Mercurial > repos > mheinzl > hd
diff hd.py @ 19:2e9f7ea7ae93 draft
planemo upload for repository https://github.com/monikaheinzl/duplexanalysis_galaxy/tree/master/tools/hd commit dfaab79252a858e8df16bbea3607ebf1b6962e5a-dirty
| author | mheinzl |
|---|---|
| date | Mon, 08 Oct 2018 05:56:04 -0400 |
| parents | a8581bf627fd |
| children | b084b6a8e3ac |
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--- a/hd.py Wed May 23 14:47:43 2018 -0400 +++ b/hd.py Mon Oct 08 05:56:04 2018 -0400 @@ -13,34 +13,35 @@ # It is also possible to perform the HD analysis with shortened tags with given sizes as input. # The tool can run on a certain number of processors, which can be defined by the user. -# USAGE: python HDnew6_1Plot_FINAL.py --inputFile filename --inputName1 filename --inputFile2 filename2 --inputName2 filename2 --sample_size int/0 --sep "characterWhichSeparatesCSVFile" / -# --only_DCS True --FamilySize3 True --subset_tag True --nproc int --minFS int --maxFS int --nr_above_bars True/False--output_csv outptufile_name_csv --output_pdf outptufile_name_pdf +# USAGE: python hd.py --inputFile filename --inputName1 filename --inputFile2 filename2 --inputName2 filename2 --sample_size int/0 --sep "characterWhichSeparatesCSVFile" / +# --only_DCS True --FamilySize3 True --subset_tag True --nproc int --minFS int --maxFS int --nr_above_bars True/False --output_tabular outptufile_name_tabular --output_pdf outputfile_name_pdf -import numpy +import argparse import itertools import operator -import matplotlib.pyplot as plt -import os.path -import cPickle as pickle -from multiprocessing.pool import Pool +import sys +from collections import Counter from functools import partial -import argparse -import sys -import os +from multiprocessing.pool import Pool + +import matplotlib.pyplot as plt +import numpy from matplotlib.backends.backend_pdf import PdfPages -from collections import Counter + +plt.switch_backend('agg') -def plotFSDwithHD2(familySizeList1,maximumXFS,minimumXFS, originalCounts, - title_file1, subtitle, pdf, relative=False, diff = True): + +def plotFSDwithHD2(familySizeList1, maximumXFS, minimumXFS, originalCounts, + title_file1, subtitle, pdf, relative=False, diff=True): if diff is False: colors = ["#e6194b", "#3cb44b", "#ffe119", "#0082c8", "#f58231", "#911eb4"] - labels = ["HD=1", "HD=2", "HD=3", "HD=4", "HD=5-8","HD>8"] + labels = ["HD=1", "HD=2", "HD=3", "HD=4", "HD=5-8", "HD>8"] else: colors = ["#93A6AB", "#403C14", "#731E41", "#BAB591", "#085B6F", "#E8AA35", "#726C66"] if relative is True: labels = ["d=0", "d=0.1", "d=0.2", "d=0.3", "d=0.4", "d=0.5-0.8", "d>0.8"] else: - labels = ["d=0","d=1", "d=2", "d=3", "d=4", "d=5-8","d>8"] + labels = ["d=0", "d=1", "d=2", "d=3", "d=4", "d=5-8", "d>8"] fig = plt.figure(figsize=(6, 7)) ax = fig.add_subplot(111) @@ -54,11 +55,11 @@ range1 = range(0, maximumXFS + 2) counts = plt.hist(familySizeList1, label=labels, color=colors, stacked=True, - rwidth=0.8,alpha=1, align="left", - edgecolor="None",bins=range1) + rwidth=0.8, alpha=1, align="left", + edgecolor="None", bins=range1) plt.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(1.45, 1)) - #plt.title(title_file1, fontsize=12) + # plt.title(title_file1, fontsize=12) plt.suptitle(subtitle, y=1, x=0.5, fontsize=14) plt.xlabel("Family size", fontsize=14) plt.ylabel("Absolute Frequency", fontsize=14) @@ -79,20 +80,17 @@ plt.text(0.15, -0.08, legend, size=12, transform=plt.gcf().transFigure) count = numpy.bincount(originalCounts) # original counts - legend1 = "{}\n{}\n{:.5f}" \ - .format(max(originalCounts), count[len(count) - 1], float(count[len(count) - 