Mercurial > repos > mheinzl > fsd
changeset 14:2a2308390e8f draft
planemo upload for repository https://github.com/monikaheinzl/duplexanalysis/tree/master/tools/fsd commit 94deec54d27310addc8b020e8eef6d2b2508d2cf
| author | mheinzl |
|---|---|
| date | Tue, 17 Jul 2018 06:23:03 -0400 |
| parents | 2921d77df2ee |
| children | 32921a67437b |
| files | fsd.py fsd.xml |
| diffstat | 2 files changed, 3 insertions(+), 433 deletions(-) [+] |
line wrap: on
line diff
--- a/fsd.py Wed May 23 14:56:37 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,432 +0,0 @@ -#!/usr/bin/env python - -# Family size distribution of SSCSs -# -# Author: Monika Heinzl, Johannes-Kepler University Linz (Austria) -# Contact: monika.heinzl@edumail.at -# -# Takes at least one TABULAR file with tags before the alignment to the SSCS, but up to 4 files can be provided, as input. -# The program produces a plot which shows the distribution of family sizes of the all SSCSs from the input files and -# a CSV file with the data of the plot, as well as a TXT file with all tags of the DCS and their family sizes. -# If only one file is provided, then a family size distribution, which is separated after SSCSs without a partner and DCSs, is produced. -# Whereas a family size distribution with multiple data in one plot is produced, when more than one file (up to 4) is given. - -# USAGE: python FSD_Galaxy_1.4_commandLine_FINAL.py --inputFile1 filename --inputName1 filename --inputFile2 filename2 --inputName2 filename2 --inputFile3 filename3 --inputName3 filename3 --inputFile4 filename4 --inputName4 filename4 --sep "characterWhichSeparatesCSVFile" --output_csv outptufile_name_csv --output_pdf outptufile_name_pdf - -import numpy -import matplotlib.pyplot as plt -from matplotlib.backends.backend_pdf import PdfPages -import argparse -import sys -import os -import re - -def readFileReferenceFree(file): - with open(file, 'r') as dest_f: - data_array = numpy.genfromtxt(dest_f, skip_header=0, delimiter='\t', comments='#', dtype='string') - return(data_array) - -def make_argparser(): - parser = argparse.ArgumentParser(description='Family Size Distribution of duplex sequencing data') - parser.add_argument('--inputFile1', - help='Tabular File with three columns: ab or ba, tag and family size.') - parser.add_argument('--inputName1') - parser.add_argument('--inputFile2',default=None, - help='Tabular File with three columns: ab or ba, tag and family size.') - parser.add_argument('--inputName2') - parser.add_argument('--inputFile3',default=None, - help='Tabular File with three columns: ab or ba, tag and family size.') - parser.add_argument('--inputName3') - parser.add_argument('--inputFile4',default=None, - help='Tabular File with three columns: ab or ba, tag and family size.') - parser.add_argument('--inputName4') - parser.add_argument('--sep', default=",", - help='Separator in the csv file.') - parser.add_argument('--output_pdf', default="data.pdf",type=str, - help='Name of the pdf file.') - parser.add_argument('--output_csv', default="data.csv",type=str, - help='Name of the csv file.') - - return parser - -def compare_read_families(argv): - parser = make_argparser() - args=parser.parse_args(argv[1:]) - - firstFile = args.inputFile1 - name1 = args.inputName1 - - secondFile = args.inputFile2 - name2 = args.inputName2 - thirdFile = args.inputFile3 - name3 = args.inputName3 - fourthFile = args.inputFile4 - name4 = args.inputName4 - - title_file = args.output_csv - title_file2 = args.output_pdf - sep = args.sep - - if type(sep) is not str or len(sep)>1: - print("Error: --sep must be a single character.") - exit(4) - - plt.rc('figure', figsize=(11.69, 8.27)) # A4 format - plt.rcParams['patch.edgecolor'] = "black" - plt.rcParams['axes.facecolor'] = "E0E0E0" # grey background color - plt.rcParams['xtick.labelsize'] = 14 - plt.rcParams['ytick.labelsize'] = 14 - - list_to_plot = [] - label = [] - data_array_list = [] - - with open(title_file, "w") as output_file, PdfPages(title_file2) as pdf: - fig = plt.figure() - plt.subplots_adjust(bottom=0.25) - if firstFile != str(None): - file1 = readFileReferenceFree(firstFile) - integers = numpy.array(file1[:, 0]).astype(int) ## keep original family sizes - - # for plot: replace all big family sizes by 22 - data1 = numpy.array(file1[:, 0]).astype(int) - bigFamilies = numpy.where(data1 > 20)[0] - data1[bigFamilies] = 22 - - name1 = name1.split(".tabular")[0] - list_to_plot.append(data1) - label.append(name1) - data_array_list.append(file1) - - legend = "\n\n\n{}".format(name1) - plt.text(0.1, 0.11, legend, size=12, transform=plt.gcf().transFigure) - legend1 = "singletons:\nabsolute nr.\n{:,}".format(numpy.bincount(data1)[1]) - plt.text(0.4, 0.11, legend1, size=12, transform=plt.gcf().transFigure) - - legend3 = "rel. freq\n{:.3f}".format(float(numpy.bincount(data1)[1]) / len(data1)) - plt.text(0.5, 0.11, legend3, size=12, transform=plt.gcf().transFigure) - - legend4 = "family size > 20:\nabsolute nr.\n{:,}".format( - numpy.bincount(data1)[len(numpy.bincount(data1)) - 1].astype(int)) - plt.text(0.6, 0.11, legend4, size=12, transform=plt.gcf().transFigure) - - legend5 = "rel. freq\n{:.3f}".format(float(numpy.bincount(data1)[len(numpy.bincount(data1)) - 1]) / len(data1)) - plt.text(0.7, 0.11, legend5, size=12, transform=plt.gcf().transFigure) - - legend6 = "total length\n{:,}".format(len(data1)) - plt.text(0.8, 0.11, legend6, size=12, transform=plt.gcf().transFigure) - - if secondFile != str(None): - file2 = readFileReferenceFree(secondFile) - data2 = numpy.asarray(file2[:, 0]).astype(int) - bigFamilies2 = numpy.where(data2 > 20)[0] - data2[bigFamilies2] = 22 - - list_to_plot.append(data2) - name2 = name2.split(".tabular")[0] - label.append(name2) - data_array_list.append(file2) - - plt.text(0.1, 0.09, name2, size=12, transform=plt.gcf().transFigure) - - legend1 = "{:,}".format(numpy.bincount(data2)[1]) - plt.text(0.4, 0.09, legend1, size=12, transform=plt.gcf().transFigure) - - legend3 = "{:.3f}".format(float(numpy.bincount(data2)[1]) / len(data2)) - plt.text(0.5, 0.09, legend3, size=12, transform=plt.gcf().transFigure) - - legend4 = "{:,}".format(numpy.bincount(data2)[len(numpy.bincount(data2)) - 1].astype(int)) - plt.text(0.6, 0.09, legend4, size=12, transform=plt.gcf().transFigure) - - legend5 = "{:.3f}".format(float(numpy.bincount(data2)[len(numpy.bincount(data2)) - 1]) / len(data2)) - plt.text(0.7, 0.09, legend5, size=12, transform=plt.gcf().transFigure) - - legend6 = "{:,}".format(len(data2)) - plt.text(0.8, 0.09, legend6, size=12, transform=plt.gcf().transFigure) - - if thirdFile != str(None): - file3 = readFileReferenceFree(thirdFile) - - data3 = numpy.asarray(file3[:, 