillomavirus type 92\"\nviral_taxa$name[viral_taxa$taxid == 509154] &lt;- \"Porcine endogenous retrovirus C\"\n\nmajor_threshold &lt;- 0.05\nfp &lt;- mrg_blast %&gt;% \n  group_by(na_type, viral_status_out, \n           highscore = adj_score_max &gt;= 20, taxid) %&gt;% count %&gt;% \n  group_by(na_type, viral_status_out, highscore) %&gt;% mutate(p=n/sum(n)) %&gt;% \n  left_join(viral_taxa, by=\"taxid\") %&gt;%\n  arrange(desc(p)) %&gt;%\n  mutate(name = ifelse(taxid == 194958, \"Porcine endogenous retrovirus A\", name))\nfp_major_tab &lt;- fp %&gt;% filter(p &gt; major_threshold) %&gt;% arrange(desc(p))\nfp_major_list &lt;- fp_major_tab %&gt;% pull(name) %&gt;% sort %&gt;% unique %&gt;% c(., \"Other\")\nfp_major &lt;- fp %&gt;% mutate(major = p &gt; major_threshold) %&gt;% \n  mutate(name_display = ifelse(major, name, \"Other\")) %&gt;%\n  group_by(na_type, viral_status_out, highscore, name_display) %&gt;% \n  summarize(n=sum(n), p=sum(p), .groups = \"drop\")  %&gt;%\n  mutate(name_display = factor(name_display, levels = fp_major_list),\n         score_display = ifelse(highscore, \"S &gt;= 20\", \"S &lt; 20\"),\n         status_display = ifelse(viral_status_out, \"True positive\", \"False positive\"))\ng_fp &lt;- ggplot(fp_major, aes(x=score_display, y=p, fill=name_display)) +\n  geom_col(position=\"stack\") +\n  scale_x_discrete(name = \"True positive?\") +\n  scale_y_continuous(name = \"% reads\", limits = c(0,1.01), \n                     breaks = seq(0,1,0.2), expand = c(0,0)) +\n  scale_fill_manual(values = palette_viral, name = \"Viral\\ntaxon\") +\n  facet_grid(na_type~status_display) +\n  guides(fill=guide_legend(ncol=3)) +\n  theme_kit\ng_fp\n\n\n\n\n\n\n\n\nCode# Configure\nref_taxid &lt;- 10359\n\n# Get taxon names\ntax_names_path &lt;- file.path(data_dir, \"taxid-names.tsv.gz\")\ntax_names &lt;- read_tsv(tax_names_path, show_col_types = FALSE)\n\n# Add missing names\ntax_names_new &lt;- tribble(~staxid, ~name,\n                         3050295, \"Cytomegalovirus humanbeta5\",\n                         459231, \"FLAG-tagging vector pFLAG97-TSR\")\ntax_names &lt;- bind_rows(tax_names, tax_names_new)\nref_name &lt;- tax_names %&gt;% filter(staxid == ref_taxid) %&gt;% pull(name)\n\n# Get major matches\nfp_staxid &lt;- mrg_blast %&gt;% filter(taxid == ref_taxid) %&gt;%\n  group_by(na_type, highscore = adj_score_max &gt;= 20) %&gt;% mutate(n_seq = n()) %&gt;%\n  left_join(blast_results_paired, by=\"seq_id\") %&gt;%\n  mutate(staxid = as.integer(staxid)) %&gt;%\n  left_join(tax_names, by=\"staxid\") %&gt;% rename(sname=name) %&gt;%\n  left_join(tax_names %&gt;% rename(taxid=staxid), by=\"taxid\")\nfp_staxid_count &lt;- fp_staxid %&gt;%\n  group_by(viral_status_out, highscore, na_type, \n           taxid, name, staxid, sname, n_seq) %&gt;%\n  count %&gt;%\n  group_by(viral_status_out, highscore, na_type, taxid, name) %&gt;%\n  mutate(p=n/n_seq)\nfp_staxid_count_major &lt;- fp_staxid_count %&gt;%\n  filter(n&gt;1, p&gt;0.1, !is.na(staxid)) %&gt;%\n  mutate(score_display = ifelse(highscore, \"S &gt;= 20\", \"S &lt; 20\"),\n         status_display = ifelse(viral_status_out, \n                                 \"True positive\", \"False positive\"))\n\n# Plot\ng &lt;- ggplot(fp_staxid_count_major, aes(x=p, y=sname)) + \n  geom_col() + \n  facet_grid(na_type~status_display+score_display, scales=\"free\",\n             labeller = label_wrap_gen(multi_line = FALSE)) +\n  scale_x_continuous(name=\"% mapped reads\", limits=c(0,1), breaks=seq(0,1,0.2),\n                     expand=c(0,0)) +\n  labs(title=paste0(ref_name, \" (taxid \", ref_taxid, \")\")) +\n  theme_base + theme(\n    axis.title.y = element_blank(),\n    plot.title = element_text(size=rel(1.5), hjust=0, face=\"plain\"))\ng\n\n\n\n\n\n\n\nHuman-infecting viruses: overall relative abundance\n\nCode# Get raw read counts\nread_counts_raw &lt;- basic_stats_raw %&gt;%\n  select(sample, date, na_type, ctrl, n_reads_raw = n_read_pairs)\n\n# Get HV read counts\nmrg_hv &lt;- mrg %&gt;% mutate(hv_status = assigned_hv | hit_hv | adj_score_max &gt;= 20)\nread_counts_hv &lt;- mrg_hv %&gt;% filter(hv_status) %&gt;% group_by(sample) %&gt;% \n  count(name=\"n_reads_hv\")\nread_counts &lt;- read_counts_raw %&gt;% left_join(read_counts_hv, by=\"sample\") %&gt;%\n  mutate(n_reads_hv = replace_na(n_reads_hv, 0))\n\n# Aggregate\nread_counts_total &lt;- read_counts %&gt;% group_by(na_type, ctrl) %&gt;%\n  summarize(n_reads_raw = sum(n_reads_raw),\n            n_reads_hv = sum(n_reads_hv), .groups=\"drop\") %&gt;%\n  mutate(sample= \"All samples\", date = \"All dates\")\nread_counts_agg &lt;- read_counts %&gt;% arrange(date) %&gt;%\n  arrange(sample) %&gt;%\n  bind_rows(read_counts_total) %&gt;% \n  mutate(sample = fct_inorder(sample),\n         p_reads_hv = n_reads_hv/n_reads_raw)\n\n\nIn non-control samples, applying a disjunctive cutoff at S=20 identifies 2591 RNA reads and 12236 DNA reads as human-viral. This gives an overall relative HV abundance of \\(1.63 \\times 10^{-5}\\) for RNA reads and \\(4.16 \\times 10^{-5}\\) for DNA reads. Reassuringly, relative abundances in control samples are at least 10x lower. We also see a sharp drop in relative abundance for both nucleic-acid types for the period when the daycare was closed (2015-01-20):\n\nCode# Visualize\ng_phv_agg &lt;- ggplot(read_counts_agg, aes(x=date, color=na_type)) +\n  geom_point(aes(y=p_reads_hv)) +\n  scale_y_log10(\"Relative abundance of human virus reads\") +\n  scale_x_discrete(name=\"Collection Date\") +\n  facet_grid(.~ctrl, scales = \"free\", space = \"free_x\") +\n  scale_color_na() + theme_rotate\ng_phv_agg\n\n\n\n\n\n\n\nThese overall RA values are similar to those we’ve seen previously for non-panel-enriched wastewater RNA data. That said, it’s notable that the DNA read RA seen here is much higher than that seen in the only DNA wastewater dataset I’ve analyzed so far (Brumfield):\n\nCode# Collate past RA values\nra_past &lt;- tribble(~dataset, ~ra, ~na_type, ~panel_enriched,\n                   \"Brumfield\", 5e-5, \"RNA\", FALSE,\n                   \"Brumfield\", 3.66e-7, \"DNA\", FALSE,\n                   \"Spurbeck\", 5.44e-6, \"RNA\", FALSE,\n                   \"Yang\", 3.62e-4, \"RNA\", FALSE,\n                   \"Rothman (unenriched)\", 1.87e-5, \"RNA\", FALSE,\n                   \"Rothman (panel-enriched)\", 3.3e-5, \"RNA\", TRUE,\n                   \"Crits-Christoph (unenriched)\", 1.37e-5, \"RNA\", FALSE,\n                   \"Crits-Christoph (panel-enriched)\", 1.26e-2, \"RNA\", TRUE)\n\n# Collate new RA values\nra_new &lt;- tribble(~dataset, ~ra, ~na_type, ~panel_enriched,\n                   \"Prussin (non-control)\", 1.63e-5, \"RNA\", FALSE,\n                   \"Prussin (non-control)\", 4.16e-5, \"DNA\", FALSE)\n\n# Plot\nra_comp &lt;- bind_rows(ra_past, ra_new) %&gt;% mutate(dataset = fct_inorder(dataset))\ng_ra_comp &lt;- ggplot(ra_comp, aes(y=dataset, x=ra, color=na_type)) +\n  geom_point() +\n  scale_color_na() +\n  scale_x_log10(name=\"Relative abundance of human virus reads\") +\n  theme_base + theme(axis.title.y = element_blank())\ng_ra_comp\n\n\n\n\n\n\n\nHuman-infecting viruses: taxonomy and composition\nAt the family level, we see that Papillomaviridae, Herpesviridae, and Polyomaviridae are the most abundant families in both DNA and RNA reads, with Adenoviridae and (in RNA reads) Picornaviridae also making a respectable showing. The Herpesviridae reads are, predictably, overwhelmingly from HCMV, while the Papillomaviridae and Polyomaviridae reads are split up among a larger number of related viruses:\n\nCode# Get viral taxon names for putative HV reads\nviral_taxa$name[viral_taxa$taxid == 249588] &lt;- \"Mamastrovirus\"\nviral_taxa$name[viral_taxa$taxid == 194960] &lt;- \"Kobuvirus\"\nviral_taxa$name[viral_taxa$taxid == 688449] &lt;- \"Salivirus\"\nviral_taxa$name[viral_taxa$taxid == 585893] &lt;- \"Picobirnaviridae\"\nviral_taxa$name[viral_taxa$taxid == 333922] &lt;- \"Betapapillomavirus\"\nviral_taxa$name[viral_taxa$taxid == 334207] &lt;- \"Betapapillomavirus 3\"\nviral_taxa$name[viral_taxa$taxid == 369960] &lt;- \"Porcine type-C oncovirus\"\nviral_taxa$name[viral_taxa$taxid == 333924] &lt;- \"Betapapillomavirus 2\"\nviral_taxa$name[viral_taxa$taxid == 687329] &lt;- \"Anelloviridae\"\nviral_taxa$name[viral_taxa$taxid == 325455] &lt;- \"Gammapapillomavirus\"\nviral_taxa$name[viral_taxa$taxid == 333750] &lt;- \"Alphapapillomavirus\"\nviral_taxa$name[viral_taxa$taxid == 694002] &lt;- \"Betacoronavirus\"\nviral_taxa$name[viral_taxa$taxid == 334202] &lt;- \"Mupapillomavirus\"\nviral_taxa$name[viral_taxa$taxid == 197911] &lt;- \"Alphainfluenzavirus\"\nviral_taxa$name[viral_taxa$taxid == 186938] &lt;- \"Respirovirus\"\nviral_taxa$name[viral_taxa$taxid == 333926] &lt;- \"Gammapapillomavirus 1\"\nviral_taxa$name[viral_taxa$taxid == 337051] &lt;- \"Betapapillomavirus 1\"\nviral_taxa$name[viral_taxa$taxid == 337043] &lt;- \"Alphapapillomavirus 4\"\nviral_taxa$name[viral_taxa$taxid == 694003] &lt;- \"Betacoronavirus 1\"\nviral_taxa$name[viral_taxa$taxid == 334204] &lt;- \"Mupapillomavirus 2\"\nviral_taxa$name[viral_taxa$taxid == 334208] &lt;- \"Betapapillomavirus 4\"\n\nviral_taxa$name[viral_taxa$taxid == 334208] &lt;- \"Betapapillomavirus 4\"\nviral_taxa$name[viral_taxa$taxid == 334208] &lt;- \"Betapapillomavirus 4\"\nviral_taxa$name[viral_taxa$taxid == 334208] &lt;- \"Betapapillomavirus 4\"\n\n\nmrg_hv_named &lt;- mrg_hv %&gt;% left_join(viral_taxa, by=\"taxid\")\n\n# Discover viral species & genera for HV reads\nraise_rank &lt;- function(read_db, taxid_db, out_rank = \"species\", verbose = FALSE){\n  # Get higher ranks than search rank\n  ranks &lt;- c(\"subspecies\", \"species\", \"subgenus\", \"genus\", \"subfamily\", \"family\", \"suborder\", \"order\", \"class\", \"subphylum\", \"phylum\", \"kingdom\", \"superkingdom\")\n  rank_match &lt;- which.max(ranks == out_rank)\n  high_ranks &lt;- ranks[rank_match:length(ranks)]\n  # Merge read DB and taxid DB\n  reads &lt;- read_db %&gt;% select(-parent_taxid, -rank, -name) %&gt;%\n    left_join(taxid_db, by=\"taxid\")\n  # Extract sequences that are already at appropriate rank\n  reads_rank &lt;- filter(reads, rank == out_rank)\n  # Drop sequences at a higher rank and return unclassified sequences\n  reads_norank &lt;- reads %&gt;% filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))\n  while(nrow(reads_norank) &gt; 0){ # As long as there are unclassified sequences...