From 58ee35e922794b93ef3519f4e636f471a2f889e2 Mon Sep 17 00:00:00 2001 From: Joe Brown Date: Thu, 28 Jun 2018 13:58:39 -0700 Subject: [PATCH] updates appearance --- index.html | 374 + notebooks/html_file.ipynb | 383 - notebooks/lca.ipynb | 556 - notebooks/perseq_graphs.ipynb | 412 - notebooks/plotly_button_prototyping.ipynb | 56693 -------------------- resources/summary_quality.png | Bin 0 -> 192026 bytes resources/summary_table.png | Bin 0 -> 163376 bytes resources/summary_taxonomy.png | Bin 0 -> 130220 bytes 8 files changed, 374 insertions(+), 58044 deletions(-) create mode 100644 index.html delete mode 100644 notebooks/html_file.ipynb delete mode 100644 notebooks/lca.ipynb delete mode 100644 notebooks/perseq_graphs.ipynb delete mode 100644 notebooks/plotly_button_prototyping.ipynb create mode 100644 resources/summary_quality.png create mode 100644 resources/summary_table.png create mode 100644 resources/summary_taxonomy.png diff --git a/index.html b/index.html new file mode 100644 index 0000000..ae9f319 --- /dev/null +++ b/index.html @@ -0,0 +1,374 @@ + + + + + + +PerSeq - Per sequence functional and taxonomic assignments + + + +
+

PerSeq - Per sequence functional and taxonomic assignments

+ + + + + + + + + + + +
+

Summary

+
+

Sequence Counts

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SampleSequencesPairs + JoinedJoin + RateAverage + InsertUniqueCleanAssigned + FunctionAssigned + TaxonomyAssigned + Both
S082000005471054.710%164.949732383062633657486
S0162000004093340.933%184.8388733409440671120785
S0172000004808648.086%183.9448703723044311381948
S0302000005392153.921%188.2470443995737251081702
+
+
+

Sequence Quality

+
+
+

Taxonomy by Count

+

Samples are sorted based on their Shannon index calculated from taxonomically +annotated sequences. The order is most to least diverse.

+
+
+

Taxonomy by Percent

+
+
+
+

Methods

+

Paired-end sequences were evaluated for quality using VSEARCH [1]. Sequence +reads are quality trimmed after successful merging using bbmerge [2]. +Sequences are allowed to be extended up 300 bp +during the merging process to account for non-overlapping R1 and R2 sequences +(k=60 extend2=60 iterations=5 qtrim2=t). Merged sequences are deduplicated +using the clumpify tool [2] then, by default, filtered of PhiX and +rRNA using bbsplit [2]. An arbitrary number of Name:FASTA pairs may be +specified during the decontamination process. Functional annotation and +taxonomic classification were performed following the decontamination step.

+
+

Functional Annotation

+

The blastx algorithm of DIAMOND [3] was used to align nucleotide sequences to +the KEGG protein reference database [4] consisting of non-redundant, family +level fungal eukaryotes and genus level prokaryotes +(--strand=both --evalue 0.00001). The highest scoring alignment per +sequence was used for functional annotation.

+
+
+

Taxonomic Annotation

+

Kmer-based taxonomic classification was performed on the merged reads using +Kaiju [5] in greedy mode (-a greedy -E 0.05). NCBI's nr database [6] +containing reference sequences for archaea, bacteria, viruses, fungi, and +microbial eukaryotes was used as the reference index for Kaiju.

+
+
+

References

+
    +
  1. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. PeerJ Inc; 2016;4:e2584.
    2. +
  2. Bushnell B. BBTools [Internet]. Available from: https://sourceforge.net/projects/bbmap/
    3. +
  3. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat. Methods. Nature Publishing Group; 2015;12:59–60.
    4. +
  4. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44:D457–62.
    5. +
  5. Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. Nature Publishing Group; 2016;7:11257.
    6. +
  6. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46:D8–D13.
    7. +
+
+
+
+

Execution Environment

+
+channels:
+    - bioconda
+    - conda-forge
+    - defaults
+dependencies:
+    - python=3.6
+    - bbmap=37.99
+    - click=6.7
+    - diamond=0.9.21
+    - kaiju=1.6.2
+    - numpy
+    - pandas=0.23.1
+    - plotly=2.7.0
+    - snakemake>=5.1.3
+    - vsearch=2.6.0
+
+
+
+

Output

+
+

Classification Tables

+

Per sample classifications in tables/ contain:

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Header IDDefinition
aa_alignment_lengthThe length of the DIAMOND blastx hit
aa_percent_idThe percent ID of the DIAMOND blastx hit; could be used to increase post-processing stringency
ecEnzyme Commission number from KEGG; semicolon delimited where multiple
koKEGG entry ID
productKEGG gene ID <semicolon> KEGG product
read_idThe sequence identifier (unique)
kaiju_alignment_lengthThe length of the Kaiju hit
kaiju_classificationThe Kaiju classification in order of superkingdom, phylum, order, class, family, genus, species; "NA" for each taxonomic level not defined
blastx_lca_classificationThe LCA result from the blastx HSPs
+
+
+

Summary Tables

+
+

Taxonomy

+

Per taxonomy assignments in tables named summaries/taxonomy/<level>.txt +contain:

+ ++++ + + + + + + + + + + + + + +
Header IDDefinition
taxonomy_<level>taxonomic level into which counts have been summed
samples namesnon-normalized, per sample sum at this taxonomic level
+
+
+

Function

+

Per function assignments in tables named summaries/function/<type>.txt +contain:

+ ++++ + + + + + + + + + + + + + + + + + + + + + + +
Header IDDefinition
<type>either KO, EC, or product into which counts have been summed
samples namesnon-normalized, per sample sum for this particular functional group
level_1KEGG hierarchy [level 1] if KO defined in first column
level_2KEGG hierarchy [level 2] if KO defined in first column
level_3KEGG hierarchy [level 3] if KO defined in first column
+
+
+

Combined

+

Per taxonomy+function assignments in tables named +summaries/combined/<type>_<level>.txt contain:

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + +
Header IDDefinition
<type>either KO, EC, or product; counts are summed using <type>+<taxonomy>
taxonomy_<level>taxonomic level; counts are summed using <type>+<taxonomy>
sample namesnon-normalized, per sample sum for this particular functional group
level_1KEGG hierarchy [level 1] if KO defined in first column
level_2KEGG hierarchy [level 2] if KO defined in first column
level_3KEGG hierarchy [level 3] if KO defined in first column
+
+
+
+
+