0]).astype(int) - bigFamilies3 = numpy.where(data3 > 20)[0] - data3[bigFamilies3] = 22 - - list_to_plot.append(data3) - name3 = name3.split(".tabular")[0] - label.append(name3) - data_array_list.append(file3) - - plt.text(0.1, 0.07, name3, size=12, transform=plt.gcf().transFigure) - - legend1 = "{:,}".format(numpy.bincount(data3)[1]) - plt.text(0.4, 0.07, legend1, size=12, transform=plt.gcf().transFigure) - - legend3 = "{:.3f}".format(float(numpy.bincount(data3)[1]) / len(data3)) - plt.text(0.5, 0.07, legend3, size=12, transform=plt.gcf().transFigure) - - legend4 = "{:,}".format(numpy.bincount(data3)[len(numpy.bincount(data3)) - 1].astype(int)) - plt.text(0.6, 0.07, legend4, size=12, transform=plt.gcf().transFigure) - - legend5 = "{:.3f}".format(float(numpy.bincount(data3)[len(numpy.bincount(data3)) - 1]) / len(data3)) - plt.text(0.7, 0.07, legend5, size=12, transform=plt.gcf().transFigure) - - legend6 = "{:,}".format(len(data3)) - plt.text(0.8, 0.07, legend6, size=12, transform=plt.gcf().transFigure) - - if fourthFile != str(None): - file4 = readFileReferenceFree(fourthFile) - - data4 = numpy.asarray(file4[:, 0]).astype(int) - bigFamilies4 = numpy.where(data4 > 20)[0] - data4[bigFamilies4] = 22 - - list_to_plot.append(data4) - name4 = name4.split(".tabular")[0] - label.append(name4) - data_array_list.append(file4) - - plt.text(0.1, 0.05, name4, size=12, transform=plt.gcf().transFigure) - - legend1 = "{:,}".format(numpy.bincount(data4)[1]) - plt.text(0.4, 0.05, legend1, size=12, transform=plt.gcf().transFigure) - - legend4 = "{:.3f}".format(float(numpy.bincount(data4)[1]) / len(data4)) - plt.text(0.5, 0.05, legend4, size=12, transform=plt.gcf().transFigure) - - legend4 = "{:,}".format(numpy.bincount(data4)[len(numpy.bincount(data4)) - 1].astype(int)) - plt.text(0.6, 0.05, legend4, size=12, transform=plt.gcf().transFigure) - - legend5 = "{:.3f}".format(float(numpy.bincount(data4)[len(numpy.bincount(data4)) - 1]) / len(data4)) - plt.text(0.7, 0.05, legend5, size=12, transform=plt.gcf().transFigure) - - legend6 = "{:,}".format(len(data4)) - plt.text(0.8, 0.05, legend6, size=12, transform=plt.gcf().transFigure) - - maximumX = numpy.amax(numpy.concatenate(list_to_plot)) - minimumX = numpy.amin(numpy.concatenate(list_to_plot)) - - counts = plt.hist(list_to_plot, bins=range(minimumX, maximumX + 1), stacked=False, edgecolor="black", - linewidth=1, label=label, align="left", alpha=0.7, rwidth=0.8) - - ticks = numpy.arange(minimumX - 1, maximumX, 1) - ticks1 = map(str, ticks) - ticks1[len(ticks1) - 1] = ">20" - plt.xticks(numpy.array(ticks), ticks1) - - plt.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(0.9, 1)) - # plt.title("Family Size Distribution", fontsize=14) - plt.xlabel("Family size", fontsize=14) - plt.ylabel("Absolute Frequency", fontsize=14) - plt.margins(0.01, None) - plt.grid(b=True, which="major", color="#424242", linestyle=":") - pdf.savefig(fig) - plt.close() - - # write data to CSV file - output_file.write("Values from family size distribution with all datasets\n") - output_file.write("\nFamily size") - for i in label: - output_file.write("{}{}".format(sep, i)) - # output_file.write("{}sum".format(sep)) - output_file.write("\n") - j = 0 - for fs in counts[1][0:len(counts[1]) - 1]: - if fs == 21: - fs = ">20" - else: - fs = "={}".format(fs) - output_file.write("FS{}{}".format(fs, sep)) - # values_of_fs = [] - if len(label) == 1: - output_file.write("{}{}".format(int(counts[0][j]), sep)) - # values_of_fs.append(int(counts[0][j])) - else: - for n in range(len(label)): - output_file.write("{}{}".format(int(counts[0][n][j]), sep)) - # values_of_fs.append(int(counts[0][n][j])) - output_file.write("\n") - #output_file.write("{}\n".format(sum(values_of_fs))) - j += 1 - output_file.write("sum{}".format(sep)) - values_for_sum = [] - if len(label) == 1: - output_file.write("{}{}".format(int(sum(counts[0])), sep)) - values_for_sum.append(int(sum(counts[0]))) - else: - for i in counts[0]: - output_file.write("{}{}".format(int(sum(i)), sep)) - values_for_sum.append(int(sum(i))) - - output_file.write("{}\n".format(sum(values_for_sum))) - -### Family size distribution after DCS and SSCS - for dataset, data, name_file in zip(list_to_plot, data_array_list, label): - maximumX = numpy.amax(dataset) - minimumX = numpy.amin(dataset) - - tags = numpy.array(data[:, 2]) - seq = numpy.array(data[:, 1]) - data = numpy.array(dataset) - - # find all unique tags and get the indices for ALL tags, but only once - u, index_unique, c = numpy.unique(numpy.array(seq), return_counts=True, return_index=True) - d = u[c > 1] - - # get family sizes, tag for duplicates - duplTags_double = data[numpy.in1d(seq, d)] - duplTags = duplTags_double[0::2] # ab of DCS - duplTagsBA = duplTags_double[1::2] # ba of DCS - - duplTags_double_tag = tags[numpy.in1d(seq, d)] - duplTags_double_seq = seq[numpy.in1d(seq, d)] - - # get family sizes for SSCS with no partner - ab = numpy.where(tags == "ab")[0] - abSeq = seq[ab] - ab = data[ab] - ba = numpy.where(tags == "ba")[0] - baSeq = seq[ba] - ba = data[ba] - - dataAB = ab[numpy.in1d(abSeq, d, invert=True)] - dataBA = ba[numpy.in1d(baSeq, d, invert=True)] - - # write DCS tags to file - # with open("DCS information_{}.txt".format(firstFile), "w") as file: - # for t, s, f in zip(duplTags_double_tag, duplTags_double_seq, duplTags_double): - # file.write("{}\t{}\t{}\n".format(t, s, f)) - - list1 = [duplTags_double, dataAB, dataBA] # list for plotting - - ## information for family size >= 3 - dataAB_FS3 = dataAB[dataAB >= 3] - dataBA_FS3 = dataBA[dataBA >= 3] - ab_FS3 = ab[ab >= 3] - ba_FS3 = ba[ba >= 3] - - duplTags_FS3 = duplTags[(duplTags >= 3) & (duplTagsBA >= 3)] # ab+ba with FS>=3 - duplTags_FS3_BA = duplTagsBA[(duplTags >= 3) & (duplTagsBA >= 3)] # ba+ab with FS>=3 - duplTags_double_FS3 = len(duplTags_FS3)+len(duplTags_FS3_BA) # both ab and ba strands with FS>=3 - - fig = plt.figure() - - plt.subplots_adjust(bottom=0.3) - counts = plt.hist(list1, bins=range(minimumX, maximumX + 1), stacked=True, - label=["duplex", "ab", "ba"], edgecolor="black", linewidth=1, - align="left", color=["#FF0000", "#5FB404", "#FFBF00"]) - # tick labels of x axis - ticks = numpy.arange(minimumX - 1, maximumX, 1) - ticks1 = map(str, ticks) - ticks1[len(ticks1) - 1] = ">20" - plt.xticks(numpy.array(ticks), ticks1) - singl = counts[0][2][0] # singletons - last = counts[0][2][len(counts[0][0]) - 1] # large families - - plt.legend(loc='upper right', fontsize=14, bbox_to_anchor=(0.9, 1), frameon=True) - # plt.title(name1, fontsize=14) - plt.xlabel("Family size", fontsize=14) - plt.ylabel("Absolute