\n    # Promote read taxids and re-merge with taxid DB, then re-classify and filter\n    reads_remaining &lt;- reads_norank %&gt;% mutate(taxid = parent_taxid) %&gt;%\n      select(-parent_taxid, -rank, -name) %&gt;%\n      left_join(taxid_db, by=\"taxid\")\n    reads_rank &lt;- reads_remaining %&gt;% filter(rank == out_rank) %&gt;%\n      bind_rows(reads_rank)\n    reads_norank &lt;- reads_remaining %&gt;%\n      filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))\n  }\n  # Finally, extract and append reads that were excluded during the process\n  reads_dropped &lt;- reads %&gt;% filter(!seq_id %in% reads_rank$seq_id)\n  reads_out &lt;- reads_rank %&gt;% bind_rows(reads_dropped) %&gt;%\n    select(-parent_taxid, -rank, -name) %&gt;%\n    left_join(taxid_db, by=\"taxid\")\n  return(reads_out)\n}\nhv_reads_species &lt;- raise_rank(mrg_hv_named, viral_taxa, \"species\")\nhv_reads_genus &lt;- raise_rank(mrg_hv_named, viral_taxa, \"genus\")\nhv_reads_family &lt;- raise_rank(mrg_hv_named, viral_taxa, \"family\")\n\n\n\nCodethreshold_major_family &lt;- 0.06\n\n# Count reads for each human-viral family\nhv_family_counts &lt;- hv_reads_family %&gt;% \n  group_by(sample, date, ctrl, na_type, name, taxid) %&gt;%\n  count(name = \"n_reads_hv\") %&gt;%\n  group_by(sample, date, ctrl, na_type) %&gt;%\n  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))\n\n# Identify high-ranking families and group others\nhv_family_major_tab &lt;- hv_family_counts %&gt;% group_by(name) %&gt;% \n  filter(p_reads_hv == max(p_reads_hv)) %&gt;% filter(row_number() == 1) %&gt;%\n  arrange(desc(p_reads_hv)) %&gt;% filter(p_reads_hv &gt; threshold_major_family)\nhv_family_counts_major &lt;- hv_family_counts %&gt;%\n  mutate(name_display = ifelse(name %in% hv_family_major_tab$name, name, \"Other\")) %&gt;%\n  group_by(sample, date, ctrl, na_type, name_display) %&gt;%\n  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), \n            .groups=\"drop\") %&gt;%\n  mutate(name_display = factor(name_display, \n                               levels = c(hv_family_major_tab$name, \"Other\")))\nhv_family_counts_display &lt;- hv_family_counts_major %&gt;%\n  rename(p_reads = p_reads_hv, classification = name_display)\n\n# Plot\ng_hv_family &lt;- g_comp_base + \n  geom_col(data=hv_family_counts_display, position = \"stack\") +\n  scale_y_continuous(name=\"% HV Reads\", limits=c(0,1.01), \n                     breaks = seq(0,1,0.2),\n                     expand=c(0,0), labels = function(y) y*100) +\n  scale_fill_manual(values=palette_viral, name = \"Viral family\")\ng_hv_family\n\n\n\n\n\n\n\n\nCodethreshold_major_genus &lt;- 0.06\n\n# Count reads for each human-viral genus\nhv_genus_counts &lt;- hv_reads_genus %&gt;% \n  group_by(sample, date, ctrl, na_type, name, taxid) %&gt;%\n  count(name = \"n_reads_hv\") %&gt;%\n  group_by(sample, date, ctrl, na_type) %&gt;%\n  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))\n\n# Identify high-ranking families and group others\nhv_genus_major_tab &lt;- hv_genus_counts %&gt;% group_by(name) %&gt;% \n  filter(p_reads_hv == max(p_reads_hv)) %&gt;% filter(row_number() == 1) %&gt;%\n  arrange(desc(p_reads_hv)) %&gt;% filter(p_reads_hv &gt; threshold_major_genus)\nhv_genus_counts_major &lt;- hv_genus_counts %&gt;%\n  mutate(name_display = ifelse(name %in% hv_genus_major_tab$name, name, \"Other\")) %&gt;%\n  group_by(sample, date, ctrl, na_type, name_display) %&gt;%\n  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), \n            .groups=\"drop\") %&gt;%\n  mutate(name_display = factor(name_display, \n                               levels = c(hv_genus_major_tab$name, \"Other\")))\nhv_genus_counts_display &lt;- hv_genus_counts_major %&gt;%\n  rename(p_reads = p_reads_hv, classification = name_display)\n\n# Plot\ng_hv_genus &lt;- g_comp_base + \n  geom_col(data=hv_genus_counts_display, position = \"stack\") +\n  scale_y_continuous(name=\"% HV Reads\", limits=c(0,1.01), \n                     breaks = seq(0,1,0.2),\n                     expand=c(0,0), labels = function(y) y*100) +\n  scale_fill_manual(values=palette_viral, name = \"Viral genus\")\ng_hv_genus\n\n\n\n\n\n\n\n\nCodethreshold_major_species &lt;- 0.10\n\n# Count reads for each human-viral species\nhv_species_counts &lt;- hv_reads_species %&gt;% \n  group_by(sample, date, ctrl, na_type, name, taxid) %&gt;%\n  count(name = \"n_reads_hv\") %&gt;%\n  group_by(sample, date, ctrl, na_type) %&gt;%\n  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))\n\n# Identify high-ranking families and group others\nhv_species_major_tab &lt;- hv_species_counts %&gt;% group_by(name) %&gt;% \n  filter(p_reads_hv == max(p_reads_hv)) %&gt;% filter(row_number() == 1) %&gt;%\n  arrange(desc(p_reads_hv)) %&gt;% filter(p_reads_hv &gt; threshold_major_species)\nhv_species_counts_major &lt;- hv_species_counts %&gt;%\n  mutate(name_display = ifelse(name %in% hv_species_major_tab$name, name, \"Other\")) %&gt;%\n  group_by(sample, date, ctrl, na_type, name_display) %&gt;%\n  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), \n            .groups=\"drop\") %&gt;%\n  mutate(name_display = factor(name_display, \n                               levels = c(hv_species_major_tab$name, \"Other\")))\nhv_species_counts_display &lt;- hv_species_counts_major %&gt;%\n  rename(p_reads = p_reads_hv, classification = name_display)\n\n# Plot\ng_hv_species &lt;- g_comp_base + \n  geom_col(data=hv_species_counts_display, position = \"stack\") +\n  scale_y_continuous(name=\"% HV Reads\", limits=c(0,1.01), \n                     breaks = seq(0,1,0.2),\n                     expand=c(0,0), labels = function(y) y*100) +\n  scale_fill_manual(values=palette_viral, name = \"Viral species\")\ng_hv_species\n\n\n\n\n\n\n\nCompared to the previous datasets I’ve analyzed, the most notable difference is the absence of enteric viruses: Norovirus, Rotavirus, and Enterovirus are all absent from the list of abundant human-viral taxa, as are ~all other gastrointestinal pathogens.\nFinally, here are the overall relative abundances of a set of specific viral genera picked out manually on the basis of scientific or medical interest. In the future, I’ll quantify the RA of these genera across all datasets analyzed with this pipeline to date, which should give us a better sense of how these data compare to others’ under this pipeline.\n\nCode# Define reference genera\npath_genera_rna &lt;- c(\"Mamastrovirus\", \"Enterovirus\", \"Salivirus\", \"Kobuvirus\", \"Norovirus\", \"Sapovirus\", \"Rotavirus\", \"Alphacoronavirus\", \"Betacoronavirus\",\n                 \"Alphainfluenzavirus\", \"Betainfluenzavirus\", \"Lentivirus\")\npath_genera_dna &lt;- c(\"Mastadenovirus\", \"Alphapolyomavirus\", \"Betapolyomavirus\", \"Alphapapillomavirus\", \"Betapapillomavirus\", \"Orthopoxvirus\", \"Simplexvirus\",\n                     \"Lymphocryptovirus\", \"Cytomegalovirus\", \"Dependoparvovirus\")\npath_genera &lt;- bind_rows(tibble(name=path_genera_rna, genome_type=\"RNA genome\"),\n                         tibble(name=path_genera_dna, genome_type=\"DNA genome\")) %&gt;%\n  left_join(viral_taxa, by=\"name\")\n\n# Count in each sample\nn_path_genera &lt;- hv_reads_genus %&gt;% \n  group_by(sample, date, na_type, ctrl, name, taxid) %&gt;% \n  count(name=\"n_reads_viral\") %&gt;% \n  inner_join(path_genera, by=c(\"name\", \"taxid\")) %&gt;%\n  left_join(read_counts_raw, by=c(\"sample\", \"date\", \"na_type\", \"ctrl\")) %&gt;%\n  mutate(p_reads_viral = n_reads_viral/n_reads_raw)\n\n# Pivot out and back to add zero lines\nn_path_genera_out &lt;- n_path_genera %&gt;% ungroup %&gt;% select(sample, name, n_reads_viral) %&gt;%\n  pivot_wider(names_from=\"name\", values_from=\"n_reads_viral\", values_fill=0) %&gt;%\n  pivot_longer(-sample, names_to=\"name\", values_to=\"n_reads_viral\") %&gt;%\n  left_join(read_counts_raw, by=\"sample\") %&gt;%\n  left_join(path_genera, by=\"name\") %&gt;%\n  mutate(p_reads_viral = n_reads_viral/n_reads_raw)\n\n## Aggregate across dates\nn_path_genera_stype &lt;- n_path_genera_out %&gt;% \n  group_by(na_type, ctrl,\n           name, taxid, genome_type) %&gt;%\n  summarize(n_reads_raw = sum(n_reads_raw),\n            n_reads_viral = sum(n_reads_viral), .groups = \"drop\") %&gt;%\n  mutate(sample=\"All samples\", date=\"All dates\",\n         p_reads_viral = n_reads_viral/n_reads_raw)\n\n# Plot\ng_path_genera &lt;- ggplot(n_path_genera_stype,\n                        aes(y=name, x=p_reads_viral, color=na_type)) +\n  geom_point() +\n  scale_x_log10(name=\"Relative abundance\") +\n  scale_color_na() +\n  facet_grid(genome_type~ctrl, scales=\"free_y\") +\n  theme_base + theme(axis.title.y = element_blank())\ng_path_genera\n\nWarning: Transformation introduced infinite values in continuous x-axis\n\n\n\n\n\n\n\n\nConclusion\nThis is the first air-sampling dataset I’ve analyzed using this pipeline, and it was interesting to see the differences from the wastewater datasets I’ve been analyzing so far. Among the more striking differences were:\n\nA much higher total fraction of human reads;\nA lower total fraction of viral reads;\nNear-total absence of enteric viruses and Tobamovirus;\nMuch higher relative abundance of herpesviruses, particularly HCMV, and papillomaviruses among human-infecting virus reads.\n\nConversely, one thing that was not notably different, at least in RNA viruses, was the total relative abundance of human-infecting viruses as a whole. Given the lower fraction of reads made up of all viruses, this means that the fraction of total viruses arising from human-infecting viruses is much higher here than we’ve historically seen with wastewater data. In particular, HCMV represents a nontrivial fraction of total viruses for many DNA libraries, the first time I’ve seen a human pathogen show up significantly in the total virus composition data.\nGoing forward, I have two more air sampling datasets to analyze, Rosario et al. 2018 and Leung et al. 2021. It will be interesting to see whether the findings from this dataset generalize to other air sampling contexts."