Downloads

+
+
+
+
+
file1:
+
ko.txt
+
+
+
+
+
file2:
+
phylum.txt
+
+
+
+
+
file3:
+
ko_phylum.txt
+
+
+
+
+
+2018-06-28
+
+
+ + diff --git a/notebooks/html_file.ipynb b/notebooks/html_file.ipynb deleted file mode 100644 index c642f84..0000000 --- a/notebooks/html_file.ipynb +++ /dev/null @@ -1,383 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import argparse\n", - "import os\n", - "import pandas as pd\n", - "import plotly.graph_objs as go\n", - "from plotly import offline\n", - "from cufflinks import iplot\n", - "from snakemake.utils import report\n", - "\n", - "PLOTLY_PARAMS = dict(\n", - " include_plotlyjs=False, show_link=False, output_type=\"div\", image_height=700\n", - ")\n", - "\n", - "\n", - "#parses log_file for extra info\n", - "\n", - "# def parse_log_file(log_file, keyword, expect_one_value=True):\n", - "# content = open(log_file).read()\n", - "# pos = content.find(keyword)\n", - "# if pos == -1:\n", - "# raise Exception(\"Didn't find {} in file:\\n\\n{}\".format(keyword, log_file))\n", - "\n", - "# else:\n", - "# if expect_one_value:\n", - "# return content[pos:].split()[2]\n", - "\n", - "# else:\n", - "# return content[pos:].split()[2:]\n", - "\n", - "#dependent functions for the others\n", - "#function to parse kaiju output\n", - "\n", - "#parse the kaiju output\n", - "def parse_align(alignment_file):\n", - " total_lines=0\n", - " order_dict={}\n", - " class_dict={}\n", - " phylum_dict={}\n", - " total_align=[]\n", - " #for alignment_file in file_list:\n", - " with open(alignment_file) as file:\n", - " classified=0\n", - " unc=0\n", - " for line in file:\n", - "\n", - "\n", - " total_lines+=1\n", - " toks=line.strip().split('\\t')\n", - " #print(toks)\n", - " classification=toks[0]\n", - "\n", - " if classification =='C':\n", - " classified+=1\n", - " bits=toks[7].split(';')\n", - " order=bits[3].strip()\n", - " clas=bits[2]\n", - " phylum=bits[1]\n", - " if order in order_dict:\n", - " order_dict[order]+=1\n", - " else:\n", - " order_dict[order]=1\n", - " if clas in class_dict:\n", - " class_dict[clas]+=1\n", - " else:\n", - " class_dict[clas]=1\n", - " if phylum in phylum_dict:\n", - " phylum_dict[phylum]+=1\n", - " else:\n", - " phylum_dict[phylum]=1\n", - " else:\n", - " unc+=1\n", - " total=classified+unc\n", - " # or should this be a list. prob list\n", - " #print('family',family)\n", - " return order_dict,class_dict,phylum_dict,total \n", - "\n", - "\n", - "#calculates the order for diversity \n", - "def gini(x, corr=False):\n", - " \"\"\"Calculates Gini coefficient, the measure of inequality among values\n", - " of a frequency distribution. A Gini coefficient of zero expresses\n", - " perfect equality.\n", - "\n", - " Port from ineq package in R::\n", - "\n", - " > library(ineq)\n", - " > t <- c(1,2,6,7,8)\n", - " > Gini(t)\n", - " [1] 0.3166667\n", - " > Gini(t, corr=TRUE)\n", - " [1] 0.3958333\n", - "\n", - " Args:\n", - " x (list): list or array of numbers\n", - " corr (Optional[bool]): finite sample correction\n", - "\n", - " Returns:\n", - " float\n", - "\n", - " >>> import numpy as np\n", - " >>> t = [1,2,6,7,8]\n", - " >>> gini(t) # doctest: +ELLIPSIS\n", - " 0.3166...\n", - " >>> gini(t, corr=True) # doctest: +ELLIPSIS\n", - " 0.3958...\n", - " >>> gini([]) # doctest: +ELLIPSIS\n", - " Traceback (most recent call last):\n", - " ...\n", - " AssertionError: x is empty\n", - " >>> t = [1,2,6,7,\"A\"]\n", - " >>> gini(t) # doctest: +ELLIPSIS\n", - " Traceback (most recent call last):\n", - " ...\n", - " ValueError: could not convert...\n", - " \"\"\"\n", - " x = np.array(x, dtype=float)\n", - " # filter out nan values as list is coming from merged dataframe\n", - " x = x[~np.isnan(x)]\n", - " n = len(x)\n", - " assert n > 0, \"x is empty\"\n", - " x.sort(kind=\"mergesort\")\n", - " G = sum(np.arange(1, n + 1) * x)\n", - " G = 2 * G / sum(x) - (n + 1)\n", - " if corr:\n", - " return G / (n - 1)\n", - " else:\n", - " return G / n\n", - " \n", - "#step 2\n", - "#so this will make the counts information that we need. but this should be put into a function because it needs to be done\n", - "#for all three of the taxa\n", - "import numpy as np\n", - "def process_reads(tax_level, pandas_df):\n", - " '''This function will take the merged reads from the file and put them into a graphable\n", - " form for both counts and percents. It will return both. '''\n", - " #cols = [i for i in m.columns.tolist() if i.startswith(\"Percent_\")]\n", - " header=merged_phy_final.columns.tolist()\n", - " header.pop(0)\n", - " full= merged_phy_final.sort_values(by=header,ascending=False) #organize these by count\n", - "\n", - " sample_order = []\n", - " for read_col in merged_phy_final.columns.tolist(): #this will make a list of the gini coefficient of where these should be located by diversity of the sample\n", - " if read_col.startswith(\"Reads_\"):\n", - " sample_order.append([read_col.strip(\"Reads_\"), gini(merged_phy_final[read_col])])\n", - " sample_order = sorted(sample_order, key=lambda x: x[1])\n", - "\n", - " sub = full[[tax_level] + [\"Reads_%s\" % sample_order[i][0] for i in range(len(sample_order))]]\n", - " sub = sub.head(100)\n", - " cols=sub[tax_level].tolist()\n", - "\n", - " #find the percent\n", - " sub_perc=sub[header].div(total_counts)\n", - " sub_perc_t=sub_perc.transpose()\n", - " sub_perc_t.columns=cols #reset the names \n", - "\n", - "\n", - " #these are just the counts\n", - " sub_t=sub.transpose() #needs to be in this form or it wont work\n", - " sub_t.columns = sub_t.loc[tax_level] #set the column names as the taxoomy levels \n", - " sub_t.drop([tax_level], inplace=True) #this just takes out the duplicate header\n", - " \n", - " return sub_t, sub_perc_t\n", - "\n", - "\n", - "#step 1\n", - "def df_by_merge(path,tax_level):\n", - " \"\"\"\n", - " Reads in multiple sample alignments from diamond in a given directory and merges them into\n", - " a single pandas.DataFrame. It returns a pandas dataframe for each of th\n", - " phylum that is ready to plug into the processing function. Also returns total counts\n", - " which is necessary to calculate the percentage of total that is being represented \n", - " \"\"\"\n", - " merged_phy = None\n", - " samples = []\n", - " total_counts=[]\n", - " for f in os.listdir(path):\n", - " f = os.path.join(path, f)\n", - "\n", - " #print(f)\n", - " #if not f.endswith(\"%s_summary.txt\" % tax_level): continue\n", - " sample = os.path.basename(f).partition(\"_aln_names.txt\")[0]\n", - " samples.append(sample) #making a list of the sample headers\n", - " header = [tax_level,\"Reads_%s\" % sample]\n", - " if merged_phy is None:\n", - " #print('yes')\n", - " #merged = pd.read_table(f, header=0, names=header, comment=\"-\")\n", - " phy,ordr,clas,tc=parse_align(f)\n", - " #print(phy)\n", - " total_counts.append(tc)\n", - " merged_phy = pd.DataFrame(data=phy,index=[0]).transpose()\n", - " merged_phy.reset_index(inplace=True)\n", - " merged_phy.columns=header\n", - " merged_clas = pd.DataFrame(data=clas, index=[0]).transpose()\n", - " merged_clas.reset_index(inplace=True)\n", - " merged_clas.columns=header\n", - " merged_ord = pd.DataFrame(data=ordr, index=[0]).transpose()\n", - " merged_ord.reset_index(inplace=True)\n", - " merged_ord.columns=header\n", - " #cols = merged.columns.tolist() #make a list of the column headers from the merged file\n", - " #cols.remove(tax_level) #remove the order tag\n", - " #cols.insert(0, tax_level) #insert it at the front.\n", - " #merged = merged[cols]\n", - " continue\n", - " phy,ordr,clas,tc=parse_align(f)\n", - " total_counts.append(tc)\n", - " df1 = pd.DataFrame(data=phy,index=[0]).transpose()\n", - " df1.reset_index(inplace=True)\n", - " df1.columns=header\n", - " df2 = pd.DataFrame(data=ordr,index=[0]).transpose()\n", - " df2.reset_index(inplace=True)\n", - " df2.columns=header\n", - " df3 = pd.DataFrame(data=clas,index=[0]).transpose()\n", - " df3.reset_index(inplace=True)\n", - " df3.columns=header\n", - " merged_phy_final = merged_phy.merge(df1, on=tax_level, how=\"outer\")\n", - " merged_ord_final = merged_ord.merge(df2, on=tax_level, how=\"outer\")\n", - " merged_class_final = merged_clas.merge(df3, on=tax_level, how=\"outer\")\n", - " #print(total_counts)\n", - "\n", - "\n", - " p_done,p_perc_done=process_reads(tax_level,merged_phy_final)\n", - " c_done,c_perc_done=process_reads(tax_level,merged_class_final)\n", - " o_done,o_perc_done=process_reads(tax_level,merged_order_final)\n", - " \n", - " return p_done,p_perc_done,c_done, c_perc_done,o_done,o_perc_done\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "def make_plots(p_done,c_done,o_done,y_axis_title):\n", - " ##code in this block works\n", - "\n", - " # data traces are taxonomies across samples\n", - " data = [go.Bar(x=p_done.index, y=p_done[tax], name=tax, visible=True) for tax in p_done.columns.tolist()] + \\\n", - " [go.Bar(x=c_done.index, y=c_done[tax], name=tax, visible=False) for tax in c_done.columns.tolist()] + \\\n", - " [go.Bar(x=o_done.index, y=o_done[tax], name=tax, visible=True) for tax in o_done.columns.tolist()]\n", - "\n", - "\n", - " # the number of taxa\n", - " trace_length = len(o_done.columns)\n", - "\n", - " # plot buttons\n", - " updatemenus = list([\n", - " dict(type=\"buttons\",\n", - " active=0,\n", - " buttons=list([ \n", - " dict(label = 'Phylum',\n", - " method = 'update',\n", - " args = [{'visible': [True]*trace_length + [False]*trace_length + [False]*trace_length},\n", - " {'yaxis': {\"title\": \"Percent\"}}\n", - " ]),\n", - " dict(label = 'Class',\n", - " method = 'update',\n", - " args = [{'visible': [False]*trace_length + [True]*trace_length + [False]*trace_length},\n", - " {'yaxis': {\"title\": \"Count\"}}\n", - " ]),\n", - " dict(label = 'Order',\n", - " method = 'update',\n", - " args = [{'visible': [False]*trace_length + [False]*trace_length + [True]*trace_length},\n", - " {'yaxis': {\"title\": \"Count\"}}\n", - " ])\n", - " ]),\n", - " direction = 'left',\n", - " pad = {'r': 0, 't': 0},\n", - " showactive = True,\n", - " x = 0,\n", - " xanchor = 'left',\n", - " y = 1.2,\n", - " yanchor = 'top' \n", - " )\n", - " ])\n", - "\n", - " # initial layout\n", - " layout = dict(title=\"Assignments per Sample By Count (Top 20)\",\n", - " updatemenus=updatemenus,\n", - " barmode=\"stack\",\n", - " margin={\"b\": 200},\n", - " xaxis={\"tickangle\": -60},\n", - " yaxis=y_axis_title,\n", - " showlegend=False,\n", - " hovermode='closest'\n", - " )\n", - "\n", - " fig = go.Figure(data=data, layout=layout)\n", - "\n", - " return fig\n", - "\n", - "def main(path_to_files):\n", - " p_done,p_perc_done,c_done,c_perc_done,o_done,o_perc_done=df_by_merge(path_to_files)\n", - " div = {} #this this appears to be what the actual html is going to call on(because it contains the plot info)\n", - " labels = {\n", - " \"percentage\": \"Percent of Total Reads Aligned\",\n", - " \"counts\": \"Count of Alignments\",\n", - " }\n", - " for variable in [\n", - " \"percentage\", \"counts\"\n", - " ]:\n", - " if variable == 'counts'\n", - " y_axis_label = labels[variable]\n", - " div[variable] = offline.plot(iplot(\n", - " make_plots(p_done,c_done,o_done,y_axis_label)\n", - " ),\n", - " **PLOTLY_PARAMS,\n", - " )\n", - " else:\n", - " y_axis_label = labels[variable]\n", - " div[variable] = offline.plot(iplot(\n", - " make_plots(p_perc_done,c_perc_done,o_perc_done,y_axis_label)\n", - " ),\n", - " **PLOTLY_PARAMS,\n", - " )\n", - " \n", - "\n", - " report_str = \"\"\"\n", - ".. raw:: html\n", - " \n", - "=============================================================\n", - "ATLAS_ - Assembly Summary\n", - "=============================================================\n", - ".. _ATLAS: https://github.com/pnnl/atlas\n", - ".. contents::\n", - " :backlinks: none\n", - "Summary\n", - "-------\n", - "N50 Counts by Taxonomy\n", - "***\n", - ".. raw:: html\n", - " {div[counts]}\n", - "Assembly Length Percentage by Taxonomy\n", - "***************\n", - ".. raw:: html\n", - " {div[percentage]}\n", - "\n", - "For more information see Table_1_\n", - "Downloads\n", - "---------\n", - "\"\"\"\n", - " report(report_str, report_out, Table_1=combined_stats, stylesheet=os.path.join(os.path.abspath(os.path.dirname(__file__)), \"report.css\"))\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " p = argparse.ArgumentParser()\n", - " p.add_argument(\"--path_to_alignment_files\", nargs=\"+\")\n", - " p.add_argument(\"--report-out\")\n", - "\n", - " args = p.parse_args()\n", - " main(\n", - " args.path_to_alignment_files,\n", - " args.report_out\n", - " )\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/lca.ipynb b/notebooks/lca.ipynb deleted file mode 100644 index 02e2b43..0000000 --- a/notebooks/lca.ipynb +++ /dev/null @@ -1,556 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "#convert the kegg id to a number\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Exiguobacterium sibiricum 255-15'" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "convert_tax('k117_1_1\tesi:Exig_1092\t53.4\t103\t41\t1\t1\t103\t127\t222\t8.3e-23\t111.7')" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "with open('real_tax_convert.txt','r') as tax_id:\n", - " converter={}\n", - " for line in tax_id:\n", - " \n", - " toks=line.strip().split('\\t')\n", - " test=toks[1]\n", - " #print(test)\n", - " try:\n", - " others=re.search(r'([a-z]+), .*; (.*)',test)\n", - " kegg_id=others.group(1)\n", - " NCBI_id=others.group(2)\n", - " converter[kegg_id]=NCBI_id\n", - " except:\n", - " print(test)\n", - " break\n", - " #kegg_id=others.group(0)\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "no\n" - ] - } - ], - "source": [ - "if 'Cryobacterium arcticum PAMC 27867' in blah:\n", - " print('yes')\n", - "else:\n", - " print('no')" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [], - "source": [ - "class Node(object):\n", - "\n", - " def __init__(self, taxonomy, node_id, parent_id, tax_level):\n", - " \"\"\"Represents a node within a tree.\n", - " Args:\n", - " taxonomy (str): taxonomy name or ID\n", - " node_id (str): taxonomy ID\n", - " parent_id (str): taxonomy ID of parent\n", - " tax_level (str): the taxonomic level for this node_id\n", - " \"\"\"\n", - " # the current node's string ID\n", - " self.taxonomy = taxonomy\n", - " # the current node's digit ID\n", - " self.node_id = node_id\n", - " self.parent_id = parent_id\n", - " self.tax_level = tax_level\n", - "\n", - "class Tree(object):\n", - "\n", - " def __init__(self, tree_file):\n", - " \"\"\"Builds reference dictionary of Taxonomy Name, Taxonomy ID, and Parent Taxonomy ID.\"\"\"\n", - " self.tree = defaultdict(dict)\n", - " \n", - " with open(tree_file) as tree_fh:\n", - " for line in tree_fh:\n", - " toks = line.strip().split(\"\\t\")\n", - " if not toks[0] == '1' and not toks[2] == '1':\n", - " assert not toks[0] == toks[2]\n", - " if not len(toks) == 4:\n", - " logging.warning(\"Line [%s] does not have ID, NAME, PARENTID, TAX LEVEL\" % line.strip())\n", - " continue\n", - " self.add_node(toks[1], toks[0], toks[2], toks[3])\n", - " \n", - " \n", - "\n", - " def add_node(self, taxonomy, node_id, parent_id, tax_level):\n", - " \"\"\"Adds node to tree dictionary.\n", - " Args:\n", - " taxonomy (str): the taxonomy name\n", - " node_id (str): the taxonomy id\n", - " parent_id (str): the parent's taxonomy id\n", - " tax_level (str): the taxonomic level for this node_id\n", - " \"\"\"\n", - " \n", - " # taxonomy id to node mapping; ensures unique nodes despite non-unique names\n", - " self.tree[node_id] = Node(taxonomy, node_id, parent_id, tax_level)\n", - " \n", - "\n", - " def lca(self, taxonomies, threshold=1.):\n", - " \"\"\"Returns the taxonomy of the LCA and optionally only use the top fraction of hits.\n", - " Args:\n", - " taxonomies (list): list of taxonomies (ID); when using threshold < 1 they\n", - " should be ordered by decreasing bitscore\n", - " threshold (Optional[float]): 0-1; threshold fraction of hits to be factored into lca\n", - " Returns:\n", - " str: node id of LCA\n", - " \"\"\"\n", - " if threshold > 1:\n", - " threshold = 1\n", - " elif threshold < 0.01:\n", - " # 10% as the minimum\n", - " threshold = 0.1\n", - "\n", - " count_target = len(taxonomies) * threshold\n", - " count_taxonomies = defaultdict(int)\n", - "\n", - " for taxonomy in taxonomies:\n", - " #print('tax',taxonomy) \n", - " \n", - " try:\n", - " current_taxonomy = self.tree[taxonomy].node_id\n", - " #print(current_taxonomy)\n", - " except AttributeError:\n", - " # dict when key not present\n", - " # taxonomy represented in the reference database, but is not present in the tree\n", - " continue\n", - " \n", - " while not current_taxonomy == \"1\":\n", - " #print('curtx',current_taxonomy)\n", - " count_taxonomies[current_taxonomy] += 1\n", - " if count_taxonomies[current_taxonomy] >= count_target:\n", - " #print('target found')\n", - " return self.tree[current_taxonomy].node_id\n", - " \n", - " # traverse up tree\n", - " current_taxonomy = self.tree[current_taxonomy].parent_id\n", - " return \"1\"\n", - " \n", - " def taxonomic_lineage(self, taxonomy):\n", - " \"\"\"For a given taxonomy ID, return its lineage as a list of IDs.\n", - " Args:\n", - " taxonomy (str): taxonomy name or taxonomy ID\n", - " Returns:\n", - " list of lineage\n", - " Example:\n", - " >>> tree = Tree(\"ref/ncbi_taxonomy_tree.txt\")\n", - " >>> tree.taxonomic_lineage(\"135621\")\n", - " ['1', '131567', '99999999', '2', '1224', '1236', '72274', '135621']\n", - " \"\"\"\n", - " # a large portion of ORFs\n", - " if taxonomy == \"1\":\n", - " return [taxonomy]\n", - "\n", - " lineage = [taxonomy]\n", - " while not taxonomy == \"1\":\n", - " taxonomy = self.tree[taxonomy].parent_id\n", - " # prepend\n", - " lineage.insert(0, taxonomy)\n", - " return lineage\n", - " \n", - " def lca_majority(self, taxonomy_list, majority_cutoff):\n", - " \"\"\"Finds a consensus majority up a tree structure.\n", - " Args:\n", - " taxonomy_list (list): list of taxonomy names or IDs\n", - " majority_cutoff (float): this is the total length of the taxonomy list * majority fraction\n", - " Returns:\n", - " list: string representing majority taxonomy ID, lineage counts dictionary with a key\n", - " of taxonomy ID and value of list populated with ordered lineage taxonomy IDs\n", - " Example:\n", - " >>> taxonomy_list = ['gamma subgroup', 'RNA similarity group I',\n", - " 'purple photosynthetic bacteria and relatives',\n", - " 'not Bacteria Haeckel 1894',\n", - " 'purple photosynthetic bacteria and relatives', 'gamma subgroup',\n", - " 'gamma subgroup', 'purple photosynthetic bacteria and relatives',\n", - " 'purple photosynthetic bacteria and relatives']\n", - " >>> majority_cutoff = len(taxonomy_list) * .5\n", - " >>> tree = Tree(\"ref/ncbi_taxonomy_tree.txt\")\n", - " >>> tree.lca_majority(taxonomy_list)\n", - " ('1224', {'1224': ['1', '131567', '99999999', '2', '1224'],\n", - " '1236': ['1', '131567', '99999999', '2', '1224', '1236'],\n", - " '2': ['1', '131567', '99999999', '2'],\n", - " '286': ['1', '131567', '99999999', '2', '1224', '1236', '72274', '135621', '286']})\n", - " \"\"\"\n", - " lineage_counts = Counter()\n", - " lineages = {}\n", - " for taxonomy in taxonomy_list:\n", - " lineage = self.taxonomic_lineage(taxonomy)\n", - " lineage_counts.update(lineage)\n", - " lineages[taxonomy] = lineage\n", - " lineage_indexes = index_of_list_items(lineages.values())\n", - " for taxonomy in lineage_indexes:\n", - " if lineage_counts[taxonomy] > majority_cutoff:\n", - " return taxonomy, lineages\n", - " return \"1\", lineages\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1783272\n" - ] - }, - { - "data": { - "text/plain": [ - "['1', '131567', '2', '1783272']" - ] - }, - "execution_count": 174, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test=Tree('refseq.tree')\n", - "lists=['6','10']\n", - "testing=test.lca(taxonomies)\n", - "print(testing)\n", - "test.taxonomic_lineage(testing)" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import Counter, defaultdict, deque, OrderedDict\n", - "import re\n", - "\n", - "\n", - "#this converts the kegg ID into the NCBI ID \n", - "def convert_tax(aligned):\n", - " \n", - " with open('real_tax_convert.txt','r') as tax_id:\n", - " converter={}\n", - " for line in tax_id:\n", - "\n", - " toks=line.strip().split('\\t')\n", - " test=toks[1]\n", - " #print(test)\n", - " others=re.search(r'([a-z]+), .*; (.*)',test)\n", - " kegg_id=others.group(1)\n", - " NCBI_id=others.group(2)\n", - " converter[kegg_id]=NCBI_id\n", - " \n", - " toks=aligned.strip().split('\\t')\n", - " kegg_id=toks[1].split(':')[0]\n", - " try:\n", - "\n", - " if kegg_id in converter:\n", - " return(converter[kegg_id])\n", - " except KeyError:\n", - " pass\n", - " \n", - " \n", - "\n", - "\n", - "\n", - "#below will make the taxonomy name to the node id so that lca can run. somehow need to integrate into the Tree class(if possible)\n", - "tax_to_node_id={}\n", - "with open('refseq.tree') as tree_fh:\n", - " for line in tree_fh:\n", - " toks = line.strip().split(\"\\t\")\n", - " if not toks[0] == '1' and not toks[2] == '1':\n", - " assert not toks[0] == toks[2]\n", - " if not len(toks) == 4:\n", - " logging.warning(\"Line [%s] does not have ID, NAME, PARENTID, TAX LEVEL\" % line.strip())\n", - " continue\n", - " tax_to_node_id[toks[1]]=toks[0]\n", - " \n", - "\n", - "#this function will generate the taxonomy that we want from the alignment file\n", - "def make_tax_list(alignment_file):\n", - "\n", - " with open(alignment_file,'r') as alignments:\n", - " taxonomies=[]\n", - " for line in alignments:\n", - " converted_line=convert_tax(line)\n", - " try:\n", - " node_id=tax_to_node_id[converted_line]\n", - " except KeyError:\n", - " continue\n", - " taxonomies.append(node_id)\n", - " return taxonomies\n", - "\n", - "\n", - "\n", - "\n", - " \n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "k117_1_1\n", - "['1', '131567', '2', '1783272']\n", - "k117_2_1\n", - "['1', '131567', '2', '1224', '28211', '356', '69277', '28100', '1867719']\n", - "k117_3_1\n", - "['1', '131567', '2', '1224', '28211', '356', '45401', '29407', '674703']\n" - ] - } - ], - "source": [ - "from itertools import groupby\n", - "with open('head_20.txt','r') as alignments:\n", - " for aligned, kegg_id in groupby(alignments, key=lambda x: x.split(\"\\t\")[0]):\n", - " print(aligned)\n", - " taxonomies=[]\n", - " for item in kegg_id:\n", - " converted_line=convert_tax(item)\n", - " try:\n", - " node_id=tax_to_node_id[converted_line]\n", - " except KeyError:\n", - " continue\n", - " taxonomies.append(node_id)\n", - " lca=test.lca(taxonomies)\n", - " lineages=test.taxonomic_lineage(lca)\n", - " print(lineages)\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1783272'" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test.lca(taxonomies)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "class DiamondHits(object):\n", - "\n", - " def __init__(self, names=None, max_hits=10, top_fraction=None):\n", - " \"\"\"Class that represents BLAST hits for a single target sequence. Hits are added to queues\n", - " for bitscore and ID and ordered by increasing bitscore.