Frequency", fontsize=14) - plt.margins(0.01, None) - plt.grid(b=True, which="major", color="#424242", linestyle=":") - - ## extra information beneath the plot - legend = "SSCS ab= \nSSCS ba= \nDCS (total)= \nlength of dataset=" - plt.text(0.1, 0.09, legend, size=12, transform=plt.gcf().transFigure) - - legend = "absolute numbers\n\n{:,}\n{:,}\n{:,} ({:,})\n{:,}" \ - .format(len(dataAB), len(dataBA), len(duplTags), len(duplTags_double), - (len(dataAB) + len(dataBA) + len(duplTags))) - plt.text(0.35, 0.09, legend, size=12, transform=plt.gcf().transFigure) - - legend = "relative frequencies\nunique\n{:.3f}\n{:.3f}\n{:.3f}\n{:,}" \ - .format(float(len(dataAB)) / (len(dataAB) + len(dataBA) + len(duplTags)), - float(len(dataBA)) / (len(dataAB) + len(dataBA) + len(duplTags)), - float(len(duplTags)) / (len(dataAB) + len(dataBA) + len(duplTags)), - (len(dataAB) + len(dataBA) + len(duplTags))) - plt.text(0.54, 0.09, legend, size=12, transform=plt.gcf().transFigure) - - legend = "total\n{:.3f}\n{:.3f}\n{:.3f} ({:.3f})\n{:,}" \ - .format(float(len(dataAB)) / (len(ab) + len(ba)), float(len(dataBA)) / (len(ab) + len(ba)), - float(len(duplTags)) / (len(ab) + len(ba)), - float(len(duplTags_double)) / (len(ab) + len(ba)), (len(ab) + len(ba))) - plt.text(0.64, 0.09, legend, size=12, transform=plt.gcf().transFigure) - - legend1 = "\nsingletons:\nfamily size > 20:" - plt.text(0.1, 0.03, legend1, size=12, transform=plt.gcf().transFigure) - - legend4 = "{:,}\n{:,}".format(singl.astype(int), last.astype(int)) - plt.text(0.35, 0.03, legend4, size=12, transform=plt.gcf().transFigure) - - legend3 = "{:.3f}\n{:.3f}".format(singl / len(data),last / len(data)) - plt.text(0.54, 0.03, legend3, size=12, transform=plt.gcf().transFigure) - - pdf.savefig(fig) - plt.close() - - # write same information to a csv file - count = numpy.bincount(integers) # original counts of family sizes - output_file.write("\nDataset:{}{}\n".format(sep, name_file)) - output_file.write("max. family size:{}{}\n".format(sep, max(integers))) - output_file.write("absolute frequency:{}{}\n".format(sep, count[len(count) - 1])) - output_file.write("relative frequency:{}{:.3f}\n\n".format(sep, float(count[len(count) - 1]) / sum(count))) - - output_file.write("{}singletons:{}{}family size > 20:\n".format(sep, sep, sep)) - output_file.write( - "{}absolute nr.{}rel. freq{}absolute nr.{}rel. freq{}total length\n".format(sep, sep, sep, sep, sep)) - output_file.write("{}{}{}{}{:.3f}{}{}{}{:.3f}{}{}\n\n".format(name_file, sep, singl.astype(int), sep, - singl / len(data), sep,last.astype(int), sep, - last / len(data), sep, len(data))) - - ## information for FS >= 1 - output_file.write( - "The unique frequencies were calculated from the dataset where the tags occured only once (=ab without DCS, ba without DCS)\n" \ - "Whereas the total frequencies were calculated from the whole dataset (=including the DCS).\n\n") - output_file.write("FS >= 1{}{}unique:{}total:\n".format(sep, sep, sep)) - output_file.write("nr./rel. freq of ab={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataAB), sep, - float(len(dataAB)) / (len(dataAB) + len(dataBA) + len( duplTags)), sep, - float(len(dataAB)) / (len(ab) + len(ba)))) - output_file.write("nr./rel. freq of ba={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataBA), sep, - float(len(dataBA)) / (len(dataBA) + len(dataBA) + len(duplTags)), sep, - float(len(dataBA)) / (len(ba) + len(ba)))) - output_file.write( - "nr./rel. freq