1]) / sum(count)) + legend1 = "{}\n{}\n{:.5f}".format(max(originalCounts), count[len(count) - 1], float(count[len(count) - 1]) / sum(count)) plt.text(0.5, -0.08, legend1, size=12, transform=plt.gcf().transFigure) - legend3 = "singletons\n{:,}\n{:.5f}".format(int(counts[0][len(counts[0]) - 1][1]), - float(counts[0][len(counts[0]) - 1][1]) / sum( - counts[0][len(counts[0]) - 1])) + legend3 = "singletons\n{:,}\n{:.5f}".format(int(counts[0][len(counts[0]) - 1][1]), float(counts[0][len(counts[0]) - 1][1]) / sum(counts[0][len(counts[0]) - 1])) plt.text(0.7, -0.08, legend3, transform=plt.gcf().transFigure, size=12) plt.grid(b=True, which='major', color='#424242', linestyle=':') pdf.savefig(fig, bbox_inches="tight") plt.close("all") - -def plotHDwithFSD(list1,maximumX,minimumX, subtitle, lenTags, title_file1,pdf, - xlabel,relative=False, nr_above_bars = True): + + +def plotHDwithFSD(list1, maximumX, minimumX, subtitle, lenTags, title_file1, pdf, xlabel, relative=False, nr_above_bars=True): if relative is True: step = 0.1 else: @@ -120,7 +118,7 @@ bins = counts[1] # width of bins counts = numpy.array(map(int, counts[0][5])) plt.suptitle(subtitle, y=1, x=0.5, fontsize=14) - # plt.title(title_file1, fontsize=12) + # plt.title(title_file1, fontsize=12) plt.xlabel(xlabel, fontsize=14) plt.ylabel("Absolute Frequency", fontsize=14) @@ -138,8 +136,8 @@ else: plt.annotate("{:,}\n{:.3f}".format(x_label, float(x_label) / sum(counts), 1), xy=(label, x_label + len(con_list1) * 0.01), - xycoords="data", color="#000066",fontsize=10) - + xycoords="data", color="#000066", fontsize=10) + legend = "sample size= {:,} against {:,}".format(sum(counts), lenTags) plt.text(0.14, -0.01, legend, size=12, transform=plt.gcf().transFigure) @@ -147,47 +145,42 @@ plt.close("all") plt.clf() -def plotHDwithinSeq_Sum2(sum1, sum2,sum1min, sum2min, min_value, lenTags, title_file1, pdf): + +def plotHDwithinSeq_Sum2(sum1, sum1min, sum2, sum2min, min_value, lenTags, title_file1, pdf): fig = plt.figure(figsize=(6, 8)) plt.subplots_adjust(bottom=0.1) - #ham = [sum1, sum2,numpy.array(min_value)] # new hd within tags - ham = [sum1, sum2, sum1min, sum2min, numpy.array(min_value)] # new hd within tags - + ham_partial = [sum1, sum1min, sum2, sum2min, numpy.array(min_value)] # new hd within tags - maximumX = numpy.amax(numpy.concatenate(ham)) - minimumX = numpy.amin(numpy.concatenate(ham)) - maximumY = numpy.amax(numpy.concatenate(map(lambda (x): numpy.bincount(x), ham))) + maximumX = numpy.amax(numpy.concatenate(ham_partial)) + minimumX = numpy.amin(numpy.concatenate(ham_partial)) + maximumY = numpy.amax(numpy.array(numpy.concatenate(map(lambda (x): numpy.bincount(x), ham_partial)))) if len(range(minimumX, maximumX)) == 0: range1 = minimumX else: range1 = range(minimumX, maximumX + 2) - counts = plt.hist(ham, align="left", rwidth=0.8, stacked=False, - # label=[ "HD a", "HD b","HD a+b"], - label=[ "HD a","HD b'", "HD b", "HD a'", "HD a+b"], - #bins=range1, color=[ "#58ACFA", "#FA5858","#585858"], - color=["#58ACFA", "#0404B4", "#FE642E", "#B40431", "#585858"], - edgecolor='black', linewidth=1) + plt.hist(ham_partial, align="left", rwidth=0.8, stacked=False, label=[ "HD a", "HD b'", "HD b", "HD a'", "HD a+b"], bins=range1, color=["#58ACFA", "#0404B4", "#FE642E", "#B40431", "#585858"], edgecolor='black', linewidth=1) + plt.