\n", - " Args:\n", - " names (Optional[list]): when initiated with a name list; :func:`best_hit` and\n", - " :func:`add` will no longer operate as intended\n", - " max_hits (int): maximum number of hits to consider for this :class:`BlastHits` group\n", - " top_fraction (float): fraction cutoff from best bitscore, e.g. 0.7 will filter out 699 when best bitscore is 1000\n", - " Notes:\n", - " max_hits and top_fraction work in conjunction of one another\n", - " Examples:\n", - " >>> hits = BlastHits()\n", - " >>> hits.add(\"name_1\", 100)\n", - " >>> hits.add(\"name_2\", 110)\n", - " >>> hits.add(\"name_3\", 120)\n", - " >>> hits.add(\"name_4\", 130)\n", - " >>> hits.add(\"name_4\", 140)\n", - " >>> hits.majority()\n", - " 'name_4'\n", - " >>> hits.best_hit()\n", - " 'name_4'\n", - " >>> hits.add(\"name_5\", 150)\n", - " >>> hits.best_hit()\n", - " 'name_5'\n", - " # demo max hits and top fraction\n", - " >>> hits = BlastHits(max_hits=3, top_fraction=0.7)\n", - " >>> hits.add(\"name_1\", 100)\n", - " # filtered hit\n", - " >>> hits.add(\"name_2\", 65)\n", - " >>> hits.names\n", - " deque(['name_1'])\n", - " >>> hits.add(\"name_3\", 70)\n", - " deque(['name_3', 'name_1'])\n", - " >>> hits.add(\"name_4\", 80)\n", - " >>> hits.add(\"name_5\", 85)\n", - " >>> hits.names\n", - " deque(['name_4', 'name_5', 'name_1'])\n", - " # new best bitscore\n", - " >>> hits.add(\"name_6\", 125)\n", - " >>> dict(zip(hits.names, hits.bitscores))\n", - " {'name_1': 100.0, 'name_6': 125.0}\n", - " \"\"\"\n", - " if names is None:\n", - " # increasing bitscore sorted\n", - " self.names = deque()\n", - " self.bitscores = deque()\n", - " else:\n", - " self.names = names\n", - " self.max_hits = max_hits\n", - " self.top_fraction = top_fraction\n", - "\n", - " def __repr__(self):\n", - " return \"{cls}[{tax}]\".format(cls=self.__class__.__name__, tax=self.names)\n", - "\n", - " def __len__(self):\n", - " return len(self.names)\n", - "\n", - " def add(self, name, bitscore):\n", - " \"\"\"Add entry to this :class:`BlastHits` group.\n", - " Args:\n", - " name (str): hit identifier\n", - " bitscore (str): bitscore for hit\n", - " \"\"\"\n", - " bitscore = float(bitscore)\n", - "\n", - " if self.top_fraction and self.bitscores:\n", - " # the filter\n", - " if bitscore < (self.bitscores[-1] * self.top_fraction):\n", - " bitscore = None\n", - " # new best\n", - " elif bitscore > self.bitscores[-1]:\n", - " score = self.bitscores[0]\n", - " while score < bitscore * self.top_fraction:\n", - " self.names.popleft()\n", - " self.bitscores.popleft()\n", - " score = self.bitscores[0]\n", - "\n", - " if bitscore:\n", - " # insert into sorted list\n", - " idx = bisect.bisect_left(self.bitscores, bitscore)\n", - " self.bitscores.insert(idx, bitscore)\n", - " self.names.insert(idx, name)\n", - " if len(self.names) > self.max_hits:\n", - " # remove lowest bitscore\n", - " self.names.popleft()\n", - " self.bitscores.popleft()\n", - "\n", - " def best_hit(self):\n", - " \"\"\"Returns the hit ID of the best scoring alignment.\"\"\"\n", - " return self.names[-1]\n", - "\n", - " def majority(self):\n", - " \"\"\"Returns the hit ID of the best scoring hit ID that is repeated or the best hit when\n", - " no items are repeated.\n", - " \"\"\"\n", - " # no repeated names\n", - " if len(self.names) == len(set(self.names)):\n", - " return self.best_hit()\n", - " else:\n", - " # count each taxonomy, grab top taxonomy\n", - " most_common = Counter(self.names).most_common(1)[0][0]\n", - " # need to flip to grab best bitscore\n", - " names_reversed = self.names.copy()\n", - " names_reversed.reverse()\n", - " # left most index match\n", - " idx = names_reversed.index(most_common)\n", - " return names_reversed[idx]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/perseq_graphs.ipynb b/notebooks/perseq_graphs.ipynb deleted file mode 100644 index 199da5d..0000000 --- a/notebooks/perseq_graphs.ipynb +++ /dev/null @@ -1,412 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os \n", - "import plotly\n", - "import plotly.plotly as py\n", - "import plotly.figure_factory as ff\n", - "import plotly.graph_objs as go\n", - "\n", - "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", - "\n", - "init_notebook_mode(connected=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#dependent functions for the others\n", - "#function to parse kaiju output\n", - "\n", - "#parse the kaiju output\n", - "def parse_align(alignment_file):\n", - " total_lines=0\n", - " order_dict={}\n", - " class_dict={}\n", - " phylum_dict={}\n", - " total_align=[]\n", - " #for alignment_file in file_list:\n", - " with open(alignment_file) as file:\n", - " classified=0\n", - " unc=0\n", - " for line in file:\n", - "\n", - "\n", - " total_lines+=1\n", - " toks=line.strip().split('\\t')\n", - " #print(toks)\n", - " classification=toks[0]\n", - "\n", - " if classification =='C':\n", - " classified+=1\n", - " bits=toks[7].split(';')\n", - " order=bits[3].strip()\n", - " clas=bits[2]\n", - " phylum=bits[1]\n", - " if order in order_dict:\n", - " order_dict[order]+=1\n", - " else:\n", - " order_dict[order]=1\n", - " if clas in class_dict:\n", - " class_dict[clas]+=1\n", - " else:\n", - " class_dict[clas]=1\n", - " if phylum in phylum_dict:\n", - " phylum_dict[phylum]+=1\n", - " else:\n", - " phylum_dict[phylum]=1\n", - " else:\n", - " unc+=1\n", - " total=classified+unc\n", - " # or should this be a list. prob list\n", - " #print('family',family)\n", - " return order_dict,class_dict,phylum_dict,total \n", - "\n", - "\n", - "\n", - "#will find the counts(pro)\n", - "\n", - "# def process_df(concat_df):\n", - "# '''This function will process the output so that it is in the proper form for plotting. this is total counts'''\n", - "# lol= concat_df.sort_values(by=[0,1],ascending=False)\n", - "# cols=['sampe 140','sample 164']\n", - "# lol.columns=cols\n", - "# top=lol.head(20) #grab top 20\n", - "\n", - "# #top.rename(index=str, columns={\"0\": \"140\", \"1\": \"164\"})\n", - "# t_top=top.transpose() #must transpose for this method to work\n", - " \n", - " \n", - "# return t_top\n", - "# def process_per(concat_df,d):\n", - "# '''This function will provide the percents of *total* read counts. Both aligned and unaligned'''\n", - "# full= concat_df.sort_values(by=[0,1],ascending=False)\n", - "# cols=['sampe 140','sample 164']\n", - "# full.columns=cols\n", - "# psorted=full.head(20) #grab top 20\n", - "# #sums=psorted.sum().tolist() #make list of the sums for each column\n", - "# perc=psorted.div(d) #divide by the sums to get the percents\n", - "# t_perc=perc.transpose()\n", - "# return t_perc\n", - "\n", - "#calculates the order for diversity \n", - "def gini(x, corr=False):\n", - " \"\"\"Calculates Gini coefficient, the measure of inequality among values\n", - " of a frequency distribution. A Gini coefficient of zero expresses\n", - " perfect equality.\n", - "\n", - " Port from ineq package in R::\n", - "\n", - " > library(ineq)\n", - " > t <- c(1,2,6,7,8)\n", - " > Gini(t)\n", - " [1] 0.3166667\n", - " > Gini(t, corr=TRUE)\n", - " [1] 0.3958333\n", - "\n", - " Args:\n", - " x (list): list or array of numbers\n", - " corr (Optional[bool]): finite sample correction\n", - "\n", - " Returns:\n", - " float\n", - "\n", - " >>> import numpy as np\n", - " >>> t = [1,2,6,7,8]\n", - " >>> gini(t) # doctest: +ELLIPSIS\n", - " 0.3166...\n", - " >>> gini(t, corr=True) # doctest: +ELLIPSIS\n", - " 0.3958...\n", - " >>> gini([]) # doctest: +ELLIPSIS\n", - " Traceback (most recent call last):\n", - " ...\n", - " AssertionError: x is empty\n", - " >>> t = [1,2,6,7,\"A\"]\n", - " >>> gini(t) # doctest: +ELLIPSIS\n", - " Traceback (most recent call last):\n", - " ...\n", - " ValueError: could not convert...\n", - " \"\"\"\n", - " x = np.array(x, dtype=float)\n", - " # filter out nan values as list is coming from merged dataframe\n", - " x = x[~np.isnan(x)]\n", - " n = len(x)\n", - " assert n > 0, \"x is empty\"\n", - " x.sort(kind=\"mergesort\")\n", - " G = sum(np.arange(1, n + 1) * x)\n", - " G = 2 * G / sum(x) - (n + 1)\n", - " if corr:\n", - " return G / (n - 1)\n", - " else:\n", - " return G / n\n", - "#step 1\n", - "def df_by_merge(path,tax_level):\n", - " \"\"\"\n", - " Reads in multiple sample alignments from diamond in a given directory and merges them into\n", - " a single pandas.DataFrame. It returns a pandas dataframe for each of th\n", - " phylum that is ready to plug into the processing function. Also returns total counts\n", - " which is necessary to calculate the percentage of total that is being represented \n", - " \"\"\"\n", - " merged_phy = None\n", - " samples = []\n", - " total_counts=[]\n", - " for f in os.listdir(path):\n", - " f = os.path.join(path, f)\n", - "\n", - " #print(f)\n", - " #if not f.endswith(\"%s_summary.txt\" % tax_level): continue\n", - " sample = os.path.basename(f).partition(\"_aln_names.txt\")[0]\n", - " samples.append(sample) #making a list of the sample headers\n", - " header = [tax_level,\"Reads_%s\" % sample]\n", - " if merged_phy is None:\n", - " #print('yes')\n", - " #merged = pd.read_table(f, header=0, names=header, comment=\"-\")\n", - " phy,ordr,clas,tc=parse_align(f)\n", - " #print(phy)\n", - " total_counts.append(tc)\n", - " merged_phy = pd.DataFrame(data=phy,index=[0]).transpose()\n", - " merged_phy.reset_index(inplace=True)\n", - " merged_phy.columns=header\n", - " merged_clas = pd.DataFrame(data=clas, index=[0]).transpose()\n", - " merged_clas.reset_index(inplace=True)\n", - " merged_clas.columns=header\n", - " merged_ord = pd.DataFrame(data=ordr, index=[0]).transpose()\n", - " merged_ord.reset_index(inplace=True)\n", - " merged_ord.columns=header\n", - " #cols = merged.columns.tolist() #make a list of the column headers from the merged file\n", - " #cols.remove(tax_level) #remove the order tag\n", - " #cols.insert(0, tax_level) #insert it at the front.