   }
 ]
\ No newline at end of file
diff --git a/notebooks/2024-04-08_brumfield.qmd b/notebooks/2024-04-08_brumfield.qmd
index 810b062..ee02b89 100644
--- a/notebooks/2024-04-08_brumfield.qmd
+++ b/notebooks/2024-04-08_brumfield.qmd
@@ -480,7 +480,6 @@ Having made these changes, the workflow identified a total of 14,073 RNA read pa
 
 ```{r}
 #| label: hv-read-counts
-
 hv_reads_filtered_path <- file.path(data_dir, "hv_hits_putative_filtered.tsv.gz")
 hv_reads_filtered <- read_tsv(hv_reads_filtered_path, show_col_types = FALSE) %>%
   inner_join(libraries, by="sample") %>% 
diff --git a/notebooks/2024-04-12_prussin.qmd b/notebooks/2024-04-12_prussin.qmd
new file mode 100644
index 0000000..012e66f
--- /dev/null
+++ b/notebooks/2024-04-12_prussin.qmd
@@ -0,0 +1,1199 @@
+---
+title: "Workflow analysis of Prussin et al. (2019)"
+subtitle: "Wastewater from a manhole in Maryland."
+author: "Will Bradshaw"
+date: 2024-04-12
+format:
+  html:
+    code-fold: true
+    code-tools: true
+    code-link: true
+    df-print: paged
+editor: visual
+title-block-banner: black
+---
+
+```{r}
+#| label: load-packages
+#| include: false
+library(tidyverse)
+library(cowplot)
+library(patchwork)
+library(fastqcr)
+library(RColorBrewer)
+source("../scripts/aux_plot-theme.R")
+theme_base <- theme_base + theme(aspect.ratio = NULL)
+theme_rotate <- theme_base + theme(
+    axis.text.x = element_text(hjust = 1, angle = 45),
+)
+theme_kit <- theme_rotate + theme(
+  axis.title.x = element_blank(),
+)
+tnl <- theme(legend.position = "none")
+```
+
+Taking a break from working on [P2RA datasets](https://doi.org/10.1101/2023.12.22.23300450), we're also working on a review of air sampling for viral pathogen detection. For that study, we're collecting and analyzing air MGS data that could give us a high-level idea of the likely viral composition of such samples.
+
+The first dataset I'm looking at for this work is [Prussin et al. (2019)](https://doi.org/10.1186/s40168-019-0672-z), a study of HVAC filter samples in a Virginia daycare center between 2014 and 2015. Samples were eluted from MERV-14 air filters collected every two weeks, with pairs of successive samples combined into four-week sampling periods. Like Brumfield et al, this study conducted both RNA and DNA sequencing; all samples were sequenced on an Illumina NextSeq500 with 2x150bp reads.
+
+# The raw data
+
+The Prussin dataset comprised sequencing data from 14 timepoints spread across the year, from 20th January 2014 to 2nd February 2015. Each sample represents a four-week sampling period. In addition to the 14 on-site samples, there were also two control samples, a negative control (NC) and an "unexposed filter" control (UFC), which were collected on December 23rd 2014.
+
+```{r}
+#| warning: false
+#| label: read-qc-data
+
+# Data input paths
+data_dir <- "../data/2024-04-11_prussin/"
+libraries_path <- file.path(data_dir, "sample-metadata.csv")
+basic_stats_path <- file.path(data_dir, "qc_basic_stats.tsv")
+adapter_stats_path <- file.path(data_dir, "qc_adapter_stats.tsv")
+quality_base_stats_path <- file.path(data_dir, "qc_quality_base_stats.tsv")
+quality_seq_stats_path <- file.path(data_dir, "qc_quality_sequence_stats.tsv")
+
+# Import libraries and extract metadata from sample names
+libraries_raw <- read_csv(libraries_path, show_col_types = FALSE)
+libraries <- libraries_raw %>%
+  arrange(desc(na_type)) %>% mutate(na_type = fct_inorder(na_type)) %>%
+  arrange(date) %>% rename(start_date = date) %>%
+  mutate(end_date = start_date + 28) %>%
+  mutate(date = fct_inorder(as.character(end_date)),
+         ctrl = ifelse(grepl("Negative_Control", sample_alias), "NC",
+                       ifelse(grepl("Unexposed_Filter", sample_alias),
+                              "UFC", "Non-Control")),
+         ctrl = factor(ctrl, levels = c("Non-Control",
+                                        "NC",
+                                        "UFC")),
+         open = (season != "Closed") & (ctrl == "On-Site"))
+
+# Import QC data
+stages <- c("raw_concat", "cleaned", "dedup", "ribo_initial", "ribo_secondary")
+basic_stats <- read_tsv(basic_stats_path, show_col_types = FALSE) %>%
+  inner_join(libraries, by="sample") %>% 
+  mutate(stage = factor(stage, levels = stages),
+         sample = fct_inorder(sample))
+adapter_stats <- read_tsv(adapter_stats_path, show_col_types = FALSE) %>%
+    mutate(sample = sub("_2$", "", sample)) %>%
+  inner_join(libraries, by="sample") %>%
+  mutate(stage = factor(stage, levels = stages),
+         read_pair = fct_inorder(as.character(read_pair)))
+quality_base_stats <- read_tsv(quality_base_stats_path, show_col_types = FALSE) %>%
+  inner_join(libraries, by="sample") %>%
+  mutate(stage = factor(stage, levels = stages),
+         read_pair = fct_inorder(as.character(read_pair)))
+quality_seq_stats <- read_tsv(quality_seq_stats_path, show_col_types = FALSE) %>%
+  inner_join(libraries, by="sample") %>%
+  mutate(stage = factor(stage, levels = stages),
+         read_pair = fct_inorder(as.character(read_pair)))
+
+# Filter to raw data
+basic_stats_raw <- basic_stats %>% filter(stage == "raw_concat")
+adapter_stats_raw <- adapter_stats %>% filter(stage == "raw_concat")
+quality_base_stats_raw <- quality_base_stats %>% filter(stage == "raw_concat")
+quality_seq_stats_raw <- quality_seq_stats %>% filter(stage == "raw_concat")
+
+# Get key values for readout
+raw_read_counts <- basic_stats_raw %>% group_by(na_type, ctrl) %>% 
+  summarize(rmin = min(n_read_pairs), rmax=max(n_read_pairs),
+            rmean=mean(n_read_pairs), .groups = "drop")
+raw_read_totals <- basic_stats_raw %>% group_by(na_type, ctrl) %>% 
+  summarize(n_read_pairs = sum(n_read_pairs), 
+            n_bases_approx = sum(n_bases_approx), .groups = "drop")
+raw_dup <- basic_stats_raw %>% group_by(na_type, ctrl) %>% 
+  summarize(dmin = min(percent_duplicates), dmax=max(percent_duplicates),
+            dmean=mean(percent_duplicates), .groups = "drop")
+
+```
+
+The 14 positive samples from the dataset yielded 5M-20M (mean 11.3M) RNA-sequencing reads and 10M-42M (mean 21.0M) DNA-sequencing reads per sample, for a total of 159M RNA read pairs and 294M DNA read pairs (46.3 and 87.0 gigabases of sequence, respectively). Controls contributed an additional 1M RNA read pairs and 17.5M DNA read pairs.
+
+In positive samples, read qualities were mostly high but tailed off slightly at the 3' end in some samples, suggesting the need for trimming. Adapter levels were high. Inferred duplication levels were low (2-4%) in DNA reads and moderate (21-44%) in RNA reads. Control sample reads were more problematic, with higher duplication and adapter levels and lower quality.
+
+```{r}
+#| fig-width: 9
+#| warning: false
+#| label: plot-basic-stats
+
+# Prepare data
+basic_stats_raw_metrics <- basic_stats_raw %>%
+  select(date, na_type, ctrl,
+         `# Read pairs` = n_read_pairs,
+         `Total base pairs\n(approx)` = n_bases_approx,
+         `% Duplicates\n(FASTQC)` = percent_duplicates) %>%
+  pivot_longer(-(date:ctrl), names_to = "metric", values_to = "value") %>%
+  mutate(metric = fct_inorder(metric))
+
+# Set up plot templates
+scale_fill_na <- purrr::partial(scale_fill_brewer, palette="Set1", 
+                                name="Nucleic acid type")
+g_basic <- ggplot(basic_stats_raw_metrics, 
+                  aes(x=date, y=value, fill=na_type)) +
+  geom_col(position = "dodge") +
+  scale_x_discrete(name="Collection Date") +
+  scale_y_continuous(expand=c(0,0)) +
+  expand_limits(y=c(0,100)) +
+  scale_fill_na() + 
+  facet_grid(metric~ctrl, scales = "free", space="free_x", switch="y") +
+  theme_rotate + theme(
+    axis.title.y = element_blank(),
+    strip.text.y = element_text(face="plain")
+  )
+g_basic
+```
+
+```{r}
+#| label: plot-raw-quality
+
+# Set up plotting templates
+scale_color_na <- purrr::partial(scale_color_brewer, palette="Set1",
+                                 name="Nucleic acid type")
+g_qual_raw <- ggplot(mapping=aes(color=na_type, linetype=read_pair, 
+                         group=interaction(sample,read_pair))) + 
+  scale_color_na() + scale_linetype_discrete(name = "Read Pair") +
+  guides(color=guide_legend(nrow=2,byrow=TRUE),
+         linetype = guide_legend(nrow=2,byrow=TRUE)) +
+  theme_base
+
+# Visualize adapters
+g_adapters_raw <- g_qual_raw + 
+  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats_raw) +
+  scale_y_continuous(name="% Adapters", limits=c(0,NA),
+                     breaks = seq(0,100,10), expand=c(0,0)) +
+  scale_x_continuous(name="Position", limits=c(0,NA),
+                     breaks=seq(0,140,20), expand=c(0,0)) +
+  facet_grid(ctrl~adapter)
+g_adapters_raw
+
+# Visualize quality
+g_quality_base_raw <- g_qual_raw +
+  geom_hline(yintercept=25, linetype="dashed", color="red") +
+  geom_hline(yintercept=30, linetype="dashed", color="red") +
+  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats_raw) +
+  scale_y_continuous(name="Mean Phred score", expand=c(0,0), limits=c(10,45)) +
+  scale_x_continuous(name="Position", limits=c(0,NA),
+                     breaks=seq(0,140,20), expand=c(0,0)) +
+  facet_grid(ctrl~.)