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(1.55, 1)) plt.suptitle('Hamming distances within tags', fontsize=14) - #plt.title(title_file1, fontsize=12) + # plt.title(title_file1, fontsize=12) plt.xlabel("HD", fontsize=14) plt.ylabel("Absolute Frequency", fontsize=14) plt.grid(b=True, which='major', color='#424242', linestyle=':') - - plt.axis((minimumX - 1, maximumX + 1, 0, maximumY * 1.1)) + plt.axis((minimumX - 1, maximumX + 1, 0, maximumY * 1.2)) plt.xticks(numpy.arange(0, maximumX + 1, 1.0)) - plt.ylim((0, maximumY * 1.2)) + # plt.ylim(0, maximumY * 1.2) - legend = "sample size= {:,} against {:,}".format(len(ham[0]), lenTags, lenTags) + legend = "sample size= {:,} against {:,}".format(sum(ham_partial[4]), lenTags) plt.text(0.14, -0.01, legend, size=12, transform=plt.gcf().transFigure) pdf.savefig(fig, bbox_inches="tight") plt.close("all") plt.clf() + def createTableFSD2(list1, diff=True): selfAB = numpy.concatenate(list1) uniqueFS = numpy.unique(selfAB) @@ -208,7 +201,7 @@ continue else: if state == 1: - for i, l in zip(uniqueFS, nr): + for i, l in zip(uniqueFS, nr): for j in table: if j[0] == uniqueFS[l]: count[l, 0] = j[1] @@ -261,31 +254,33 @@ return (final, sumCol) -def createFileFSD2(summary, sumCol, overallSum, output_file, name,sep, rel=False, diff=True): + +def createFileFSD2(summary, sumCol, overallSum, output_file, name, sep, rel=False, diff=True): output_file.write(name) output_file.write("\n") if diff is False: - output_file.write("{}HD=1{}HD=2{}HD=3{}HD=4{}HD=5-8{}HD>8{}sum{}\n".format(sep,sep,sep,sep,sep,sep,sep,sep)) + output_file.write("{}HD=1{}HD=2{}HD=3{}HD=4{}HD=5-8{}HD>8{}sum{}\n".format(sep, sep, sep, sep, sep, sep, sep, sep)) else: if rel is False: - output_file.write("{}diff=0{}diff=1{}diff=2{}diff=3{}diff=4{}diff=5-8{}diff>8{}sum{}\n".format(sep,sep,sep,sep,sep,sep,sep,sep,sep)) + output_file.write("{}diff=0{}diff=1{}diff=2{}diff=3{}diff=4{}diff=5-8{}diff>8{}sum{}\n".format(sep, sep, sep, sep, sep, sep, sep, sep, sep)) else: - output_file.write("{}diff=0{}diff=0.1{}diff=0.2{}diff=0.3{}diff=0.4{}diff=0.5-0.8{}diff>0.8{}sum{}\n".format(sep,sep,sep,sep,sep,sep,sep,sep,sep)) + output_file.write("{}diff=0{}diff=0.1{}diff=0.2{}diff=0.3{}diff=0.4{}diff=0.5-0.8{}diff>0.8{}sum{}\n".format(sep, sep, sep, sep, sep, sep, sep, sep, sep)) for item in summary: for nr in item: if "FS" not in nr and "diff" not in nr: nr = nr.astype(float) nr = nr.astype(int) - output_file.write("{}{}".format(nr,sep)) + output_file.write("{}{}".format(nr, sep)) output_file.write("\n") output_file.write("sum{}".format(sep)) sumCol = map(int, sumCol) for el in sumCol: - output_file.write("{}{}".format(el,sep)) - output_file.write("{}{}".format(overallSum.astype(int),sep)) + output_file.write("{}{}".format(el, sep)) + output_file.write("{}{}".format(overallSum.astype(int), sep)) output_file.write("\n\n") + def createTableHD(list1, row_label): selfAB = numpy.concatenate(list1) uniqueHD = numpy.unique(selfAB) @@ -302,7 +297,7 @@ continue else: if state == 1: - for i, l in zip(uniqueHD, nr): + for i, l in zip(uniqueHD, nr): for j in table: if j[0] == uniqueHD[l]: count[l, 0] = j[1] @@ -339,16 +334,17 @@ sumRow = count.sum(axis=1) sumCol = count.sum(axis=0) - first = ["{}{}".format(row_label,i) for i in uniqueHD] + first = ["{}{}".format(row_label, i) for i in uniqueHD] final = numpy.column_stack((first, count, sumRow)) return (final, sumCol) + def createTableHDwithTags(list1): selfAB = numpy.concatenate(list1) uniqueHD = numpy.unique(selfAB) nr = numpy.arange(0, len(uniqueHD), 1) - count = numpy.zeros((len(uniqueHD), 3)) + count = numpy.zeros((len(uniqueHD), 5)) state = 1 for i in list1: @@ -361,7 +357,7 @@ continue else: if state == 1: - for i, l in zip(uniqueHD, nr): + for i, l in zip(uniqueHD, nr): for j in table: if j[0] == uniqueHD[l]: count[l, 0] = j[1] @@ -370,12 +366,22 @@ for j in table: if j[0] == uniqueHD[l]: count[l, 1] = j[1] - if state == 3: for i, l