\n", - " #merged = merged[cols]\n", - " continue\n", - " phy,ordr,clas,tc=parse_align(f)\n", - " total_counts.append(tc)\n", - " df1 = pd.DataFrame(data=phy,index=[0]).transpose()\n", - " df1.reset_index(inplace=True)\n", - " df1.columns=header\n", - " df2 = pd.DataFrame(data=ordr,index=[0]).transpose()\n", - " df2.reset_index(inplace=True)\n", - " df2.columns=header\n", - " df3 = pd.DataFrame(data=clas,index=[0]).transpose()\n", - " df3.reset_index(inplace=True)\n", - " df3.columns=header\n", - " merged_phy_final = merged_phy.merge(df1, on=tax_level, how=\"outer\")\n", - " merged_ord_final = merged_ord.merge(df2, on=tax_level, how=\"outer\")\n", - " merged_class_final = merged_clas.merge(df3, on=tax_level, how=\"outer\")\n", - " print(total_counts)\n", - "\n", - "\n", - " \n", - " return merged_phy_final, merged_ord_final, merged_class_final, total_counts\n", - "\n", - "\n", - "#step 2\n", - "#so this will make the counts information that we need. but this should be put into a function because it needs to be done\n", - "#for all three of the taxa\n", - "import numpy as np\n", - "def process_reads(tax_level, pandas_df):\n", - " '''This function will take the merged reads from the file and put them into a graphable\n", - " form for both counts and percents. It will return both. '''\n", - " #cols = [i for i in m.columns.tolist() if i.startswith(\"Percent_\")]\n", - " header=merged_phy_final.columns.tolist()\n", - " header.pop(0)\n", - " full= merged_phy_final.sort_values(by=header,ascending=False) #organize these by count\n", - "\n", - " sample_order = []\n", - " for read_col in merged_phy_final.columns.tolist(): #this will make a list of the gini coefficient of where these should be located by diversity of the sample\n", - " if read_col.startswith(\"Reads_\"):\n", - " sample_order.append([read_col.strip(\"Reads_\"), gini(merged_phy_final[read_col])])\n", - " sample_order = sorted(sample_order, key=lambda x: x[1])\n", - "\n", - " sub = full[[tax_level] + [\"Reads_%s\" % sample_order[i][0] for i in range(len(sample_order))]]\n", - " sub = sub.head(100)\n", - " cols=sub[tax_level].tolist()\n", - "\n", - " #find the percent\n", - " sub_perc=sub[header].div(total_counts)\n", - " sub_perc_t=sub_perc.transpose()\n", - " sub_perc_t.columns=cols #reset the names \n", - "\n", - "\n", - " #these are just the counts\n", - " sub_t=sub.transpose() #needs to be in this form or it wont work\n", - " sub_t.columns = sub_t.loc[tax_level] #set the column names as the taxoomy levels \n", - " sub_t.drop([tax_level], inplace=True) #this just takes out the duplicate header\n", - "\n", - " return sub_t, sub_perc_t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#step 1 \n", - "test_p,test_c,test_o,total_counts= df_by_merge(path,tax_level)\n", - "#step 2\n", - "p_done,p_perc_done=process_reads(tax_level,test_p)\n", - "c_done,c_perc_done=process_reads(tax_level,test_c)\n", - "o_done,o_perc_done=process_reads(tax_level,test_o)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##this will make counts graphs\n", - "\n", - "# data traces are taxonomies across samples\n", - "data = [go.Bar(x=p_done.index, y=p_done[tax], name=tax, visible=True) for tax in p_done.columns.tolist()] + \\\n", - "[go.Bar(x=c_done.index, y=c_done[tax], name=tax, visible=False) for tax in c_done.columns.tolist()] + \\\n", - "[go.Bar(x=o_done.index, y=o_done[tax], name=tax, visible=True) for tax in o_done.columns.tolist()]\n", - "\n", - "\n", - "# the number of taxa\n", - "trace_length = len(o_done.columns)\n", - "\n", - "# plot buttons\n", - "updatemenus = list([\n", - " dict(type=\"buttons\",\n", - " active=0,\n", - " buttons=list([ \n", - " dict(label = 'Phylum',\n", - " method = 'update',\n", - " args = [{'visible': [True]*trace_length + [False]*trace_length + [False]*trace_length},\n", - " {'yaxis': {\"title\": \"Percent\"}}\n", - " ]),\n", - " dict(label = 'Order',\n", - " method = 'update',\n", - " args = [{'visible': [False]*trace_length + [True]*trace_length + [False]*trace_length},\n", - " {'yaxis': {\"title\": \"Count\"}}\n", - " ]),\n", - " dict(label = 'Class',\n", - " method = 'update',\n", - " args = [{'visible': [False]*trace_length + [False]*trace_length + [True]*trace_length},\n", - " {'yaxis': {\"title\": \"Count\"}}\n", - " ])\n", - " ]),\n", - " direction = 'left',\n", - " pad = {'r': 0, 't': 0},\n", - " showactive = True,\n", - " x = 0,\n", - " xanchor = 'left',\n", - " y = 1.2,\n", - " yanchor = 'top' \n", - " )\n", - "])\n", - "\n", - "# initial layout\n", - "layout = dict(title=\"Assignments per Sample By Count (Top 20)\",\n", - " updatemenus=updatemenus,\n", - " barmode=\"stack\",\n", - " margin={\"b\": 200},\n", - " xaxis={\"tickangle\": -60},\n", - " yaxis={\"title\": \"Percent\"},\n", - " showlegend=False,\n", - " hovermode='closest'\n", - " )\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "\n", - "iplot(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##this will make percents graph\n", - "\n", - "# data traces are taxonomies across samples\n", - "data = [go.Bar(x=p_perc_done.index, y=p_perc_done[tax], name=tax, visible=True) for tax in p_perc_done.columns.tolist()] + \\\n", - "[go.Bar(x=c_perc_done.index, y=c_perc_done[tax], name=tax, visible=False) for tax in c_perc_done.columns.tolist()] + \\\n", - "[go.Bar(x=o_perc_done.index, y=o_perc_done[tax], name=tax, visible=True) for tax in o_perc_done.columns.tolist()]\n", - "\n", - "\n", - "# the number of taxa\n", - "trace_length = len(o_perc_done.columns)\n", - "\n", - "# plot buttons\n", - "updatemenus = list([\n", - " dict(type=\"buttons\",\n", - " active=0,\n", - " buttons=list([ \n", - " dict(label = 'Phylum',\n", - " method = 'update',\n", - " args = [{'visible': [True]*trace_length + [False]*trace_length + [False]*trace_length},\n", - " {'yaxis': {\"title\": \"Percent\"}}\n", - " ]),\n", - " dict(label = 'Order',\n", - " method = 'update',\n", - " args = [{'visible': [False]*trace_length + [True]*trace_length + [False]*trace_length},\n", - " {'yaxis': {\"title\": \"Count\"}}\n", - " ]),\n", - " dict(label = 'Class',\n", - " method = 'update',\n", - " args = [{'visible': [False]*trace_length + [False]*trace_length + [True]*trace_length},\n", - " {'yaxis': {\"title\": \"Count\"}}\n", - " ])\n", - " ]),\n", - " direction = 'left',\n", - " pad = {'r': 0, 't': 0},\n", - " showactive = True,\n", - " x = 0,\n", - " xanchor = 'left',\n", - " y = 1.2,\n", - " yanchor = 'top' \n", - " )\n", - "])\n", - "\n", - "# initial layout\n", - "layout = dict(title=\"Assignments per Sample By Percentage of Total (Top 20)\",\n", - " updatemenus=updatemenus,\n", - " barmode=\"stack\",\n", - " margin={\"b\": 200},\n", - " xaxis={\"tickangle\": -60},\n", - " yaxis={\"title\": \"Percent\"},\n", - " showlegend=False,\n", - " hovermode='closest'\n", - " )\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "\n", - "iplot(fig)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/plotly_button_prototyping.ipynb b/notebooks/plotly_button_prototyping.ipynb deleted file mode 100644 index beae9a2..0000000 --- a/notebooks/plotly_button_prototyping.ipynb +++ /dev/null @@ -1,56693 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/vnd.plotly.v1+html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/vnd.plotly.v1+html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import cufflinks as cf\n", - "import os\n", - "import pandas as pd\n", - "import plotly\n", - "import plotly.plotly as py\n", - "import plotly.figure_factory as ff\n", - "import plotly.graph_objs as go\n", - "import numpy as np\n", - "# from plotly.graph_objs import Layout, Table, Figure\n", - "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", - "\n", - "plotly.offline.init_notebook_mode(connected=True)\n", - "cf.set_config_file(offline=True)\n", - "cf.set_config_file(theme='white')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# kaiju_summary = \"sample1_summary.txt\"\n", - "# sample = os.path.basename(kaiju_summary).partition(\"_summary.txt\")[0]\n", - "# header = [\"Percent_%s\" % sample, \"Reads_%s\" % sample, \"Order\"]\n", - "# df = pd.read_table(kaiju_summary, header=0, names=header, comment=\"-\")\n", - "# cols = df.columns.tolist()\n", - "# cols.remove(\"Order\")\n", - "# cols.insert(0, \"Order\")\n", - "# df = df[cols]\n", - "# df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# kaiju_summary = \"sample2_summary.txt\"\n", - "# sample = os.path.basename(kaiju_summary).partition(\"_summary.txt\")[0]\n", - "# header = [\"Percent_%s\" % sample, \"Reads_%s\" % sample, \"Order\"]\n", - "# df2 = pd.read_table(kaiju_summary, header=0, names=header, comment=\"-\")\n", - "# df2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def df_by_merge(path, tax_level):\n", - " \"\"\"\n", - " Reads in multiple TSVs in a given directory and merges them into\n", - " a single pandas.DataFrame.\n", - " \"\"\"\n", - " merged = None\n", - " samples = []\n", - " for f in os.listdir(path):\n", - " f = os.path.join(path, f)\n", - " if not f.endswith(\"%s_summary.txt\" % tax_level): continue\n", - " sample = os.path.basename(f).partition(\"_%s_summary.txt\" % tax_level)[0]\n", - " samples.append(sample)\n", - " header = [\"Percent_%s\" % sample, \"Reads_%s\" % sample, tax_level]\n", - " if merged is None:\n", - " merged = pd.read_table(f, header=0, names=header, comment=\"-\")\n", - " cols = merged.columns.tolist()\n", - " cols.remove(tax_level)\n", - " cols.insert(0, tax_level)\n", - " merged = merged[cols]\n", - " continue\n", - " df = pd.read_table(f, header=0, names=header, comment=\"-\")\n", - " merged = merged.merge(df, on=tax_level, how=\"outer\")\n", - " return samples, merged" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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orderReads_WL8_S29_001Reads_WL39_S28_001Reads_WL84_S36_001Reads_WL187_S5_001Reads_WL121_S37_001Reads_WL142_S30_001Reads_WL57_S40_001Reads_WL106_S10_001Reads_WL134_S34_001...Reads_WL110_S14_001Reads_WL148_S24_001Reads_WL40_S3_001Reads_WL124_S19_001Reads_WL2_S2_001Reads_WL112_S38_001Reads_WL51_S1_001Reads_WL130_S16_001Reads_WL93_S33_001Reads_WL69_S27_001
412cannot be assigned to a order835405.01587459.04222117.03676849.04660480.0339573.01530270.04347658.02765567.0...2860746.03282043.01078075.05869968.01896028.01864322.02420650.02466153.05129521.02208842.0
0Lactobacillales250226.0217735.02224612.01832091.02519273.049303.0715484.02591916.01895974.0...1484097.01676231.0642967.06863032.02902051.01434848.01923510.02740638.04947499.01651551.0
413unclassified577520.0936302.02621485.01613517.02016587.0250931.0722642.01234499.01294635.0...1111115.0935144.0194847.0846016.0228832.0184864.027763.0110961.038253.044011.0
1Enterobacterales109425.0114408.057848.034727.093402.0155885.0668164.043394.030591.0...32820.02567430.01092443.039799.0268536.02775047.02437271.02625969.04200385.04308198.0
2Bacillales77537.077306.057872.035012.069625.0102992.0440365.036849.030381.0...167688.01767571.0786078.056009.0171677.01748051.01304662.01516751.02452150.02610838.0
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5 rows × 41 columns