+g_quality_base_raw
+
+g_quality_seq_raw <- g_qual_raw +
+  geom_vline(xintercept=25, linetype="dashed", color="red") +
+  geom_vline(xintercept=30, linetype="dashed", color="red") +
+  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats_raw) +
+  scale_x_continuous(name="Mean Phred score", expand=c(0,0)) +
+  scale_y_continuous(name="# Sequences", expand=c(0,0)) +
+  facet_grid(ctrl~., scales = "free_y")
+g_quality_seq_raw
+```
+
+# Preprocessing
+
+The average fraction of reads lost at each stage in the preprocessing pipeline is shown in the following table. For positive samples, on average, cleaning and deduplication removed about 19% of total read pairs from RNA libraries and about 7% from DNA libraries. Subsequent ribodepletion removed a further \~32% of total read pairs on average from RNA libraries but \<0.5% of total read pairs from DNA libraries.
+
+Control samples, meanwhile, lost an average of 63% of RNA read pairs and 21% of DNA read pairs during cleaning and deduplication, consistent with the lower qualities observed above. Subsequent ribodepletion removed an additional 9% of total RNA read pairs and 0.3% of total DNA read pairs.
+
+```{r}
+#| label: preproc-table
+n_reads_rel <- basic_stats %>% 
+  select(sample, date, na_type, ctrl, stage, 
+         percent_duplicates, n_read_pairs) %>%
+  group_by(sample, na_type) %>% arrange(sample, na_type, stage) %>%
+  mutate(p_reads_retained = replace_na(n_read_pairs / lag(n_read_pairs), 0),
+         p_reads_lost = 1 - p_reads_retained,
+         p_reads_retained_abs = n_read_pairs / n_read_pairs[1],
+         p_reads_lost_abs = 1-p_reads_retained_abs,
+         p_reads_lost_abs_marginal = replace_na(p_reads_lost_abs - lag(p_reads_lost_abs), 0))
+n_reads_rel_display <- n_reads_rel %>% 
+  rename(Stage=stage, `NA Type`=na_type, `Control?`=ctrl) %>% 
+  group_by(`Control?`, `NA Type`, Stage) %>% 
+  summarize(`% Total Reads Lost (Cumulative)` = paste0(round(min(p_reads_lost_abs*100),1), "-", round(max(p_reads_lost_abs*100),1), " (mean ", round(mean(p_reads_lost_abs*100),1), ")"),
+            `% Total Reads Lost (Marginal)` = paste0(round(min(p_reads_lost_abs_marginal*100),1), "-", round(max(p_reads_lost_abs_marginal*100),1), " (mean ", round(mean(p_reads_lost_abs_marginal*100),1), ")"), .groups="drop") %>% 
+  filter(Stage != "raw_concat") %>%
+  mutate(Stage = Stage %>% as.numeric %>% factor(labels=c("Trimming & filtering", "Deduplication", "Initial ribodepletion", "Secondary ribodepletion")))
+n_reads_rel_display
+```
+
+```{r}
+#| label: preproc-figures
+#| warning: false
+#| fig-height: 8
+#| fig-width: 6
+
+g_stage_trace <- ggplot(basic_stats, aes(x=stage, color=na_type, group=sample)) +
+  scale_color_na() +
+  facet_wrap(ctrl~na_type, scales="free", ncol=2,
+             labeller = label_wrap_gen(multi_line=FALSE)) +
+  theme_kit
+
+# Plot reads over preprocessing
+g_reads_stages <- g_stage_trace +
+  geom_line(aes(y=n_read_pairs)) +
+  scale_y_continuous("# Read pairs", expand=c(0,0), limits=c(0,NA))
+g_reads_stages
+
+# Plot relative read losses during preprocessing
+g_reads_rel <- ggplot(n_reads_rel, aes(x=stage, color=na_type, group=sample)) +
+  geom_line(aes(y=p_reads_lost_abs_marginal)) +
+  scale_y_continuous("% Total Reads Lost", expand=c(0,0), 
+                     labels = function(x) x*100) +
+  scale_color_na() +
+  facet_wrap(ctrl~na_type, scales="free", ncol=2,
+             labeller = label_wrap_gen(multi_line=FALSE)) +
+  theme_kit
+g_reads_rel
+```
+
+In both positive and control samples, data cleaning with FASTP was very successful at removing adapters, with very few adapter sequences found by FASTQC at any stage after the raw data. FASTP was also successful at improving read quality.
+
+```{r}
+#| warning: false
+#| label: plot-quality
+#| fig-height: 7
+
+g_qual <- ggplot(mapping=aes(color=na_type, linetype=read_pair, 
+                         group=interaction(sample,read_pair))) + 
+  scale_color_na() + scale_linetype_discrete(name = "Read Pair") +
+  guides(color=guide_legend(nrow=2,byrow=TRUE),
+         linetype = guide_legend(nrow=2,byrow=TRUE)) +
+  theme_base
+
+# Visualize adapters
+g_adapters <- g_qual + 
+  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats) +
+  scale_y_continuous(name="% Adapters", limits=c(0,20),
+                     breaks = seq(0,50,10), expand=c(0,0)) +
+  scale_x_continuous(name="Position", limits=c(0,NA),
+                     breaks=seq(0,140,20), expand=c(0,0)) +
+  facet_grid(stage~adapter)
+g_adapters
+
+# Visualize quality
+g_quality_base <- g_qual +
+  geom_hline(yintercept=25, linetype="dashed", color="red") +
+  geom_hline(yintercept=30, linetype="dashed", color="red") +
+  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats) +
+  scale_y_continuous(name="Mean Phred score", expand=c(0,0), limits=c(10,45)) +
+  scale_x_continuous(name="Position", limits=c(0,NA),
+                     breaks=seq(0,140,20), expand=c(0,0)) +
+  facet_grid(stage~.)
+g_quality_base
+
+g_quality_seq <- g_qual +
+  geom_vline(xintercept=25, linetype="dashed", color="red") +
+  geom_vline(xintercept=30, linetype="dashed", color="red") +
+  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats) +
+  scale_x_continuous(name="Mean Phred score", expand=c(0,0)) +
+  scale_y_continuous(name="# Sequences", expand=c(0,0)) +
+  facet_grid(stage~.)
+g_quality_seq
+```
+
+According to FASTQC, deduplication was moderately effective at reducing measured duplicate levels in on-site samples, with FASTQC-measured levels falling from an average of 34% to 23% for RNA reads and from 2.7% to 2.2% for DNA reads.
+
+```{r}
+#| label: preproc-dedup
+#| fig-height: 8
+#| fig-width: 6
+
+stage_dup <- basic_stats %>% group_by(na_type, ctrl, stage) %>% 
+  summarize(dmin = min(percent_duplicates), dmax=max(percent_duplicates),
+            dmean=mean(percent_duplicates), .groups = "drop")
+
+g_dup_stages <- g_stage_trace +
+  geom_line(aes(y=percent_duplicates)) +
+  scale_y_continuous("% Duplicates", limits=c(0,NA), expand=c(0,0))
+g_dup_stages
+
+g_readlen_stages <- g_stage_trace + geom_line(aes(y=mean_seq_len)) +
+  scale_y_continuous("Mean read length (nt)", expand=c(0,0), limits=c(0,NA))
+g_readlen_stages
+```
+
+# High-level composition
+
+As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:
+
+```{r}
+#| label: prepare-composition
+
+# Import Bracken data
+bracken_path <- file.path(data_dir, "bracken_counts.tsv")
+bracken <- read_tsv(bracken_path, show_col_types = FALSE)
+total_assigned <- bracken %>% group_by(sample) %>% summarize(
+  name = "Total",
+  kraken_assigned_reads = sum(kraken_assigned_reads),
+  added_reads = sum(added_reads),
+  new_est_reads = sum(new_est_reads),
+  fraction_total_reads = sum(fraction_total_reads)
+)
+bracken_spread <- bracken %>% select(name, sample, new_est_reads) %>%
+  mutate(name = tolower(name)) %>%
+  pivot_wider(id_cols = "sample", names_from = "name", 
+              values_from = "new_est_reads")
+
+# Count reads
+read_counts_preproc <- basic_stats %>% 
+  select(sample, na_type, ctrl, date, stage, n_read_pairs) %>%
+  pivot_wider(id_cols = c("sample", "na_type", "ctrl", "date"),
+              names_from="stage", values_from="n_read_pairs")
+read_counts <- read_counts_preproc %>%
+  inner_join(total_assigned %>% select(sample, new_est_reads), by = "sample") %>%
+  rename(assigned = new_est_reads) %>%
+  inner_join(bracken_spread, by="sample")
+
+# Assess composition
+read_comp <- transmute(read_counts, sample=sample, na_type=na_type,
+                       ctrl = ctrl, date = date,
+                       n_filtered = raw_concat-cleaned,
+                       n_duplicate = cleaned-dedup,
+                       n_ribosomal = (dedup-ribo_initial) + (ribo_initial-ribo_secondary),
+                       n_unassigned = ribo_secondary-assigned,
+                       n_bacterial = bacteria,
+                       n_archaeal = archaea,
+                       n_viral = viruses,
+                       n_human = eukaryota)
+read_comp_long <- pivot_longer(read_comp, -(sample:date), 
+                               names_to = "classification",
+                               names_prefix = "n_", values_to = "n_reads") %>%
+  mutate(classification = fct_inorder(str_to_sentence(classification))) %>%
+  group_by(sample) %>% mutate(p_reads = n_reads/sum(n_reads))
+
+# Summarize composition
+read_comp_summ <- read_comp_long %>% 
+  group_by(na_type, ctrl, classification) %>%
+  summarize(n_reads = sum(n_reads), .groups = "drop_last") %>%
+  mutate(n_reads = replace_na(n_reads,0),
+    p_reads = n_reads/sum(n_reads),
+    pc_reads = p_reads*100)
+```
+
+```{r}
+#| label: plot-composition-all
+
+# Prepare plotting templates
+g_comp_base <- ggplot(mapping=aes(x=date, y=p_reads, fill=classification)) +
+  scale_x_discrete(name="Collection Date") +
+  facet_grid(na_type~ctrl, scales = "free", space = "free_x") +
+  theme_rotate
+scale_y_pc_reads <- purrr::partial(scale_y_continuous, name = "% Reads",
+                                   expand = c(0,0), labels = function(y) y*100)
+
+# Plot overall composition
+g_comp <- g_comp_base + geom_col(data = read_comp_long, position = "stack") +
+  scale_y_pc_reads(limits = c(0,1.01), breaks = seq(0,1,0.2)) +
+  scale_fill_brewer(palette = "Set1", name = "Classification")
+g_comp
+
+# Plot composition of minor components
+read_comp_minor <- read_comp_long %>% 
+  filter(classification %in% c("Archaeal", "Viral", "Other"))
+palette_minor <- brewer.pal(9, "Set1")[c(6,7,9)]
+g_comp_minor <- g_comp_base + geom_col(data=read_comp_minor, position = "stack") +
+  scale_y_pc_reads() +
+  scale_fill_manual(values=palette_minor, name = "Classification")
+g_comp_minor
+
+```
+
+```{r}
+#| label: composition-summary
+
+p_reads_summ_group <- read_comp_long %>%
+  mutate(classification = ifelse(classification %in% c("Filtered", "Duplicate", "Unassigned"), "Excluded", as.character(classification)),
+         classification = fct_inorder(classification)) %>%
+  group_by(classification, sample, na_type, ctrl) %>%
+  summarize(p_reads = sum(p_reads), .groups = "drop") %>%
+  group_by(classification, na_type, ctrl) %>%
+  summarize(pc_min = min(p_reads)*100, pc_max = max(p_reads)*100, 
+            pc_mean = mean(p_reads)*100, .groups = "drop")
+p_reads_summ_prep <- p_reads_summ_group %>%
+  mutate(classification = fct_inorder(classification),
+         pc_min = pc_min %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
+         pc_max = pc_max %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
+         pc_mean = pc_mean %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
+         display = paste0(pc_min, "-", pc_max, "% (mean ", pc_mean, "%)"))
+p_reads_summ <- p_reads_summ_prep %>%
+  select(ctrl, classification, na_type, display) %>%
+  pivot_wider(names_from=na_type, values_from = display) %>% 
+  arrange(ctrl, classification)
+p_reads_summ
+```
+
+What we see is a very different picture from the wastewater samples I've been analyzing so far. Most notably, the fraction of (non-ribosomal) human reads is *much* higher. Even the 2015-01-20 samples, which were taken when the daycare center was closed for the winter holidays, showed human read fractions (for both nucleic-acid types) of \>12%; non-control samples as a whole averaged 27% for RNA reads and 34% for DNA reads. Compare [Brumfield](https://data.securebio.org/wills-public-notebook/notebooks/2024-04-08_brumfield.html) (average 0.08% for RNA and 0.02% for DNA), [Yang](https://data.securebio.org/wills-public-notebook/notebooks/2024-03-16_yang.html) (mean 0.05% for RNA) or even [Rothman](https://data.securebio.org/wills-public-notebook/notebooks/2024-02-27_rothman-1.html) (mean 1.8% for non-panel-enriched RNA samples).