of DCS (total)={}{} ({}){}{:.3f}{}{:.3f} ({:.3f})\n".format(sep, len(duplTags), len(duplTags_double), sep, - float(len(duplTags)) / ( len(dataAB) + len( dataBA) + len(duplTags)), - sep, float(len(duplTags)) / ( len(ab) + len(ba)), - float( len(duplTags_double)) / (len(ab) + len(ba)))) - output_file.write( - "length of dataset={}{}{}{}{}{}\n".format(sep, (len(dataAB) + len(dataBA) + len(duplTags)), sep, - (len(dataAB) + len(dataBA) + len(duplTags)), sep,(len(ab) + len(ba)))) - ## information for FS >= 3 - output_file.write("FS >= 3{}{}unique:{}total:\n".format(sep, sep, sep)) - output_file.write("nr./rel. freq of ab={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataAB_FS3), sep, - float(len(dataAB_FS3)) / (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), - sep, float(len(dataAB_FS3)) / ( len(ab_FS3) + len(ba_FS3)))) - output_file.write("nr./rel. freq of ba={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataBA_FS3), sep, - float(len(dataBA_FS3)) / ( len(dataBA_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), - sep,float(len(dataBA_FS3)) / (len(ba_FS3) + len(ba_FS3)))) - output_file.write( - "nr./rel. freq of DCS (total)={}{} ({}){}{:.3f}{}{:.3f} ({:.3f})\n".format(sep, len(duplTags_FS3),duplTags_double_FS3, - sep, float(len( duplTags_FS3)) / (len(dataBA_FS3) + len(duplTags_FS3)), - sep, float(len(duplTags_FS3)) / (len(ab_FS3) + len(ba_FS3)), - float(duplTags_double_FS3) / (len(ab_FS3) + len(ba_FS3)))) - output_file.write( - "length of dataset={}{}{}{}{}{}\n".format(sep, (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), sep, - (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), sep, - (len(ab_FS3) + len(ba_FS3)))) - - output_file.write("\nValues from family size distribution\n") - output_file.write("{}duplex{}ab{}ba{}sum\n".format(sep, sep, sep, sep)) - for dx, ab, ba, fs in zip(counts[0][0], counts[0][1], counts[0][2], counts[1]): - if fs == 21: - fs = ">20" - else: - fs = "={}".format(fs) - ab1 = ab - dx - ba1 = ba - ab - output_file.write( - "FS{}{}{}{}{}{}{}{}{}\n".format(fs, sep, int(dx), sep, int(ab1), sep, int(ba1), sep, int(ba))) - - print("Files successfully created!") - -if __name__ == '__main__': - sys.exit(compare_read_families(sys.argv))
--- a/fsd.xml Wed May 23 14:56:37 2018 -0400 +++ b/fsd.xml Tue Jul 17 06:23:03 2018 -0400 @@ -5,10 +5,12 @@ <requirements> <requirement type="package" version="2.7">python</requirement> <requirement type="package" version="1.4">matplotlib</requirement> + <requirement type="package" version="0.0.1">duplexanalysis</requirement> + </requirements> <command> - python2 $__tool_directory__/fsd.py --inputFile1 "$file1" --inputName1 "$file1.name" --inputFile2 "$file2" --inputName2 "$file2.name" --inputFile3 "$file3" --inputName3 "$file3.name" --inputFile4 "$file4" --inputName4 "$file4.name" --sep $separator --output_pdf $output_pdf --output_csv $output_csv + fsd.py --inputFile1 "$file1" --inputName1 "$file1.name" --inputFile2 "$file2" --inputName2 "$file2.name" --inputFile3 "$file3" --inputName3 "$file3.name" --inputFile4 "$file4" --inputName4 "$file4.name" --sep $separator --output_pdf $output_pdf --output_csv $output_csv </command> <inputs> <param name="file1" type="data" format="tabular" label="Dataset 1: input tags" optional="false"/>