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" - ], - "text/plain": [ - " order Reads_WL8_S29_001 Reads_WL39_S28_001 \\\n", - "412 cannot be assigned to a order 835405.0 1587459.0 \n", - "0 Lactobacillales 250226.0 217735.0 \n", - "413 unclassified 577520.0 936302.0 \n", - "1 Enterobacterales 109425.0 114408.0 \n", - "2 Bacillales 77537.0 77306.0 \n", - "\n", - " Reads_WL84_S36_001 Reads_WL187_S5_001 Reads_WL121_S37_001 \\\n", - "412 4222117.0 3676849.0 4660480.0 \n", - "0 2224612.0 1832091.0 2519273.0 \n", - "413 2621485.0 1613517.0 2016587.0 \n", - "1 57848.0 34727.0 93402.0 \n", - "2 57872.0 35012.0 69625.0 \n", - "\n", - " Reads_WL142_S30_001 Reads_WL57_S40_001 Reads_WL106_S10_001 \\\n", - "412 339573.0 1530270.0 4347658.0 \n", - "0 49303.0 715484.0 2591916.0 \n", - "413 250931.0 722642.0 1234499.0 \n", - "1 155885.0 668164.0 43394.0 \n", - "2 102992.0 440365.0 36849.0 \n", - "\n", - " Reads_WL134_S34_001 ... Reads_WL110_S14_001 \\\n", - "412 2765567.0 ... 2860746.0 \n", - "0 1895974.0 ... 1484097.0 \n", - "413 1294635.0 ... 1111115.0 \n", - "1 30591.0 ... 32820.0 \n", - "2 30381.0 ... 167688.0 \n", - "\n", - " Reads_WL148_S24_001 Reads_WL40_S3_001 Reads_WL124_S19_001 \\\n", - "412 3282043.0 1078075.0 5869968.0 \n", - "0 1676231.0 642967.0 6863032.0 \n", - "413 935144.0 194847.0 846016.0 \n", - "1 2567430.0 1092443.0 39799.0 \n", - "2 1767571.0 786078.0 56009.0 \n", - "\n", - " Reads_WL2_S2_001 Reads_WL112_S38_001 Reads_WL51_S1_001 \\\n", - "412 1896028.0 1864322.0 2420650.0 \n", - "0 2902051.0 1434848.0 1923510.0 \n", - "413 228832.0 184864.0 27763.0 \n", - "1 268536.0 2775047.0 2437271.0 \n", - "2 171677.0 1748051.0 1304662.0 \n", - "\n", - " Reads_WL130_S16_001 Reads_WL93_S33_001 Reads_WL69_S27_001 \n", - "412 2466153.0 5129521.0 2208842.0 \n", - "0 2740638.0 4947499.0 1651551.0 \n", - "413 110961.0 38253.0 44011.0 \n", - "1 2625969.0 4200385.0 4308198.0 \n", - "2 1516751.0 2452150.0 2610838.0 \n", - "\n", - "[5 rows x 41 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tax_level = \"order\"\n", - "samples, m = df_by_merge(\"/Users/brow015/devel/qc/classifications/logs/bailey_qc/kaiju\", tax_level)\n", - "cols = [i for i in m.columns.tolist() if i.startswith(\"Percent_\")]\n", - "m[\"Total_Percent\"] = m[cols].sum(axis=1)\n", - "m.sort_values(\"Total_Percent\", ascending=False, inplace=True)\n", - "tmp = m[(~m[tax_level].str.contains(\"unclassified|cannot\"))]\n", - "sample_order = []\n", - "for read_col in tmp.columns.tolist():\n", - " if read_col.startswith(\"Reads_\"):\n", - " sample_order.append([read_col.strip(\"Reads_\"), gini(tmp[read_col])])\n", - "sample_order = sorted(sample_order, key=lambda x: x[1])\n", - "# m.head()\n", - "\n", - "# sub = m[[tax_level] + cols]\n", - "# sub = sub.head(100)\n", - "# sub.head(5)\n", - "\n", - "# cols = [i for i in m.columns.tolist() if i.startswith(\"Reads_\")]\n", - "sub = m[[tax_level] + [\"Reads_%s\" % sample_order[i][0] for i in range(len(sample_order))]]\n", - "sub = sub.head(100)\n", - "sub.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# trace = Table(\n", - "# header=dict(values=m.columns,\n", - "# fill=dict(color='#F8F8F8'),\n", - "# align=['left']),\n", - "# cells=dict(values=[m[i] for i in m.columns],\n", - "# align=['left']))\n", - "\n", - "# data = [sub.columns, [sub[i] for i in .columns]]\n", - "# data\n", - "# data = [trace]\n", - "# fig = ff.create_table(sub, height_constant=20)\n", - "# layout = Layout?\n", - "# fig = Figure(data, layout)\n", - "# iplot(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# t = sub.copy()\n", - "# t.set_index(tax_level, inplace=True)\n", - "# cols = t.columns.tolist()\n", - "# cols = [i.strip(\"Percent_\") for i in cols]\n", - "# t.columns = cols\n", - "# fig = t.iplot(title=\"Top 100 Assignments by Percent\", xTitle=\"Taxonomy\", yTitle=\"Percent\", asFigure=True)\n", - "# fig.layout.margin.update({'b':200})\n", - "# fig.layout.xaxis.update({\"tickangle\":-60, \"title\":\"Taxonomy\"})\n", - "# # print(fig)\n", - "# # t.iplot?\n", - "# iplot(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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orderLactobacillalesEnterobacteralesBacillalesBurkholderialesPseudomonadalesClostridialesRhizobialesNeisserialesCorynebacterialesSporadotrichidaEurotialesXanthomonadalesCoriobacterialesStreptomycetalesPeronosporalesMicrococcalesHaemosporidaEucoccidioridaAcidobacterialesSaccharomycetales
Reads_WL8_S29_00125022610942577537537621180219513176052302322093402115592108956792413474331174972221067121372322
Reads_WL39_S28_0012177351144087730655117180702034762515344316827956523546813243418638106650171631907514263240317888112136
Reads_WL84_S36_0012.22461e+06578485787229544917505577877183349195551658061739414073633364387961101618664661536480531071245226713396
Reads_WL187_S5_0011.83209e+06347273501220280446868646158393191083086630181941145743216828187074458303435842568537584505310518
Reads_WL121_S37_0012.51927e+06934026962521280136210217259916813919610121938163951293153601038638062682052539972828337524489710825
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" - ], - "text/plain": [ - "order Lactobacillales Enterobacterales Bacillales \\\n", - "Reads_WL8_S29_001 250226 109425 77537 \n", - "Reads_WL39_S28_001 217735 114408 77306 \n", - "Reads_WL84_S36_001 2.22461e+06 57848 57872 \n", - "Reads_WL187_S5_001 1.83209e+06 34727 35012 \n", - "Reads_WL121_S37_001 2.51927e+06 93402 69625 \n", - "\n", - "order Burkholderiales Pseudomonadales Clostridiales Rhizobiales \\\n", - "Reads_WL8_S29_001 53762 11802 19513 176052 \n", - "Reads_WL39_S28_001 55117 18070 20347 62515 \n", - "Reads_WL84_S36_001 295449 175055 77877 183349 \n", - "Reads_WL187_S5_001 202804 46868 64615 839319 \n", - "Reads_WL121_S37_001 212801 362102 172599 168139 \n", - "\n", - "order Neisseriales Corynebacteriales Sporadotrichida Eurotiales \\\n", - "Reads_WL8_S29_001 3023 22093 4021 15592 \n", - "Reads_WL39_S28_001 3443 168279 5652 35468 \n", - "Reads_WL84_S36_001 19555 165806 17394 140736 \n", - "Reads_WL187_S5_001 10830 86630 18194 114574 \n", - "Reads_WL121_S37_001 19610 121938 16395 129315 \n", - "\n", - "order Xanthomonadales Coriobacteriales Streptomycetales \\\n", - "Reads_WL8_S29_001 10895 679 24134 \n", - "Reads_WL39_S28_001 13243 418638 106650 \n", - "Reads_WL84_S36_001 33364 38796 110161 \n", - "Reads_WL187_S5_001 32168 2818 70744 \n", - "Reads_WL121_S37_001 36010 3863 80626 \n", - "\n", - "order Peronosporales Micrococcales Haemosporida Eucoccidiorida \\\n", - "Reads_WL8_S29_001 7433 11749 7222 1067 \n", - "Reads_WL39_S28_001 17163 19075 14263 2403 \n", - "Reads_WL84_S36_001 86646 61536 48053 107124 \n", - "Reads_WL187_S5_001 58303 43584 25685 3758 \n", - "Reads_WL121_S37_001 82052 53997 28283 3752 \n", - "\n", - "order Acidobacteriales Saccharomycetales \n", - "Reads_WL8_S29_001 12137 2322 \n", - "Reads_WL39_S28_001 17888 112136 \n", - "Reads_WL84_S36_001 52267 13396 \n", - "Reads_WL187_S5_001 45053 10518 \n", - "Reads_WL121_S37_001 44897 10825 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "counts = sub.copy()\n", - "# t = t[~t[\"Order\"] == \"unclassified\"]\n", - "counts = counts[(~counts[tax_level].str.contains(\"unclassified|cannot\"))]\n", - "# top 10 taxonomies\n", - "counts = counts.head(20)\n", - "cols = counts.columns.tolist()\n", - "# cols = [i.partition(\"Percent_\")[-1] for i in cols if \"Percent_\" in i]\n", - "# cols = [i.partition(\"Reads_\")[-1] for i in cols if \"Reads_\" in i]\n", - "counts.columns = [tax_level] + [\"Reads_%s\" % sample_order[i][0] for i in range(len(sample_order))]\n", - "counts = counts.T\n", - "counts.columns = counts.loc[tax_level]\n", - "# t.loc[\"Order\"]\n", - "# t.set_index(\"Order\")\n", - "counts.drop([tax_level], inplace=True)\n", - "# t[t[\"Order\"] != \"Order\"]\n", - "counts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "data": [ - { - "name": "Lactobacillales", - "type": "bar", - "x": [ - "WL8_S29_001", - "WL39_S28_001", - "WL84_S36_001", - 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"WL40_S3_001", - "WL124_S19_001", - "WL2_S2_001", - "WL112_S38_001", - "WL51_S1_001", - "WL130_S16_001", - "WL93_S33_001", - "WL69_S27_001" - ], - "y": [ - 4.337254, - 2.561963, - 0.475857, - 0.353068, - 0.773403, - 11.175193, - 13.107757999999999, - 0.406344, - 0.40747, - 3.32761, - 1.940316, - 0.43078500000000003, - 13.822785999999999, - 2.237689, - 0.42269300000000004, - 0.428486, - 0.5286109999999999, - 0.5319649999999999, - 0.268881, - 0.351444, - 0.372813, - 0.34087199999999995, - 5.716522, - 10.300478, - 2.4857, - 3.4099199999999996, - 0.792254, - 0.376853, - 2.065298, - 0.41115100000000004, - 0.29655, - 21.280839999999998, - 25.066761, - 0.245794, - 3.4726809999999997, - 31.579786, - 25.105785, - 24.72374, - 21.638634, - 37.41259 - ] - }, - { - "name": "Bacillales", - "type": "bar", - "x": [ - "WL8_S29_001", - "WL39_S28_001", - "WL84_S36_001", - "WL187_S5_001", - "WL121_S37_001", - "WL142_S30_001", - "WL57_S40_001", - "WL106_S10_001", - "WL134_S34_001", - "WL70_S39_001", - "WL147_S4_001", - "WL162_S15_001", - "WL182_S25_001", - "WL183_S26_001", - "WL1161and2_S12_001", - "WL1141_S35_001", - "WL33_S32_001", - "WL172_S22_001", - "WL55_S20_001", - "WL113_S9_001", - "WL64_S7_001", - "WL1382_S8_001", - "WL180_S31_001", - "WL68_S23_001", - "WL79_S13_001", - "WL3_S6_001", - "WL891_S18_001", - "WL1331and2_S21_001", - "WL115_S17_001", - "WL381and2_S11_001", - "WL110_S14_001", - "WL148_S24_001", - "WL40_S3_001", - "WL124_S19_001", - "WL2_S2_001", - "WL112_S38_001", - "WL51_S1_001", - "WL130_S16_001", - "WL93_S33_001", - "WL69_S27_001" - ], - "y": [ - 3.073316, - 1.7311299999999998, - 0.476054, - 0.35596500000000003, - 0.576521, - 7.383361999999999, - 8.638893, - 0.345056, - 0.404673, - 2.055846, - 1.391311, - 2.090114, - 9.217377, - 1.430388, - 2.0929990000000003, - 0.377633, - 0.392423, - 0.305742, - 0.501642, - 24.566545, - 0.364558, - 0.213734, - 3.470972, - 0.39729200000000003, - 1.228338, - 1.991785, - 1.362862, - 2.8225, - 2.489838, - 1.0675379999999999, - 1.515168, - 14.650991000000001, - 