+
+Conversely, total viral reads are very low: mean 0.033% for RNA reads and 0.019% for DNA reads. Wastewater RNA datasets have typically had much higher total viruses: mean 0.5% for Brumfield, about the same for Crits-Christoph, 5.5% for Yang, 4.5% for Rothman. Brumfield's DNA data contained substantially fewer viruses than their RNA data, but still more than Prussin: about 0.08% on average.
+
+Looking at viral families...was less informative than usual, especially for DNA reads. It turns out that these samples contain a lot of viral reads that Kraken2 was only able to classify to the class level. In DNA reads, samples were dominated by *Caudoviricetes* phages, though *Herviviricetes* (which includes herpesviruses) and *Naldaviricetes* (a class of arthropod-infecting viruses) also put in a respectable showing. In RNA reads, *Caudoviricetes* was again a major presence, but *Alsuviricetes* (a family of primarily plant pathogens) was often as or more prevalent, and *Revtraviricetes* (a class that includes Hepatitis B virus and retroviruses) was also significant.
+
+```{r}
+#| label: extract-viral-taxa
+
+# Get viral taxonomy
+viral_taxa_path <- file.path(data_dir, "viral-taxids.tsv.gz")
+viral_taxa <- read_tsv(viral_taxa_path, show_col_types = FALSE)
+
+# Import Kraken reports & extract viral taxa
+samples <- as.character(basic_stats_raw$sample)
+col_names <- c("pc_reads_total", "n_reads_clade", "n_reads_direct",
+               "rank", "taxid", "name")
+report_paths <- paste0(data_dir, "kraken/", samples, ".report.gz")
+kraken_reports <- lapply(1:length(samples), function(n) 
+  read_tsv(report_paths[n], col_names = col_names, show_col_types = FALSE) %>%
+    mutate(sample = samples[n])) %>% bind_rows
+kraken_reports_viral <- filter(kraken_reports, taxid %in% viral_taxa$taxid) %>%
+  group_by(sample) %>%
+  mutate(p_reads_viral = n_reads_clade/n_reads_clade[1])
+kraken_reports_viral_cleaned <- kraken_reports_viral %>%
+  inner_join(libraries, by="sample") %>%
+  select(-pc_reads_total, -n_reads_direct) %>%
+  select(name, taxid, p_reads_viral, n_reads_clade, everything())
+
+viral_classes <- kraken_reports_viral_cleaned %>% filter(rank == "C")
+viral_families <- kraken_reports_viral_cleaned %>% filter(rank == "F")
+
+```
+
+```{r}
+#| label: viral-class-composition
+
+# Identify major viral classes
+viral_classes_major_tab <- viral_classes %>% 
+  group_by(name, taxid) %>%
+  summarize(p_reads_viral_max = max(p_reads_viral), .groups="drop") %>%
+  filter(p_reads_viral_max >= 0.04)
+viral_classes_major_list <- viral_classes_major_tab %>% pull(name)
+viral_classes_major <- viral_classes %>% 
+  filter(name %in% viral_classes_major_list) %>%
+  select(name, taxid, sample, na_type, ctrl, date, p_reads_viral)
+viral_classes_minor <- viral_classes_major %>% 
+  group_by(sample, na_type, ctrl, date) %>%
+  summarize(p_reads_viral_major = sum(p_reads_viral), .groups = "drop") %>%
+  mutate(name = "Other", taxid=NA, p_reads_viral = 1-p_reads_viral_major) %>%
+  select(name, taxid, sample, na_type, ctrl, date, p_reads_viral)
+viral_classes_display <- bind_rows(viral_classes_major, viral_classes_minor) %>%
+  arrange(desc(p_reads_viral)) %>% 
+  mutate(name = factor(name, levels=c(viral_classes_major_list, "Other"))) %>%
+  rename(p_reads = p_reads_viral, classification=name)
+
+palette_viral <- c(brewer.pal(12, "Set3"), brewer.pal(8, "Set2"))
+g_classes <- g_comp_base + 
+  geom_col(data=viral_classes_display, position = "stack") +
+  scale_y_continuous(name="% Viral Reads", limits=c(0,1.01), breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral class")
+g_classes
+
+```
+
+Of these, the most interesting are the strong presence of *Herviviricetes* in the DNA reads and *Revtraviricetes* in the RNA reads, as both of these are families that contain important human pathogens.
+
+Digging into the former, it turns out these reads are composed almost exclusively of *Herpesviridae* at the family level. Within non-control samples, these arise overwhelmingly from *Cytomegalovirus.* Digging in at the species level, these in turn are primarily attributed to a single CMV, Human betaherpesvirus 5, a.k.a. human cytomegalovirus (HCMV). I was excited to see this: this is the first time a single human pathogen, or even all human pathogens combined, have constituted a significant fraction of all viral reads in a sample I've analyzed with this pipeline.
+
+```{r}
+#| label: herviviricetes
+
+# Get all read counts in class
+hervi_taxid <- 2731363
+hervi_desc_taxids_old <- hervi_taxid
+hervi_desc_taxids_new <- unique(c(hervi_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% hervi_desc_taxids_old) %>% pull(taxid)))
+while (length(hervi_desc_taxids_new) > length(hervi_desc_taxids_old)){
+  hervi_desc_taxids_old <- hervi_desc_taxids_new
+  hervi_desc_taxids_new <- unique(c(hervi_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% hervi_desc_taxids_old) %>% pull(taxid)))
+}
+hervi_counts <- kraken_reports_viral_cleaned %>%
+  filter(taxid %in% hervi_desc_taxids_new) %>%
+  mutate(p_reads_hervi = n_reads_clade/n_reads_clade[1])
+
+# Get genus composition
+hervi_genera <- hervi_counts %>% filter(rank == "S", na_type == "DNA")
+hervi_genera_major_tab <- hervi_genera %>% 
+  group_by(name, taxid) %>%
+  summarize(p_reads_hervi_max = max(p_reads_hervi), .groups="drop") %>%
+  filter(p_reads_hervi_max >= 0.04)
+hervi_genera_major_list <- hervi_genera_major_tab %>% pull(name)
+hervi_genera_major <- hervi_genera %>% 
+  filter(name %in% hervi_genera_major_list) %>%
+  select(name, taxid, sample, na_type, ctrl, date, p_reads_hervi)
+hervi_genera_minor <- hervi_genera_major %>% 
+  group_by(sample, na_type, ctrl, date) %>%
+  summarize(p_reads_hervi_major = sum(p_reads_hervi), .groups = "drop") %>%
+  mutate(name = "Other", taxid=NA, p_reads_hervi = 1-p_reads_hervi_major) %>%
+  select(name, taxid, sample, na_type, ctrl, date, p_reads_hervi)
+hervi_genera_display <- bind_rows(hervi_genera_major, hervi_genera_minor) %>%
+  arrange(desc(p_reads_hervi)) %>% 
+  mutate(name = factor(name, levels=c(hervi_genera_major_list, "Other"))) %>%
+  rename(p_reads = p_reads_hervi, classification=name)
+
+# Plot
+g_hervi_genera <- g_comp_base + 
+  geom_col(data=hervi_genera_display, position = "stack") +
+  scale_y_continuous(name="% Herviviricetes Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral species")
+g_hervi_genera
+
+```
+
+*Revtraviricetes* reads are similarly dominated by a single viral genus, *Alpharetrovirus.* Digging in at the species level, we see contributions from a variety of avian oncoviruses. To my (admittedly non-expert) knowledge, none of these infect humans, and I think they are probably primarily arising from local birds (or possibly rats).
+
+```{r}
+#| label: revtraviricetes
+
+# Get all read counts in class
+revtra_taxid <- 2732514
+revtra_desc_taxids_old <- revtra_taxid
+revtra_desc_taxids_new <- unique(c(revtra_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% revtra_desc_taxids_old) %>% pull(taxid)))
+while (length(revtra_desc_taxids_new) > length(revtra_desc_taxids_old)){
+  revtra_desc_taxids_old <- revtra_desc_taxids_new
+  revtra_desc_taxids_new <- unique(c(revtra_desc_taxids_old, viral_taxa %>% filter(parent_taxid %in% revtra_desc_taxids_old) %>% pull(taxid)))
+}
+revtra_counts <- kraken_reports_viral_cleaned %>%
+  filter(taxid %in% revtra_desc_taxids_new) %>%
+  mutate(p_reads_revtra = n_reads_clade/n_reads_clade[1])
+
+# Get genus composition
+revtra_species <- revtra_counts %>% filter(rank == "S", na_type == "RNA")
+revtra_species_major_tab <- revtra_species %>% 
+  group_by(name, taxid) %>%
+  summarize(p_reads_revtra_max = max(p_reads_revtra), .groups="drop") %>%
+  filter(p_reads_revtra_max >= 0.04)
+revtra_species_major_list <- revtra_species_major_tab %>% pull(name)
+revtra_species_major <- revtra_species %>% 
+  filter(name %in% revtra_species_major_list) %>%
+  select(name, taxid, sample, na_type, ctrl, date, p_reads_revtra)
+revtra_species_minor <- revtra_species_major %>% 
+  group_by(sample, na_type, ctrl, date) %>%
+  summarize(p_reads_revtra_major = sum(p_reads_revtra), .groups = "drop") %>%
+  mutate(name = "Other", taxid=NA, p_reads_revtra = 1-p_reads_revtra_major) %>%
+  select(name, taxid, sample, na_type, ctrl, date, p_reads_revtra)
+revtra_species_display <- bind_rows(revtra_species_major, revtra_species_minor) %>%
+  arrange(desc(p_reads_revtra)) %>% 
+  mutate(name = factor(name, levels=c(revtra_species_major_list, "Other"))) %>%
+  rename(p_reads = p_reads_revtra, classification=name)
+
+# Plot
+g_revtra_species <- g_comp_base + 
+  geom_col(data=revtra_species_display, position = "stack") +
+  scale_y_continuous(name="% Revtraviricetes Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral species")
+g_revtra_species
+
+```
+
+# Human-infecting virus reads: validation
+
+Next, I investigated the human-infecting virus read content of these unenriched samples. Using the same workflow I used for Brumfield et al, I identified 2811 RNA read pairs and 12792 DNA read pairs as putatively human viral: 0.003% and 0.005% of surviving reads, respectively.
