Merge branch 'dev-ratio' into dev-ratio-genesets

assets/schema_tools.json

@@ -12,8 +12,9 @@
             },
             "diff_method": {
                 "type": "string",
-                "errorMessage": "choose a differential analysis method (eg. deseq2, propd, limme, etc) or none",
-                "meta": ["diff_method"]
+                "errorMessage": "choose a differential analysis method (eg. deseq2, propd) or none",
+                "meta": ["diff_method"],
+                "enum": ["propd", "deseq2", "none"]
             },
             "args_diff": {
                 "type": "string",
@@ -22,7 +23,8 @@
             "cor_method": {
                 "type": "string",
                 "meta": ["cor_method"],
-                "errorMessage": "choose a correlation method (eg. propr) or none"
+                "errorMessage": "choose a correlation method (eg. propr) or none",
+                "enum": ["propr", "none"]
             },
             "args_cor": {
                 "type": "string",

assets/tools_samplesheet.csv

@@ -7,4 +7,4 @@ cor,,,propr,--metric cor,,
 propd_grea,propd,,,,grea,
 propd_ora,propd,,,,gprofiler2,
 deseq2,deseq2,,,,,
-deseq2_ora,deseq2,,,,gprofiler2,
+deseq2_ora,deseq2,,,,gprofiler2,

subworkflows/local/correlation/main.nf

@@ -3,9 +3,16 @@
 //
 include {PROPR_PROPR as PROPR} from "../../../modules/local/propr/propr/main.nf"
 
+def correct_meta_data = { meta, data, pathway ->
+    def meta_clone = meta.clone() + pathway
+    meta_clone.remove('cor_method')
+    meta_clone.remove('args_cor')
+    return [meta_clone, data]
+}
+
 workflow CORRELATION {
     take:
-    ch_tools
+    ch_tools        // [ pathway_name, correlation_map ]
     ch_counts
 
     main:
@@ -17,7 +24,7 @@ workflow CORRELATION {
     // branch tools to select the correct correlation analysis method
     ch_tools
         .branch {
-            propr:  it[0]["cor_method"] == "propr"
+            propr:  it[1]["cor_method"] == "propr"
         }
         .set { ch_tools_single }
 
@@ -27,19 +34,22 @@ workflow CORRELATION {
 
     ch_counts
         .combine(ch_tools_single.propr)
-        .map {
-            metacounts, counts, metatools ->
-                [ metacounts+metatools, counts ]
+        .multiMap {
+            metacounts, counts, pathway, metatools ->
+                input:   [ metacounts+metatools, counts ]
+                pathway: [ metacounts+metatools, pathway ]
         }
         .set { ch_counts_propr }
 
-    PROPR(ch_counts_propr)
-    ch_matrix = ch_matrix.mix(PROPR.out.matrix)
-    ch_adjacency = ch_adjacency.mix(PROPR.out.adjacency)
+    PROPR(ch_counts_propr.input.unique())
+    ch_matrix    = PROPR.out.matrix
+                        .join(ch_counts_propr.pathway).map(correct_meta_data).mix(ch_matrix)
+    ch_adjacency = PROPR.out.adjacency
+                        .join(ch_counts_propr.pathway).map(correct_meta_data).mix(ch_adjacency)
 
     // TODO: divide propr module into cor, propr, pcor, pcorbshrink, etc.
 
     emit:
-    matrix = ch_matrix
+    matrix    = ch_matrix
     adjacency = ch_adjacency
 }

subworkflows/local/differential/main.nf

@@ -5,12 +5,19 @@ include { PROPR_PROPD as PROPD } from "../../../modules/local/propr/propd/main.n
 include { DESEQ2_DIFFERENTIAL as DESEQ2 } from '../../../modules/nf-core/deseq2/differential/main'
 include { FILTER_DIFFTABLE as FILTER_DESEQ2 } from '../../../modules/local/filter_difftable'
 
+def correct_meta_data = { meta, data, pathway ->
+    def meta_clone = meta.clone() + pathway
+    meta_clone.remove('diff_method')
+    meta_clone.remove('args_diff')
+    return [meta_clone, data]
+}
+
 workflow DIFFERENTIAL {
     take:
-    ch_tools
+    ch_tools        // [ pathway_name, differential_map ]
     ch_counts
     ch_samplesheet
-    ch_contrasts
+    ch_contrasts    // [meta, contrast_variable, reference, target]
     ch_control_features
     ch_transcript_lengths
 
@@ -26,8 +33,8 @@ workflow DIFFERENTIAL {
     // branch tools to select the correct differential analysis method
     ch_tools
         .branch {
-            propd:  it[0]["diff_method"] == "propd"
-            deseq2: it[0]["diff_method"] == "deseq2"
+            propd:  it[1]["diff_method"] == "propd"
+            deseq2: it[1]["diff_method"] == "deseq2"
         }
         .set { ch_tools_single }
 
@@ -39,21 +46,25 @@ workflow DIFFERENTIAL {
         .join(ch_samplesheet)
         .combine(ch_contrasts)
         .combine(ch_tools_single.propd)
-        .map {
-            meta_data, counts, samplesheet, meta_contrast, contrast_variable, reference, target, meta_tools ->
+        .multiMap {
+            meta_data, counts, samplesheet, meta_contrast, contrast_variable, reference, target, pathway, meta_tools ->
                 def meta = meta_data.clone() + ['contrast': meta_contrast.id] + meta_tools.clone()
-                [ meta, counts, samplesheet, contrast_variable, reference, target ]
+                input:   [ meta, counts, samplesheet, contrast_variable, reference, target ]
+                pathway: [ meta, pathway ]
         }
-        .unique()
         .set { ch_propd }
 