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"Reads_WL40_S3_001", - "Reads_WL124_S19_001", - "Reads_WL2_S2_001", - "Reads_WL112_S38_001", - "Reads_WL51_S1_001", - "Reads_WL130_S16_001", - "Reads_WL93_S33_001", - "Reads_WL69_S27_001" - ], - "y": [ - 7433, - 17163, - 86646, - 58303, - 82052, - 4207, - 32033, - 50348, - 29826, - 40564, - 40529, - 13345, - 5281, - 30858, - 167754, - 71997, - 93472, - 72388, - 49066, - 42092, - 31872, - 31166, - 3113, - 66098, - 166774, - 39284, - 236883, - 116431, - 79585, - 107936, - 153506, - 25111, - 7600, - 84472, - 9952, - 2491, - 1002, - 17735, - 1769, - 1264 - ] - }, - { - "name": "Micrococcales", - "type": "bar", - "x": [ - "Reads_WL8_S29_001", - "Reads_WL39_S28_001", - "Reads_WL84_S36_001", - "Reads_WL187_S5_001", - "Reads_WL121_S37_001", - "Reads_WL142_S30_001", - "Reads_WL57_S40_001", - "Reads_WL106_S10_001", - "Reads_WL134_S34_001", - "Reads_WL70_S39_001", - "Reads_WL147_S4_001", - "Reads_WL162_S15_001", - "Reads_WL182_S25_001", - "Reads_WL183_S26_001", - "Reads_WL1161and2_S12_001", - "Reads_WL1141_S35_001", - "Reads_WL33_S32_001", - "Reads_WL172_S22_001", - "Reads_WL55_S20_001", - "Reads_WL113_S9_001", - "Reads_WL64_S7_001", - "Reads_WL1382_S8_001", - "Reads_WL180_S31_001", - "Reads_WL68_S23_001", - "Reads_WL79_S13_001", - "Reads_WL3_S6_001", - "Reads_WL891_S18_001", - "Reads_WL1331and2_S21_001", - "Reads_WL115_S17_001", - "Reads_WL381and2_S11_001", - "Reads_WL110_S14_001", - "Reads_WL148_S24_001", - "Reads_WL40_S3_001", - "Reads_WL124_S19_001", - "Reads_WL2_S2_001", - "Reads_WL112_S38_001", - "Reads_WL51_S1_001", - "Reads_WL130_S16_001", - "Reads_WL93_S33_001", - "Reads_WL69_S27_001" - ], - "y": [ - 11749, - 19075, - 61536, - 43584, - 53997, - 4808, - 18259, - 29656, - 24647, - 46120, - 25521, - 5339, - 5199, - 28154, - 46163, - 45508, - 47139, - 188332, - 35585, - 25434, - 24060, - 23778, - 5469, - 35860, - 66419, - 26456, - 43069, - 67148, - 44754, - 52308, - 64976, - 20350, - 5639, - 21836, - 10171, - 6032, - 1386, - 14812, - 3057, - 1534 - ] - }, - { - "name": "Haemosporida", - "type": "bar", - "x": [ - "Reads_WL8_S29_001", - "Reads_WL39_S28_001", - "Reads_WL84_S36_001", - "Reads_WL187_S5_001", - "Reads_WL121_S37_001", - "Reads_WL142_S30_001", - "Reads_WL57_S40_001", - "Reads_WL106_S10_001", - "Reads_WL134_S34_001", - "Reads_WL70_S39_001", - "Reads_WL147_S4_001", - "Reads_WL162_S15_001", - "Reads_WL182_S25_001", - "Reads_WL183_S26_001", - "Reads_WL1161and2_S12_001", - "Reads_WL1141_S35_001", - "Reads_WL33_S32_001", - "Reads_WL172_S22_001", - "Reads_WL55_S20_001", - "Reads_WL113_S9_001", - "Reads_WL64_S7_001", - "Reads_WL1382_S8_001", - "Reads_WL180_S31_001", - "Reads_WL68_S23_001", - "Reads_WL79_S13_001", - "Reads_WL3_S6_001", - "Reads_WL891_S18_001", - "Reads_WL1331and2_S21_001", - "Reads_WL115_S17_001", - "Reads_WL381and2_S11_001", - "Reads_WL110_S14_001", - "Reads_WL148_S24_001", - "Reads_WL40_S3_001", - "Reads_WL124_S19_001", - "Reads_WL2_S2_001", - "Reads_WL112_S38_001", - "Reads_WL51_S1_001", - "Reads_WL130_S16_001", - "Reads_WL93_S33_001", - "Reads_WL69_S27_001" - ], - "y": [ - 7222, - 14263, - 48053, - 25685, - 28283, - 2999, - 10757, - 25245, - 42260, - 26448, - 19164, - 7718, - 3544, - 16075, - 64887, - 86470, - 48585, - 33031, - 42014, - 21286, - 14683, - 17219, - 2742, - 53851, - 59633, - 24739, - 64027, - 65540, - 60572, - 61412, - 50706, - 19286, - 5896, - 34852, - 3546, - 1592, - 353, - 4335, - 658, - 679 - ] - }, - { - "name": "Eucoccidiorida", - "type": "bar", - "x": [ - "Reads_WL8_S29_001", - "Reads_WL39_S28_001", - "Reads_WL84_S36_001", - "Reads_WL187_S5_001", - "Reads_WL121_S37_001", - "Reads_WL142_S30_001", - "Reads_WL57_S40_001", - "Reads_WL106_S10_001", - "Reads_WL134_S34_001", - "Reads_WL70_S39_001", - "Reads_WL147_S4_001", - "Reads_WL162_S15_001", - "Reads_WL182_S25_001", - "Reads_WL183_S26_001", - "Reads_WL1161and2_S12_001", - "Reads_WL1141_S35_001", - "Reads_WL33_S32_001", - "Reads_WL172_S22_001", - "Reads_WL55_S20_001", - "Reads_WL113_S9_001", - "Reads_WL64_S7_001", - "Reads_WL1382_S8_001", - "Reads_WL180_S31_001", - "Reads_WL68_S23_001", - "Reads_WL79_S13_001", - "Reads_WL3_S6_001", - "Reads_WL891_S18_001", - "Reads_WL1331and2_S21_001", - "Reads_WL115_S17_001", - "Reads_WL381and2_S11_001", - "Reads_WL110_S14_001", - "Reads_WL148_S24_001", - "Reads_WL40_S3_001", - "Reads_WL124_S19_001", - "Reads_WL2_S2_001", - "Reads_WL112_S38_001", - "Reads_WL51_S1_001", - "Reads_WL130_S16_001", - "Reads_WL93_S33_001", - "Reads_WL69_S27_001" - ], - "y": [ - 1067, - 2403, - 107124, - 3758, - 3752, - 409, - 1378, - 2565, - 8763, - 3532, - 4061, - 655, - 90925, - 2258, - 7023, - 16825, - 18069, - 3120, - 3540, - 4849, - 2051, - 1886, - 498, - 4444, - 3450, - 2445, - 15888, - 2473, - 3480, - 2055, - 5137, - 1731, - 489, - 11163, - 722, - 382, - 274068, - 746, - 658, - 120 - ] - }, - { - "name": "Acidobacteriales", - "type": "bar", - "x": [ - "Reads_WL8_S29_001", - "Reads_WL39_S28_001", - "Reads_WL84_S36_001", - "Reads_WL187_S5_001", - "Reads_WL121_S37_001", - "Reads_WL142_S30_001", - "Reads_WL57_S40_001", - "Reads_WL106_S10_001", - "Reads_WL134_S34_001", - "Reads_WL70_S39_001", - "Reads_WL147_S4_001", - "Reads_WL162_S15_001", - "Reads_WL182_S25_001", - "Reads_WL183_S26_001", - "Reads_WL1161and2_S12_001", - "Reads_WL1141_S35_001", - "Reads_WL33_S32_001", - "Reads_WL172_S22_001", - "Reads_WL55_S20_001", - "Reads_WL113_S9_001", - "Reads_WL64_S7_001", - "Reads_WL1382_S8_001", - "Reads_WL180_S31_001", - "Reads_WL68_S23_001", - "Reads_WL79_S13_001", - "Reads_WL3_S6_001", - "Reads_WL891_S18_001", - "Reads_WL1331and2_S21_001", - "Reads_WL115_S17_001", - "Reads_WL381and2_S11_001", - "Reads_WL110_S14_001", - "Reads_WL148_S24_001", - "Reads_WL40_S3_001", - "Reads_WL124_S19_001", - "Reads_WL2_S2_001", - "Reads_WL112_S38_001", - "Reads_WL51_S1_001", - "Reads_WL130_S16_001", - "Reads_WL93_S33_001", - "Reads_WL69_S27_001" - ], - "y": [ - 12137, - 17888, - 52267, - 45053, - 44897, - 6005, - 17402, - 42779, - 17865, - 43954, - 27499, - 1956, - 4811, - 23317, - 40696, - 51997, - 69164, - 38102, - 35401, - 41497, - 26991, - 22072, - 4960, - 65426, - 10446, - 29576, - 11143, - 51228, - 14996, - 29069, - 10823, - 28588, - 5625, - 27215, - 11115, - 4565, - 1411, - 1571, - 1759, - 846 - ] - }, - { - "name": "Saccharomycetales", - "type": "bar", - "x": [ - "Reads_WL8_S29_001", - "Reads_WL39_S28_001", - "Reads_WL84_S36_001", - "Reads_WL187_S5_001", - "Reads_WL121_S37_001", - "Reads_WL142_S30_001", - "Reads_WL57_S40_001", - "Reads_WL106_S10_001", - "Reads_WL134_S34_001", - "Reads_WL70_S39_001", - "Reads_WL147_S4_001", - "Reads_WL162_S15_001", - "Reads_WL182_S25_001", - "Reads_WL183_S26_001", - "Reads_WL1161and2_S12_001", - "Reads_WL1141_S35_001", - "Reads_WL33_S32_001", - "Reads_WL172_S22_001", - "Reads_WL55_S20_001", - "Reads_WL113_S9_001", - "Reads_WL64_S7_001", - "Reads_WL1382_S8_001", - "Reads_WL180_S31_001", - "Reads_WL68_S23_001", - "Reads_WL79_S13_001", - "Reads_WL3_S6_001", - "Reads_WL891_S18_001", - "Reads_WL1331and2_S21_001", - "Reads_WL115_S17_001", - "Reads_WL381and2_S11_001", - "Reads_WL110_S14_001", - "Reads_WL148_S24_001", - "Reads_WL40_S3_001", - "Reads_WL124_S19_001", - "Reads_WL2_S2_001", - "Reads_WL112_S38_001", - "Reads_WL51_S1_001", - "Reads_WL130_S16_001", - "Reads_WL93_S33_001", - "Reads_WL69_S27_001" - ], - "y": [ - 2322, - 112136, - 13396, - 10518, - 10825, - 1227, - 4242, - 9297, - 14660, - 10133, - 331178, - 2112, - 1053, - 7400, - 15893, - 30582, - 24002, - 22435, - 14704, - 8004, - 5029, - 5510, - 974, - 31063, - 13135, - 7661, - 56840, - 10724, - 31896, - 12071, - 11772, - 7335, - 1164, - 14195, - 1567, - 937, - 304, - 2280, - 1213, - 328 - ] - } - ], - "layout": { - "barmode": "stack", - "margin": { - "b": 200 - }, - "title": "Assignments per Sample (Top 20; Order)", - "xaxis": { - "tickangle": -60 - }, - "yaxis": { - "title": "Count" - } - } - }, - "text/html": [ - "
" - ], - "text/vnd.plotly.v1+html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# t = sub.copy()\n", - "# t.set_index(\"Order\", inplace=True)\n", - "# cols = t.columns.tolist()\n", - "# cols = [i.strip(\"Percent_\") for i in cols]\n", - "# t.columns = cols\n", - "# fig = t.iplot(title=\"Top 100 Assignments by Percent\", xTitle=\"Taxonomy\", yTitle=\"Percent\", asFigure=True)\n", - "# fig.layout.margin.update({'b':200})\n", - "# fig.layout.xaxis.update({\"tickangle\":-60, \"title\":\"Taxonomy\"})\n", - "# print(fig)\n", - "# t.iplot?\n", - "# iplot(fig)\n", - "\n", - "data = [go.Bar(x=counts.index, y=counts[tax], name=tax) for tax in counts.columns.tolist()]\n", - "\n", - "layout = go.Layout(\n", - " barmode='stack',\n", - " title='Assignments per Sample (Top 20; Order)',\n", - " margin={'b':200},\n", - " xaxis={\"tickangle\":-60},\n", - " yaxis={\"title\":\"Count\"}\n", - ")\n", - "# print(data)\n", - "fig = go.Figure(data=data, layout=layout)\n", - "iplot(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "data": [ - { - "name": "Lactobacillales", - "type": "bar", - "visible": true, - "x": [ - "WL8_S29_001", - "WL39_S28_001", - "WL84_S36_001", - "WL187_S5_001", - "WL121_S37_001", - "WL142_S30_001", - "WL57_S40_001", - "WL106_S10_001", - "WL134_S34_001", - "WL70_S39_001", - "WL147_S4_001", - "WL162_S15_001", - "WL182_S25_001", - "WL183_S26_001", - "WL1161and2_S12_001", - "WL1141_S35_001", - "WL33_S32_001", - "WL172_S22_001", - "WL55_S20_001", - "WL113_S9_001", - "WL64_S7_001", - "WL1382_S8_001", - "WL180_S31_001", - "WL68_S23_001", - "WL79_S13_001", - "WL3_S6_001", - "WL891_S18_001", - "WL1331and2_S21_001", - "WL115_S17_001", - "WL381and2_S11_001", - "WL110_S14_001", - "WL148_S24_001", - "WL40_S3_001", - "WL124_S19_001", - "WL2_S2_001", - "WL112_S38_001", - "WL51_S1_001", - "WL130_S16_001", - "WL93_S33_001", - "WL69_S27_001" - ], - "y": [ - 9.91815, - 4.875787, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# data traces are taxonomies across samples\n", - "data = [go.Bar(x=percents.index, y=percents[tax], name=tax, visible=True) for tax in percents.columns.tolist()] + \\\n", - " [go.Bar(x=counts.index, y=counts[tax], name=tax, visible=False) for tax in