+
+```{r}
+#| label: hv-read-counts
+hv_reads_filtered_path <- file.path(data_dir, "hv_hits_putative_filtered.tsv.gz")
+hv_reads_filtered <- read_tsv(hv_reads_filtered_path, show_col_types = FALSE) %>%
+  inner_join(libraries, by="sample") %>% 
+  arrange(date, na_type)
+n_hv_filtered <- hv_reads_filtered %>% group_by(sample, date, na_type, ctrl) %>% count %>% inner_join(basic_stats %>% filter(stage == "ribo_initial") %>% select(sample, n_read_pairs), by="sample") %>% rename(n_putative = n, n_total = n_read_pairs) %>% mutate(p_reads = n_putative/n_total, pc_reads = p_reads * 100)
+n_hv_filtered_summ <- n_hv_filtered %>% group_by(na_type, ctrl) %>% summarize(n_putative = sum(n_putative), n_total = sum(n_total), .groups="drop") %>% mutate(p_reads = n_putative/n_total, pc_reads = p_reads*100)
+```
+
+```{r}
+#| label: plot-hv-scores
+#| warning: false
+#| fig-width: 8
+
+mrg <- hv_reads_filtered %>%
+  mutate(kraken_label = ifelse(assigned_hv, "Kraken2 HV\nassignment",
+                               ifelse(hit_hv, "Kraken2 HV\nhit",
+                                      "No hit or\nassignment"))) %>%
+  group_by(sample, na_type) %>% arrange(desc(adj_score_fwd), desc(adj_score_rev)) %>%
+  mutate(seq_num = row_number(),
+         adj_score_max = pmax(adj_score_fwd, adj_score_rev))
+# Import Bowtie2/Kraken data and perform filtering steps
+g_mrg <- ggplot(mrg, aes(x=adj_score_fwd, y=adj_score_rev)) +
+  geom_point(alpha=0.5, shape=16) + 
+  scale_x_continuous(name="S(forward read)", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +
+  scale_y_continuous(name="S(reverse read)", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +
+  facet_grid(na_type~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +
+  theme_base + theme(aspect.ratio=1)
+g_mrg
+
+g_hist_0 <- ggplot(mrg, aes(x=adj_score_max)) + geom_histogram(binwidth=5,boundary=0,position="dodge") + facet_grid(na_type~kraken_label, scales="free_y") + scale_x_continuous(name = "Maximum adjusted alignment score") + scale_y_continuous(name="# Read pairs") + scale_fill_brewer(palette = "Dark2") + theme_base + geom_vline(xintercept=20, linetype="dashed", color="red")
+g_hist_0
+```
+
+As previously described, I ran BLASTN on these reads via a dedicated EC2 instance, using the same parameters I've used for previous datasets.
+
+```{r}
+#| label: make-blast-fasta
+mrg_fasta <-  mrg %>%
+  mutate(seq_head = paste0(">", seq_id)) %>%
+  ungroup %>%
+  select(header1=seq_head, seq1=query_seq_fwd, 
+         header2=seq_head, seq2=query_seq_rev) %>%
+  mutate(header1=paste0(header1, "_1"), header2=paste0(header2, "_2"))
+mrg_fasta_out <- do.call(paste, c(mrg_fasta, sep="\n")) %>% paste(collapse="\n")
+write(mrg_fasta_out, file.path(data_dir, "blast/putative-viral.fasta"))
+```
+
+```{r}
+#| label: process-blast-data
+#| warning: false
+
+# Import BLAST results
+# Pre-filtering BLAST results to save space
+# blast_results_path <- file.path(data_dir, "blast/putative-viral.blast.gz")
+# blast_cols <- c("qseqid", "sseqid", "sgi", "staxid", "qlen", "evalue", "bitscore", "qcovs", "length", "pident", "mismatch", "gapopen", "sstrand", "qstart", "qend", "sstart", "send")
+# blast_results <- read_tsv(blast_results_path, show_col_types = FALSE,
+#                           col_names = blast_cols, col_types = cols(.default="c"))
+blast_results_path <- file.path(data_dir, "blast/putative-viral-best.blast.gz")
+blast_results <- read_tsv(blast_results_path, show_col_types = FALSE)
+
+# Filter for best hit for each query/subject combination
+blast_results_best <- blast_results %>% group_by(qseqid, staxid) %>% 
+  filter(bitscore == max(bitscore)) %>%
+  filter(length == max(length)) %>% filter(row_number() == 1)
+
+# Rank hits for each query and filter for high-ranking hits
+blast_results_ranked <- blast_results_best %>% 
+  group_by(qseqid) %>% mutate(rank = dense_rank(desc(bitscore)))
+blast_results_highrank <- blast_results_ranked %>% filter(rank <= 5) %>%
+    mutate(read_pair = str_split(qseqid, "_") %>% sapply(nth, n=-1), 
+         seq_id = str_split(qseqid, "_") %>% sapply(nth, n=1)) %>%
+    mutate(bitscore = as.numeric(bitscore))
+
+# Summarize by read pair and taxid
+blast_results_paired <- blast_results_highrank %>%
+  group_by(seq_id, staxid) %>%
+  summarize(bitscore_max = max(bitscore), bitscore_min = min(bitscore),
+            n_reads = n(), .groups = "drop")
+
+# Add viral status
+blast_results_viral <- mutate(blast_results_paired, viral = staxid %in% viral_taxa$taxid) %>%
+  mutate(viral_full = viral & n_reads == 2)
+
+# Compare to Kraken & Bowtie assignments
+mrg_assign <- mrg %>% select(sample, seq_id, taxid, assigned_taxid, adj_score_max, na_type)
+blast_results_assign <- left_join(blast_results_viral, mrg_assign, by="seq_id") %>%
+    mutate(taxid_match_bowtie = (staxid == taxid),
+           taxid_match_kraken = (staxid == assigned_taxid),
+           taxid_match_any = taxid_match_bowtie | taxid_match_kraken)
+blast_results_out <- blast_results_assign %>%
+  group_by(seq_id) %>%
+  summarize(viral_status = ifelse(any(viral_full), 2,
+                                  ifelse(any(taxid_match_any), 2,
+                                             ifelse(any(viral), 1, 0))),
+            .groups = "drop")
+```
+
+```{r}
+#| label: plot-blast-results
+#| fig-height: 6
+#| warning: false
+
+# Merge BLAST results with unenriched read data
+mrg_blast <- full_join(mrg, blast_results_out, by="seq_id") %>%
+  mutate(viral_status = replace_na(viral_status, 0),
+         viral_status_out = ifelse(viral_status == 0, FALSE, TRUE))
+mrg_blast_rna <- mrg_blast %>% filter(na_type == "RNA")
+mrg_blast_dna <- mrg_blast %>% filter(na_type == "DNA")
+
+# Plot RNA
+g_mrg_blast_rna <- mrg_blast_rna %>%
+  ggplot(aes(x=adj_score_fwd, y=adj_score_rev, color=viral_status_out)) +
+  geom_point(alpha=0.5, shape=16) +
+  scale_x_continuous(name="S(forward read)", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +
+  scale_y_continuous(name="S(reverse read)", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +
+  scale_color_brewer(palette = "Set1", name = "Viral status") +
+  facet_grid(viral_status_out~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +
+  theme_base + labs(title="RNA") + 
+  theme(aspect.ratio=1, plot.title = element_text(size=rel(2), hjust=0))
+g_mrg_blast_rna
+
+# Plot DNA
+g_mrg_blast_dna <- mrg_blast_dna %>%
+  ggplot(aes(x=adj_score_fwd, y=adj_score_rev, color=viral_status_out)) +
+  geom_point(alpha=0.5, shape=16) +
+  scale_x_continuous(name="S(forward read)", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +
+  scale_y_continuous(name="S(reverse read)", limits=c(0,65), breaks=seq(0,100,10), expand = c(0,0)) +
+  scale_color_brewer(palette = "Set1", name = "Viral status") +
+  facet_grid(viral_status_out~kraken_label, labeller = labeller(kit = label_wrap_gen(20))) +
+  theme_base + labs(title="DNA") + 
+  theme(aspect.ratio=1, plot.title = element_text(size=rel(2), hjust=0))
+g_mrg_blast_dna
+```
+
+```{r}
+#| label: plot-blast-histogram
+g_hist_1 <- ggplot(mrg_blast, aes(x=adj_score_max)) + geom_histogram(binwidth=5,boundary=0,position="dodge") + facet_grid(na_type~viral_status_out, scales = "free_y") + scale_x_continuous(name = "Maximum adjusted alignment score") + scale_y_continuous(name="# Read pairs") + scale_fill_brewer(palette = "Dark2") + theme_base + geom_vline(xintercept=20, linetype="dashed", color="red")
+g_hist_1
+```
+
+These results look good on visual inspection, and indeed precision and sensitivity are both very high. For a disjunctive score threshold of 20, my updated workflow achieves an F1 score of 96.7% for RNA sequences and 98.2% for DNA sequences.