-    PROPD(ch_propd)
-
-    ch_results_pairwise = ch_results_pairwise.mix(PROPD.out.results)
-    ch_results_pairwise_filtered = ch_results_pairwise_filtered.mix(PROPD.out.results_filtered)
-    ch_results_genewise = ch_results_genewise.mix(PROPD.out.connectivity)
-    ch_results_genewise_filtered = ch_results_genewise_filtered.mix(PROPD.out.hub_genes)
-    ch_adjacency = ch_adjacency.mix(PROPD.out.adjacency)
+    PROPD(ch_propd.input.unique())
+    ch_results_pairwise          = PROPD.out.results
+                                        .join(ch_propd.pathway).map(correct_meta_data).mix(ch_results_pairwise)
+    ch_results_pairwise_filtered = PROPD.out.results_filtered
+                                        .join(ch_propd.pathway).map(correct_meta_data).mix(ch_results_pairwise_filtered)
+    ch_results_genewise          = PROPD.out.connectivity
+                                        .join(ch_propd.pathway).map(correct_meta_data).mix(ch_results_genewise)
+    ch_results_genewise_filtered = PROPD.out.hub_genes
+                                        .join(ch_propd.pathway).map(correct_meta_data).mix(ch_results_genewise_filtered)
+    ch_adjacency                 = PROPD.out.adjacency
+                                        .join(ch_propd.pathway).map(correct_meta_data).mix(ch_adjacency)
 
     // ----------------------------------------------------
     // Perform differential analysis with DESeq2

subworkflows/local/enrichment/main.nf

@@ -7,6 +7,7 @@ include { GPROFILER2_GOST } from "../../../modules/nf-core/gprofiler2/gost/main.
 
 workflow ENRICHMENT {
     take:
+    ch_tools        // [ pathway_name, enrichment_map ]
     ch_counts
     ch_results_genewise
     ch_results_genewise_filtered
@@ -47,10 +48,20 @@ workflow ENRICHMENT {
     // ----------------------------------------------------
 
     ch_adjacency
-        .filter { it[0]["enr_method"] == "grea" }
-        .set { ch_adjacency_grea }
+        .map { meta, matrix -> [meta.subMap(["pathway_name"]), meta, matrix] }
+        .join(ch_tools, by: [0])
+        .map {
+            pathway_name, meta, matrix, meta_tools ->
+                def new_meta = meta.clone() + meta_tools.clone()
+                [ new_meta, matrix ]
+            }
+        .branch {
+            grea:  it[0]["enr_method"] == "grea"
+            gsea: it[0]["enr_method"] == "gsea"
+        }
+        .set { ch_adjacency }
 
-    GREA(ch_adjacency_grea, ch_gene_sets.collect())
+    GREA(ch_adjacency.grea, ch_gene_sets.collect())
     ch_enriched = ch_enriched.mix(GREA.out.results)
 
     // ----------------------------------------------------

subworkflows/local/experimental/main.nf

@@ -7,15 +7,23 @@ include { ENRICHMENT }   from '../enrichment/main.nf'
 
 workflow EXPERIMENTAL {
     take:
-    ch_contrasts
-    ch_samplesheet
-    ch_counts
-    ch_tools
+    ch_contrasts    // [ meta, contrast_variable, reference, target ]
+    ch_samplesheet  // [ meta, samplesheet ]
+    ch_counts       // [ meta, counts]
+    ch_tools        // [ pathway_name, differential_map, correlation_map, enrichment_map ]
 
     main:
 
-    // check tools
-    ch_tools.view()
+    // split toolsheet into channels
+    ch_tools
+        .multiMap{
+            pathway_name, differential_map, correlation_map, enrichment_map ->
+                diff: [ pathway_name, differential_map ]
+                corr: [ pathway_name, correlation_map ]
+                enr:  [ pathway_name, enrichment_map ]
+        }
+        .set{ ch_tools }
+
 
     // initialize empty results channels
     ch_results_pairwise = Channel.empty()               // differential results for pairwise analysis - it should be a table
@@ -44,7 +52,7 @@ workflow EXPERIMENTAL {
     }
 
     DIFFERENTIAL(
-        ch_tools,
+        ch_tools.diff,
         ch_counts,
         ch_samplesheet,
         ch_contrasts,
@@ -62,7 +70,7 @@ workflow EXPERIMENTAL {
     // ----------------------------------------------------
 
     CORRELATION(
-        ch_tools,
+        ch_tools.corr,
         ch_counts
     )
     ch_matrix = ch_matrix.mix(CORRELATION.out.matrix)
@@ -73,6 +81,7 @@ workflow EXPERIMENTAL {
     // ----------------------------------------------------
 
     ENRICHMENT(
+        ch_tools.enr,
         ch_counts,
         ch_results_genewise,
         ch_results_genewise_filtered,

workflows/differentialabundance.nf

@@ -363,8 +363,16 @@ workflow DIFFERENTIALABUNDANCE {
     // TODO the experimental branch should be independent from this file
     } else if (params.study_type == 'experimental') {
 
-        // Convert the samplesheet.csv in a channel with the proper format
+        // Convert the toolsheet.csv in a channel with the proper format
         ch_tools = Channel.fromList(samplesheetToList(params.tools, './assets/schema_tools.json'))
+                    .map {
+                        it ->
+                            def pathway_name     = it[0].subMap(["pathway_name"])
+                            def differential_map = it[0].subMap(["diff_method","args_diff"])
+                            def correlation_map  = it[0].subMap(["cor_method","args_cor"])
+                            def enrichment_map   = it[0].subMap(["enr_method","args_enr"])
+                            [ pathway_name, differential_map, correlation_map, enrichment_map ]
+                    }.unique()
 
         // Filter the tools to the pathway(s) of interest, or run everything if requested
         if (params.pathway == "all") {