counts.columns.tolist()]\n", - "\n", - "# the number of taxa\n", - "trace_length = len(percents.columns)\n", - "\n", - "# plot buttons\n", - "updatemenus = list([\n", - " dict(type=\"buttons\",\n", - " active=0,\n", - " buttons=list([ \n", - " dict(label = 'Percent',\n", - " method = 'update',\n", - " args = [{'visible': [True]*trace_length + [False]*trace_length},\n", - " {'yaxis': {\"title\": \"Percent\"}}\n", - " ]),\n", - " dict(label = 'Count',\n", - " method = 'update',\n", - " args = [{'visible': [False]*trace_length + [True]*trace_length},\n", - " {'yaxis': {\"title\": \"Count\"}}\n", - " ])\n", - " ]),\n", - " direction = 'left',\n", - " pad = {'r': 0, 't': 0},\n", - " showactive = True,\n", - " x = 0,\n", - " xanchor = 'left',\n", - " y = 1.2,\n", - " yanchor = 'top' \n", - " )\n", - "])\n", - "\n", - "# initial layout\n", - "layout = dict(title=\"Assignments per Sample (Top 20; Order)\",\n", - " updatemenus=updatemenus,\n", - " barmode=\"stack\",\n", - " margin={\"b\": 200},\n", - " xaxis={\"tickangle\": -60},\n", - " yaxis={\"title\": \"Percent\"}\n", - " )\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "iplot(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "jf = pd.read_table(\"/Users/brow015/devel/qc/classifications/logs/bailey_qc/jellyfish/WL106_S10_001_count_summary.txt\",\n", - " header=None, \n", - " names=[\"metric\", \"SAMPLE\"],\n", - " delim_whitespace=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Unique Distinct Total Max_count\n", - "WL8_S29_001 350415648 433132670 1032711102 939692\n", - "WL39_S28_001 546861865 680944156 1827130748 1992783\n", - "WL84_S36_001 1297593177 1647486372 4973585018 5318513\n", - "WL187_S5_001 1105270631 1391611174 4139632811 5287050\n", - "WL121_S37_001 1400998829 1682070600 4939124100 6576649" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "jf = jf.reindex([sample_order[i][0] for i in range(len(sample_order))])\n", - "jf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "data": [ - { - "name": "Total", - "type": "bar", - "x": [ - "WL8_S29_001", - "WL39_S28_001", - "WL84_S36_001", - "WL187_S5_001", - "WL121_S37_001", - "WL142_S30_001", - "WL57_S40_001", - "WL106_S10_001", - "WL134_S34_001", - "WL70_S39_001", - "WL147_S4_001", - "WL162_S15_001", - "WL182_S25_001", - "WL183_S26_001", - "WL1161and2_S12_001", - "WL1141_S35_001", - "WL33_S32_001", - "WL172_S22_001", - "WL55_S20_001", - "WL113_S9_001", - "WL64_S7_001", - "WL1382_S8_001", - "WL180_S31_001", - "WL68_S23_001", - "WL79_S13_001", - "WL3_S6_001", - "WL891_S18_001", - "WL1331and2_S21_001", - "WL115_S17_001", - "WL381and2_S11_001", - "WL110_S14_001", - "WL148_S24_001", - "WL40_S3_001", - "WL124_S19_001", - "WL2_S2_001", - "WL112_S38_001", - "WL51_S1_001", - "WL130_S16_001", - "WL93_S33_001", - "WL69_S27_001" - ], - "y": [ - 1032711102, - 1827130748, - 4973585018, - 4139632811, - 4939124100, - 571038766, - 2085678513, - 4490451873, - 3068748784, - 4565505282, - 3683206620, - 690984726, - 680080483, - 2777555392, - 3877020916, - 6777901266, - 7213102474, - 6686813123, - 4994741593, - 4065147492, - 3018436897, - 4123680896, - 780111244, - 6922026222, - 4339960607, - 4399469900, - 5873266948, - 6467864364, - 5755290440, - 5447737629, - 4651469170, - 4932882224, - 1833213532, - 6797994372, - 3245957654, - 3591705527, - 4074521754, - 4459970101, - 7926177327, - 4707505945 - ] - }, - { - "name": "Unique", - "type": "bar", - "x": [ - "WL8_S29_001", - "WL39_S28_001", - "WL84_S36_001", - "WL187_S5_001", - "WL121_S37_001", - "WL142_S30_001", - "WL57_S40_001", - "WL106_S10_001", - "WL134_S34_001", - "WL70_S39_001", - "WL147_S4_001", - "WL162_S15_001", - "WL182_S25_001", - "WL183_S26_001", - "WL1161and2_S12_001", - "WL1141_S35_001", - "WL33_S32_001", - "WL172_S22_001", - "WL55_S20_001", - "WL113_S9_001", - "WL64_S7_001", - "WL1382_S8_001", - "WL180_S31_001", - "WL68_S23_001", - "WL79_S13_001", - "WL3_S6_001", - "WL891_S18_001", - "WL1331and2_S21_001", - "WL115_S17_001", - "WL381and2_S11_001", - "WL110_S14_001", - "WL148_S24_001", - "WL40_S3_001", - "WL124_S19_001", - "WL2_S2_001", - "WL112_S38_001", - "WL51_S1_001", - "WL130_S16_001", - "WL93_S33_001", - "WL69_S27_001" - ], - "y": [ - 350415648, - 546861865, - 1297593177, - 1105270631, - 1400998829, - 157205970, - 471542428, - 1199872361, - 917535868, - 1240862269, - 1023199094, - 219245754, - 189714738, - 683196345, - 961213226, - 1964080341, - 2311563072, - 1829479162, - 1525010392, - 1259185512, - 702833038, - 1065237509, - 198972324, - 2078677939, - 955109301, - 1282359806, - 1144686818, - 1724900246, - 1288952178, - 1265307753, - 1008320998, - 1100054477, - 288836463, - 2095014377, - 877525748, - 605602717, - 808146061, - 865246664, - 1364801098, - 620010018 - ] - } - ], - "layout": { - "margin": { - "b": 200 - }, - "title": "Kmer", - "xaxis": { - "tickangle": -60 - }, - "yaxis": { - "title": "Count" - } - } - }, - "text/html": [ - "
" - ], - "text/vnd.plotly.v1+html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data = [go.Bar(x=jf.index, y=jf[i], name=i) for i in [\"Total\", \"Unique\"]]\n", - "\n", - "layout = go.Layout(\n", - "# barmode='stack',\n", - " title='Kmer',\n", - " margin={'b':200},\n", - " xaxis={\"tickangle\":-60},\n", - " yaxis={\"title\":\"Count\"}\n", - ")\n", - "# print(data)\n", - "fig = go.Figure(data=data, layout=layout)\n", - "iplot(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def lc(x):\n", - " \"\"\"Computes the ordinary and generalized Lorenz curve of a list.\n", - "\n", - " In R::\n", - "\n", - " > library(ineq)\n", - " > t <- c(1,2,6,7,8)\n", - " > lc <- Lc(t)\n", - " > lc$p\n", - " [1] 0.0 0.2 0.4 0.6 0.8 1.0\n", - " > lc$L\n", - " [1] 0.00000000 0.04166667 0.12500000 0.37500000 0.66666667 1.00000000\n", - "\n", - " Args:\n", - " x (list): list or array of positive numbers\n", - "\n", - " Returns:\n", - " tuple: list of percentages, list with values of the ordinary Lorenz curve\n", - "\n", - " >>> import numpy as np\n", - " >>> t = [1,2,np.nan,7,8]\n", - " >>> p, L, Lg = lc(t)\n", - " >>> len(p) == len(L) == len(Lg)\n", - " True\n", - " >>> p[1:4]\n", - " array([ 0.25, 0.5 , 0.75])\n", - " >>> L[1:4] # doctest: +ELLIPSIS\n", - " array([ 0.055..., 0.166..., 0.555...])\n", - " >>> Lg[1:4]\n", - " array([ 0.25, 0.75, 2.5 ])\n", - " >>> t = [1,2,np.nan,7,-8]\n", - " >>> lc(t) # doctest: +ELLIPSIS\n", - " Traceback (most recent call last):\n", - " ...\n", - " ValueError: x contained negative number\n", - " \"\"\"\n", - " assert len(x) > 0, \"x is empty\"\n", - " a = np.array(x, dtype=float)\n", - " a = a[np.isfinite(a)]\n", - " if a.min() < 0:\n", - " raise ValueError(\"x contained negative number\")\n", - " a.sort(kind='mergesort')\n", - " a_len = len(a)\n", - " p = np.arange(1, len(a) + 1) / len(a)\n", - " p = np.append([0], p)\n", - " L = a.cumsum() / a.sum()\n", - " L = np.append([0], L)\n", - " Lg = L * np.mean(a)\n", - " return p, L, Lg" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def gini(x, corr=False):\n", - " \"\"\"Calculates Gini coefficient, the measure of inequality among values\n", - " of a frequency distribution. A Gini coefficient of zero expresses\n", - " perfect equality.\n", - "\n", - " Port from ineq package in R::\n", - "\n", - " > library(ineq)\n", - " > t <- c(1,2,6,7,8)\n", - " > Gini(t)\n", - " [1] 0.3166667\n", - " > Gini(t, corr=TRUE)\n", - " [1] 0.3958333\n", - "\n", - " Args:\n", - " x (list): list or array of numbers\n", - " corr (Optional[bool]): finite sample correction\n", - "\n", - " Returns:\n", - " float\n", - "\n", - " >>> import numpy as np\n", - " >>> t = [1,2,6,7,8]\n", - " >>> gini(t) # doctest: +ELLIPSIS\n", - " 0.3166...\n", - " >>> gini(t, corr=True) # doctest: +ELLIPSIS\n", - " 0.3958...\n", - " >>> gini([]) # doctest: +ELLIPSIS\n", - " Traceback (most recent call last):\n", - " ...\n", - " AssertionError: x is empty\n", - " >>> t = [1,2,6,7,\"A\"]\n", - " >>> gini(t) # doctest: +ELLIPSIS\n", - " Traceback (most recent call last):\n", - " ...\n", - " ValueError: could not convert...\n", - " \"\"\"\n", - " x = np.array(x, dtype=float)\n", - " # filter out nan values as list is coming from merged dataframe\n", - " x = x[~np.isnan(x)]\n", - " n = len(x)\n", - " assert n > 0, \"x is empty\"\n", - " x.sort(kind=\"mergesort\")\n", - " G = sum(np.arange(1, n + 1) * x)\n", - " G = 2 * G / sum(x) - (n + 1)\n", - " if corr:\n", - " return G / (n - 1)\n", - " else:\n", - " return G / n" - ] - }, - { - "cell_type": "code", - "execution_count": 444, - "metadata": {}, - "outputs": [], - "source": [ - "l = m[\"Reads_WL69_S27_001\"].values\n", - "l = m[\"Reads_WL84_S36_001\"].values" - ] - }, - { - "cell_type": "code", - "execution_count": 463, - "metadata": {}, - "outputs": [], - "source": [ - "tmp = m.copy()\n", - "tmp = tmp[(~tmp[tax_level].str.contains(\"unclassified|cannot\"))]\n", - "sample_order = []\n", - "for read_col in tmp.columns.tolist():\n", - " if read_col.startswith(\"Reads_\"):\n", - " sample_order.append([read_col.strip(\"Reads_\"), gini(tmp[read_col])])" - ] - }, - { - "cell_type": "code", - "execution_count": 464, - "metadata": {}, - "outputs": [], - "source": [ - "sample_order = sorted(sample_order, key=lambda x: x[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 465, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['WL8_S29_001',\n", - " 'WL39_S28_001',\n", - " 'WL84_S36_001',\n", - " 'WL187_S5_001',\n", - " 'WL121_S37_001',\n", - " 'WL142_S30_001',\n", - " 'WL57_S40_001',\n", - " 'WL106_S10_001',\n", - " 'WL134_S34_001',\n", - " 'WL70_S39_001',\n", - " 'WL147_S4_001',\n", - " 'WL162_S15_001',\n", - " 'WL182_S25_001',\n", - " 'WL183_S26_001',\n", - " 'WL1161and2_S12_001',\n", - " 'WL1141_S35_001',\n", - " 'WL33_S32_001',\n", - " 'WL172_S22_001',\n", - " 'WL55_S20_001',\n", - " 'WL113_S9_001',\n", - " 'WL64_S7_001',\n", - " 'WL1382_S8_001',\n", - " 'WL180_S31_001',\n", - " 'WL68_S23_001',\n", - " 'WL79_S13_001',\n", - " 'WL3_S6_001',\n", - " 'WL891_S18_001',\n", - " 'WL1331and2_S21_001',\n", - " 'WL115_S17_001',\n", - " 'WL381and2_S11_001',\n", - " 'WL110_S14_001',\n", - " 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