+
+```{r}
+#| label: plot-f1
+test_sens_spec <- function(tab, score_threshold, conjunctive, include_special){
+  if (!include_special) tab <- filter(tab, viral_status_out %in% c("TRUE", "FALSE"))
+  tab_retained <- tab %>% mutate(
+    conjunctive = conjunctive,
+    retain_score_conjunctive = (adj_score_fwd > score_threshold & adj_score_rev > score_threshold), 
+    retain_score_disjunctive = (adj_score_fwd > score_threshold | adj_score_rev > score_threshold),
+    retain_score = ifelse(conjunctive, retain_score_conjunctive, retain_score_disjunctive),
+    retain = assigned_hv | hit_hv | retain_score) %>%
+    group_by(viral_status_out, retain) %>% count
+  pos_tru <- tab_retained %>% filter(viral_status_out == "TRUE", retain) %>% pull(n) %>% sum
+  pos_fls <- tab_retained %>% filter(viral_status_out != "TRUE", retain) %>% pull(n) %>% sum
+  neg_tru <- tab_retained %>% filter(viral_status_out != "TRUE", !retain) %>% pull(n) %>% sum
+  neg_fls <- tab_retained %>% filter(viral_status_out == "TRUE", !retain) %>% pull(n) %>% sum
+  sensitivity <- pos_tru / (pos_tru + neg_fls)
+  specificity <- neg_tru / (neg_tru + pos_fls)
+  precision   <- pos_tru / (pos_tru + pos_fls)
+  f1 <- 2 * precision * sensitivity / (precision + sensitivity)
+  out <- tibble(threshold=score_threshold, include_special = include_special, 
+                conjunctive = conjunctive, sensitivity=sensitivity, 
+                specificity=specificity, precision=precision, f1=f1)
+  return(out)
+}
+range_f1 <- function(intab, inc_special, inrange=15:45){
+  tss <- purrr::partial(test_sens_spec, tab=intab, include_special=inc_special)
+  stats_conj <- lapply(inrange, tss, conjunctive=TRUE) %>% bind_rows
+  stats_disj <- lapply(inrange, tss, conjunctive=FALSE) %>% bind_rows
+  stats_all <- bind_rows(stats_conj, stats_disj) %>%
+    pivot_longer(!(threshold:conjunctive), names_to="metric", values_to="value") %>%
+    mutate(conj_label = ifelse(conjunctive, "Conjunctive", "Disjunctive"))
+  return(stats_all)
+}
+inc_special <- FALSE
+stats_rna <- range_f1(mrg_blast_rna, inc_special) %>% mutate(na_type = "RNA")
+stats_dna <- range_f1(mrg_blast_dna, inc_special) %>% mutate(na_type = "DNA")
+stats_0 <- bind_rows(stats_rna, stats_dna) %>% mutate(attempt=0)
+threshold_opt_0 <- stats_0 %>% group_by(conj_label,attempt,na_type) %>% 
+  filter(metric == "f1") %>%
+  filter(value == max(value)) %>% filter(threshold == min(threshold))
+g_stats_0 <- ggplot(stats_0, aes(x=threshold, y=value, color=metric)) +
+  geom_vline(data = threshold_opt_0, mapping = aes(xintercept=threshold),
+             color = "red", linetype = "dashed") +
+  geom_line() +
+  scale_y_continuous(name = "Value", limits=c(0,1), breaks = seq(0,1,0.2), expand = c(0,0)) +
+  scale_x_continuous(name = "Threshold", expand = c(0,0)) +
+  scale_color_brewer(palette="Set3") +
+  facet_grid(na_type~conj_label) +
+  theme_base
+g_stats_0
+```
+
+Looking into the composition of different read groups, nothing stands out except the predominance of HCMV (human betaherpesvirus 5), which is consistent with the Kraken results above and borne out by the BLASTN alignments:
+
+```{r}
+#| label: fp
+#| fig-height: 6
+
+viral_taxa$name[viral_taxa$taxid == 211787] <- "Human papillomavirus type 92"
+viral_taxa$name[viral_taxa$taxid == 509154] <- "Porcine endogenous retrovirus C"
+
+major_threshold <- 0.05
+fp <- mrg_blast %>% 
+  group_by(na_type, viral_status_out, 
+           highscore = adj_score_max >= 20, taxid) %>% count %>% 
+  group_by(na_type, viral_status_out, highscore) %>% mutate(p=n/sum(n)) %>% 
+  left_join(viral_taxa, by="taxid") %>%
+  arrange(desc(p)) %>%
+  mutate(name = ifelse(taxid == 194958, "Porcine endogenous retrovirus A", name))
+fp_major_tab <- fp %>% filter(p > major_threshold) %>% arrange(desc(p))
+fp_major_list <- fp_major_tab %>% pull(name) %>% sort %>% unique %>% c(., "Other")
+fp_major <- fp %>% mutate(major = p > major_threshold) %>% 
+  mutate(name_display = ifelse(major, name, "Other")) %>%
+  group_by(na_type, viral_status_out, highscore, name_display) %>% 
+  summarize(n=sum(n), p=sum(p), .groups = "drop")  %>%
+  mutate(name_display = factor(name_display, levels = fp_major_list),
+         score_display = ifelse(highscore, "S >= 20", "S < 20"),
+         status_display = ifelse(viral_status_out, "True positive", "False positive"))
+g_fp <- ggplot(fp_major, aes(x=score_display, y=p, fill=name_display)) +
+  geom_col(position="stack") +
+  scale_x_discrete(name = "True positive?") +
+  scale_y_continuous(name = "% reads", limits = c(0,1.01), 
+                     breaks = seq(0,1,0.2), expand = c(0,0)) +
+  scale_fill_manual(values = palette_viral, name = "Viral\ntaxon") +
+  facet_grid(na_type~status_display) +
+  guides(fill=guide_legend(ncol=3)) +
+  theme_kit
+g_fp
+
+```
+
+```{r}
+#| label: hcmv-blast-hits
+
+# Configure
+ref_taxid <- 10359
+
+# Get taxon names
+tax_names_path <- file.path(data_dir, "taxid-names.tsv.gz")
+tax_names <- read_tsv(tax_names_path, show_col_types = FALSE)
+
+# Add missing names
+tax_names_new <- tribble(~staxid, ~name,
+                         3050295, "Cytomegalovirus humanbeta5",
+                         459231, "FLAG-tagging vector pFLAG97-TSR")
+tax_names <- bind_rows(tax_names, tax_names_new)
+ref_name <- tax_names %>% filter(staxid == ref_taxid) %>% pull(name)
+
+# Get major matches
+fp_staxid <- mrg_blast %>% filter(taxid == ref_taxid) %>%
+  group_by(na_type, highscore = adj_score_max >= 20) %>% mutate(n_seq = n()) %>%
+  left_join(blast_results_paired, by="seq_id") %>%
+  mutate(staxid = as.integer(staxid)) %>%
+  left_join(tax_names, by="staxid") %>% rename(sname=name) %>%
+  left_join(tax_names %>% rename(taxid=staxid), by="taxid")
+fp_staxid_count <- fp_staxid %>%
+  group_by(viral_status_out, highscore, na_type, 
+           taxid, name, staxid, sname, n_seq) %>%
+  count %>%
+  group_by(viral_status_out, highscore, na_type, taxid, name) %>%
+  mutate(p=n/n_seq)
+fp_staxid_count_major <- fp_staxid_count %>%
+  filter(n>1, p>0.1, !is.na(staxid)) %>%
+  mutate(score_display = ifelse(highscore, "S >= 20", "S < 20"),
+         status_display = ifelse(viral_status_out, 
+                                 "True positive", "False positive"))
+
+# Plot
+g <- ggplot(fp_staxid_count_major, aes(x=p, y=sname)) + 
+  geom_col() + 
+  facet_grid(na_type~status_display+score_display, scales="free",
+             labeller = label_wrap_gen(multi_line = FALSE)) +
+  scale_x_continuous(name="% mapped reads", limits=c(0,1), breaks=seq(0,1,0.2),
+                     expand=c(0,0)) +
+  labs(title=paste0(ref_name, " (taxid ", ref_taxid, ")")) +
+  theme_base + theme(
+    axis.title.y = element_blank(),
+    plot.title = element_text(size=rel(1.5), hjust=0, face="plain"))
+g
+```
+
+# Human-infecting viruses: overall relative abundance
+
+```{r}
+#| label: count-hv-reads
+
+# Get raw read counts
+read_counts_raw <- basic_stats_raw %>%
+  select(sample, date, na_type, ctrl, n_reads_raw = n_read_pairs)
+
+# Get HV read counts
+mrg_hv <- mrg %>% mutate(hv_status = assigned_hv | hit_hv | adj_score_max >= 20)
+read_counts_hv <- mrg_hv %>% filter(hv_status) %>% group_by(sample) %>% 
+  count(name="n_reads_hv")
+read_counts <- read_counts_raw %>% left_join(read_counts_hv, by="sample") %>%
+  mutate(n_reads_hv = replace_na(n_reads_hv, 0))
+
+# Aggregate
+read_counts_total <- read_counts %>% group_by(na_type, ctrl) %>%
+  summarize(n_reads_raw = sum(n_reads_raw),
+            n_reads_hv = sum(n_reads_hv), .groups="drop") %>%
+  mutate(sample= "All samples", date = "All dates")
+read_counts_agg <- read_counts %>% arrange(date) %>%
+  arrange(sample) %>%
+  bind_rows(read_counts_total) %>% 
+  mutate(sample = fct_inorder(sample),
+         p_reads_hv = n_reads_hv/n_reads_raw)
+```
+
+In non-control samples, applying a disjunctive cutoff at S=20 identifies 2591 RNA reads and 12236 DNA reads as human-viral. This gives an overall relative HV abundance of $1.63 \times 10^{-5}$ for RNA reads and $4.16 \times 10^{-5}$ for DNA reads. Reassuringly, relative abundances in control samples are at least 10x lower. We also see a sharp drop in relative abundance for both nucleic-acid types for the period when the daycare was closed (2015-01-20):
+
+```{r}
+#| label: plot-hv-ra
+#| warning: false
+# Visualize
+g_phv_agg <- ggplot(read_counts_agg, aes(x=date, color=na_type)) +
+  geom_point(aes(y=p_reads_hv)) +
+  scale_y_log10("Relative abundance of human virus reads") +
+  scale_x_discrete(name="Collection Date") +
+  facet_grid(.~ctrl, scales = "free", space = "free_x") +
+  scale_color_na() + theme_rotate
+g_phv_agg
+```
+
+These overall RA values are similar to those we've seen previously for non-panel-enriched wastewater RNA data. That said, it's notable that the DNA read RA seen here is much higher than that seen in the only DNA wastewater dataset I've analyzed so far (Brumfield):
+
+```{r}
+#| label: ra-hv-past
+
+# Collate past RA values
+ra_past <- tribble(~dataset, ~ra, ~na_type, ~panel_enriched,
+                   "Brumfield", 5e-5, "RNA", FALSE,
+                   "Brumfield", 3.66e-7, "DNA", FALSE,
+                   "Spurbeck", 5.44e-6, "RNA", FALSE,
+                   "Yang", 3.62e-4, "RNA", FALSE,
+                   "Rothman (unenriched)", 1.87e-5, "RNA", FALSE,
+                   "Rothman (panel-enriched)", 3.3e-5, "RNA", TRUE,
+                   "Crits-Christoph (unenriched)", 1.37e-5, "RNA", FALSE,
+                   "Crits-Christoph (panel-enriched)", 1.26e-2, "RNA", TRUE)
+
+# Collate new RA values
+ra_new <- tribble(~dataset, ~ra, ~na_type, ~panel_enriched,
+                   "Prussin (non-control)", 1.63e-5, "RNA", FALSE,
+                   "Prussin (non-control)", 4.16e-5, "DNA", FALSE)
+
+# Plot
+ra_comp <- bind_rows(ra_past, ra_new) %>% mutate(dataset = fct_inorder(dataset))
+g_ra_comp <- ggplot(ra_comp, aes(y=dataset, x=ra, color=na_type)) +
+  geom_point() +
+  scale_color_na() +
+  scale_x_log10(name="Relative abundance of human virus reads") +
+  theme_base + theme(axis.title.y = element_blank())
+g_ra_comp
+```
+
+# Human-infecting viruses: taxonomy and composition
+
+At the family level, we see that *Papillomaviridae*, *Herpesviridae*, and *Polyomaviridae* are the most abundant families in both DNA and RNA reads, with *Adenoviridae* and (in RNA reads) *Picornaviridae* also making a respectable showing. The *Herpesviridae* reads are, predictably, overwhelmingly from HCMV, while the *Papillomaviridae* and *Polyomaviridae* reads are split up among a larger number of related viruses:
+
+```{r}
+#| label: raise-hv-taxa
+
+# Get viral taxon names for putative HV reads
+viral_taxa$name[viral_taxa$taxid == 249588] <- "Mamastrovirus"
+viral_taxa$name[viral_taxa$taxid == 194960] <- "Kobuvirus"
+viral_taxa$name[viral_taxa$taxid == 688449] <- "Salivirus"
+viral_taxa$name[viral_taxa$taxid == 585893] <- "Picobirnaviridae"
+viral_taxa$name[viral_taxa$taxid == 333922] <- "Betapapillomavirus"
+viral_taxa$name[viral_taxa$taxid == 334207] <- "Betapapillomavirus 3"
+viral_taxa$name[viral_taxa$taxid == 369960] <- "Porcine type-C oncovirus"
+viral_taxa$name[viral_taxa$taxid == 333924] <- "Betapapillomavirus 2"
+viral_taxa$name[viral_taxa$taxid == 687329] <- "Anelloviridae"
+viral_taxa$name[viral_taxa$taxid == 325455] <- "Gammapapillomavirus"
+viral_taxa$name[viral_taxa$taxid == 333750] <- "Alphapapillomavirus"
+viral_taxa$name[viral_taxa$taxid == 694002] <- "Betacoronavirus"
+viral_taxa$name[viral_taxa$taxid == 334202] <- "Mupapillomavirus"
+viral_taxa$name[viral_taxa$taxid == 197911] <- "Alphainfluenzavirus"
+viral_taxa$name[viral_taxa$taxid == 186938] <- "Respirovirus"
+viral_taxa$name[viral_taxa$taxid == 333926] <- "Gammapapillomavirus 1"
+viral_taxa$name[viral_taxa$taxid == 337051] <- "Betapapillomavirus 1"
+viral_taxa$name[viral_taxa$taxid == 337043] <- "Alphapapillomavirus 4"
+viral_taxa$name[viral_taxa$taxid == 694003] <- "Betacoronavirus 1"
+viral_taxa$name[viral_taxa$taxid == 334204] <- "Mupapillomavirus 2"
+viral_taxa$name[viral_taxa$taxid == 334208] <- "Betapapillomavirus 4"
+
+viral_taxa$name[viral_taxa$taxid == 334208] <- "Betapapillomavirus 4"
+viral_taxa$name[viral_taxa$taxid == 334208] <- "Betapapillomavirus 4"
+viral_taxa$name[viral_taxa$taxid == 334208] <- "Betapapillomavirus 4"
+
+
+mrg_hv_named <- mrg_hv %>% left_join(viral_taxa, by="taxid")
+
+# Discover viral species & genera for HV reads
+raise_rank <- function(read_db, taxid_db, out_rank = "species", verbose = FALSE){
+  # Get higher ranks than search rank
+  ranks <- c("subspecies", "species", "subgenus", "genus", "subfamily", "family", "suborder", "order", "class", "subphylum", "phylum", "kingdom", "superkingdom")
+  rank_match <- which.max(ranks == out_rank)
+  high_ranks <- ranks[rank_match:length(ranks)]
+  # Merge read DB and taxid DB
+  reads <- read_db %>% select(-parent_taxid, -rank, -name) %>%
+    left_join(taxid_db, by="taxid")
+  # Extract sequences that are already at appropriate rank
+  reads_rank <- filter(reads, rank == out_rank)
+  # Drop sequences at a higher rank and return unclassified sequences
+  reads_norank <- reads %>% filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))
+  while(nrow(reads_norank) > 0){ # As long as there are unclassified sequences...
+    # Promote read taxids and re-merge with taxid DB, then re-classify and filter
+    reads_remaining <- reads_norank %>% mutate(taxid = parent_taxid) %>%
+      select(-parent_taxid, -rank, -name) %>%
+      left_join(taxid_db, by="taxid")
+    reads_rank <- reads_remaining %>% filter(rank == out_rank) %>%
+      bind_rows(reads_rank)
+    reads_norank <- reads_remaining %>%
+      filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))
+  }
+  # Finally, extract and append reads that were excluded during the process
+  reads_dropped <- reads %>% filter(!seq_id %in% reads_rank$seq_id)
+  reads_out <- reads_rank %>% bind_rows(reads_dropped) %>%
+    select(-parent_taxid, -rank, -name) %>%
+    left_join(taxid_db, by="taxid")
+  return(reads_out)
+}
+hv_reads_species <- raise_rank(mrg_hv_named, viral_taxa, "species")
+hv_reads_genus <- raise_rank(mrg_hv_named, viral_taxa, "genus")
+hv_reads_family <- raise_rank(mrg_hv_named, viral_taxa, "family")
+```
+
+```{r}
+#| label: hv-family
+#| fig-height: 6
+
+threshold_major_family <- 0.06
+
+# Count reads for each human-viral family
+hv_family_counts <- hv_reads_family %>% 
+  group_by(sample, date, ctrl, na_type, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date, ctrl, na_type) %>%
+  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+hv_family_major_tab <- hv_family_counts %>% group_by(name) %>% 
+  filter(p_reads_hv == max(p_reads_hv)) %>% filter(row_number() == 1) %>%
+  arrange(desc(p_reads_hv)) %>% filter(p_reads_hv > threshold_major_family)
+hv_family_counts_major <- hv_family_counts %>%
+  mutate(name_display = ifelse(name %in% hv_family_major_tab$name, name, "Other")) %>%
+  group_by(sample, date, ctrl, na_type, name_display) %>%
+  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(hv_family_major_tab$name, "Other")))
+hv_family_counts_display <- hv_family_counts_major %>%
+  rename(p_reads = p_reads_hv, classification = name_display)
+
+# Plot
+g_hv_family <- g_comp_base + 
+  geom_col(data=hv_family_counts_display, position = "stack") +
+  scale_y_continuous(name="% HV Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral family")
+g_hv_family
+```
+
+```{r}
+#| label: hv-genus
+#| fig-height: 6
+
+threshold_major_genus <- 0.06
+
+# Count reads for each human-viral genus
+hv_genus_counts <- hv_reads_genus %>% 
+  group_by(sample, date, ctrl, na_type, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date, ctrl, na_type) %>%
+  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+hv_genus_major_tab <- hv_genus_counts %>% group_by(name) %>% 
+  filter(p_reads_hv == max(p_reads_hv)) %>% filter(row_number() == 1) %>%
+  arrange(desc(p_reads_hv)) %>% filter(p_reads_hv > threshold_major_genus)
+hv_genus_counts_major <- hv_genus_counts %>%
+  mutate(name_display = ifelse(name %in% hv_genus_major_tab$name, name, "Other")) %>%
+  group_by(sample, date, ctrl, na_type, name_display) %>%
+  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(hv_genus_major_tab$name, "Other")))
+hv_genus_counts_display <- hv_genus_counts_major %>%
+  rename(p_reads = p_reads_hv, classification = name_display)
+
+# Plot
+g_hv_genus <- g_comp_base + 
+  geom_col(data=hv_genus_counts_display, position = "stack") +
+  scale_y_continuous(name="% HV Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral genus")
+g_hv_genus
+```
+
+```{r}
+#| label: hv-species
+#| fig-height: 6
+
+threshold_major_species <- 0.10
+
+# Count reads for each human-viral species
+hv_species_counts <- hv_reads_species %>% 
+  group_by(sample, date, ctrl, na_type, name, taxid) %>%
+  count(name = "n_reads_hv") %>%
+  group_by(sample, date, ctrl, na_type) %>%
+  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))
+
+# Identify high-ranking families and group others
+hv_species_major_tab <- hv_species_counts %>% group_by(name) %>% 
+  filter(p_reads_hv == max(p_reads_hv)) %>% filter(row_number() == 1) %>%
+  arrange(desc(p_reads_hv)) %>% filter(p_reads_hv > threshold_major_species)
+hv_species_counts_major <- hv_species_counts %>%
+  mutate(name_display = ifelse(name %in% hv_species_major_tab$name, name, "Other")) %>%
+  group_by(sample, date, ctrl, na_type, name_display) %>%
+  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), 
+            .groups="drop") %>%
+  mutate(name_display = factor(name_display, 
+                               levels = c(hv_species_major_tab$name, "Other")))
+hv_species_counts_display <- hv_species_counts_major %>%
+  rename(p_reads = p_reads_hv, classification = name_display)
+
+# Plot
+g_hv_species <- g_comp_base + 
+  geom_col(data=hv_species_counts_display, position = "stack") +
+  scale_y_continuous(name="% HV Reads", limits=c(0,1.01), 
+                     breaks = seq(0,1,0.2),
+                     expand=c(0,0), labels = function(y) y*100) +
+  scale_fill_manual(values=palette_viral, name = "Viral species")
+g_hv_species
+```
+
+Compared to the previous datasets I've analyzed, the most notable difference is the absence of enteric viruses: *Norovirus*, *Rotavirus*, and *Enterovirus* are all absent from the list of abundant human-viral taxa, as are \~all other gastrointestinal pathogens.
+
+Finally, here are the overall relative abundances of a set of specific viral genera picked out manually on the basis of scientific or medical interest. In the future, I'll quantify the RA of these genera across all datasets analyzed with this pipeline to date, which should give us a better sense of how these data compare to others' under this pipeline.
+
+```{r}
+#| fig-height: 5
+#| label: ra-genera
+
+# Define reference genera
+path_genera_rna <- c("Mamastrovirus", "Enterovirus", "Salivirus", "Kobuvirus", "Norovirus", "Sapovirus", "Rotavirus", "Alphacoronavirus", "Betacoronavirus",
+                 "Alphainfluenzavirus", "Betainfluenzavirus", "Lentivirus")
+path_genera_dna <- c("Mastadenovirus", "Alphapolyomavirus", "Betapolyomavirus", "Alphapapillomavirus", "Betapapillomavirus", "Orthopoxvirus", "Simplexvirus",
+                     "Lymphocryptovirus", "Cytomegalovirus", "Dependoparvovirus")
+path_genera <- bind_rows(tibble(name=path_genera_rna, genome_type="RNA genome"),
+                         tibble(name=path_genera_dna, genome_type="DNA genome")) %>%
+  left_join(viral_taxa, by="name")
+
+# Count in each sample
+n_path_genera <- hv_reads_genus %>% 
+  group_by(sample, date, na_type, ctrl, name, taxid) %>% 
+  count(name="n_reads_viral") %>% 
+  inner_join(path_genera, by=c("name", "taxid")) %>%
+  left_join(read_counts_raw, by=c("sample", "date", "na_type", "ctrl")) %>%
+  mutate(p_reads_viral = n_reads_viral/n_reads_raw)
+
+# Pivot out and back to add zero lines
+n_path_genera_out <- n_path_genera %>% ungroup %>% select(sample, name, n_reads_viral) %>%
+  pivot_wider(names_from="name", values_from="n_reads_viral", values_fill=0) %>%
+  pivot_longer(-sample, names_to="name", values_to="n_reads_viral") %>%
+  left_join(read_counts_raw, by="sample") %>%
+  left_join(path_genera, by="name") %>%
+  mutate(p_reads_viral = n_reads_viral/n_reads_raw)
+
+## Aggregate across dates
+n_path_genera_stype <- n_path_genera_out %>% 
+  group_by(na_type, ctrl,
+           name, taxid, genome_type) %>%
+  summarize(n_reads_raw = sum(n_reads_raw),
+            n_reads_viral = sum(n_reads_viral), .groups = "drop") %>%
+  mutate(sample="All samples", date="All dates",
+         p_reads_viral = n_reads_viral/n_reads_raw)
+
+# Plot
+g_path_genera <- ggplot(n_path_genera_stype,
+                        aes(y=name, x=p_reads_viral, color=na_type)) +
+  geom_point() +
+  scale_x_log10(name="Relative abundance") +
+  scale_color_na() +
+  facet_grid(genome_type~ctrl, scales="free_y") +
+  theme_base + theme(axis.title.y = element_blank())
+g_path_genera
+```
+
+# Conclusion
+
+This is the first air-sampling dataset I've analyzed using this pipeline, and it was interesting to see the differences from the wastewater datasets I've been analyzing so far. Among the more striking differences were:
+
+-   A much higher total fraction of human reads;
+
+-   A lower total fraction of viral reads;
+
+-   Near-total absence of enteric viruses and *Tobamovirus*;
+
+-   Much higher relative abundance of herpesviruses, particularly HCMV, and papillomaviruses among human-infecting virus reads.
+
+Conversely, one thing that was not notably different, at least in RNA viruses, was the total relative abundance of human-infecting viruses as a whole. Given the lower fraction of reads made up of all viruses, this means that the fraction of total viruses arising from human-infecting viruses is much higher here than we've historically seen with wastewater data. In particular, HCMV represents a nontrivial fraction of total viruses for many DNA libraries, the first time I've seen a human pathogen show up significantly in the total virus composition data.
+
+Going forward, I have two more air sampling datasets to analyze, [Rosario et al. 2018](https://doi.org/10.1186/s40168-021-01044-7) and [Leung et al. 2021](https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-021-01044-7). It will be interesting to see whether the findings from this dataset generalize to other air sampling contexts.