in zip(uniqueHD, nr): for j in table: if j[0] == uniqueHD[l]: count[l, 2] = j[1] + if state == 4: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 3] = j[1] + if state == 5: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 4] = j[1] + state = state + 1 sumRow = count.sum(axis=1) @@ -385,42 +391,45 @@ return (final, sumCol) -def createFileHD(summary, sumCol, overallSum, output_file, name,sep): + +def createFileHD(summary, sumCol, overallSum, output_file, name, sep): output_file.write(name) output_file.write("\n") - output_file.write("{}FS=1{}FS=2{}FS=3{}FS=4{}FS=5-10{}FS>10{}sum{}\n".format(sep,sep,sep,sep,sep,sep,sep,sep)) + output_file.write("{}FS=1{}FS=2{}FS=3{}FS=4{}FS=5-10{}FS>10{}sum{}\n".format(sep, sep, sep, sep, sep, sep, sep, sep)) for item in summary: for nr in item: if "HD" not in nr and "diff" not in nr: nr = nr.astype(float) nr = nr.astype(int) - output_file.write("{}{}".format(nr,sep)) + output_file.write("{}{}".format(nr, sep)) output_file.write("\n") output_file.write("sum{}".format(sep)) sumCol = map(int, sumCol) for el in sumCol: - output_file.write("{}{}".format(el,sep)) - output_file.write("{}{}".format(overallSum.astype(int),sep)) + output_file.write("{}{}".format(el, sep)) + output_file.write("{}{}".format(overallSum.astype(int), sep)) output_file.write("\n\n") -def createFileHDwithinTag(summary, sumCol, overallSum, output_file, name,sep): + +def createFileHDwithinTag(summary, sumCol, overallSum, output_file, name, sep): output_file.write(name) output_file.write("\n") - output_file.write("{}HD a+b;HD a{}HD b{}sum{}\n".format(sep,sep,sep,sep)) + output_file.write("{}HD a{}HD b'{}HD b{}HD a'{}HD a+b{}sum{}\n".format(sep, sep, sep, sep, sep, sep, sep)) for item in summary: for nr in item: if "HD" not in nr: nr = nr.astype(float) nr = nr.astype(int) - output_file.write("{}{}".format(nr,sep)) + output_file.write("{}{}".format(nr, sep)) output_file.write("\n") output_file.write("sum{}".format(sep)) sumCol = map(int, sumCol) for el in sumCol: - output_file.write("{}{}".format(el,sep)) - output_file.write("{}{}".format(overallSum.astype(int),sep)) + output_file.write("{}{}".format(el, sep)) + output_file.write("{}{}".format(overallSum.astype(int), sep)) output_file.write("\n\n") - + + def hamming(array1, array2): res = 99 * numpy.ones(len(array1)) i = 0 @@ -428,28 +437,28 @@ for a in array1: dist = numpy.array([sum(itertools.imap(operator.ne, a, b)) for b in array2]) # fastest res[i] = numpy.amin(dist[dist > 0]) # pick min distance greater than zero - #print(i) + # print(i) i += 1 return res + def hamming_difference(array1, array2, mate_b): array2 = numpy.unique(array2) # remove duplicate sequences to decrease running time - array1_half = numpy.array([i[0:(len(i)) / 2] for i in array1]) # mate1 part1 - array1_half2 = numpy.array([i[len(i) / 2:len(i)] for i in array1]) # mate1 part 2 + array1_half = numpy.array([i[0:(len(i)) / 2] for i in array1]) # mate1 part1 + array1_half2 = numpy.array([i[len(i) / 2:len(i)] for i in array1]) # mate1 part 2 - array2_half = numpy.array([i[0:(len(i)) / 2] for i in array2]) # mate2 part1 + array2_half = numpy.array([i[0:(len(i)) / 2] for i in array2]) # mate2 part1 array2_half2 = numpy.array([i[len(i) / 2:len(i)] for i in array2]) # mate2 part2 - #diff11 = 999 * numpy.ones(len(array2)) - #relativeDiffList = 999 * numpy.ones(len(array2)) - #ham1 = 999 * numpy.ones(len(array2)) - #ham2 = 999 * numpy.ones(len(array2)) - #min_valueList = 999 * numpy.ones(len(array2)) - #min_tagsList = 999 * numpy.ones(len(array2)) - #diff11_zeros = 999 * numpy.ones(len(array2)) - #min_tagsList_zeros = 999 * numpy.ones(len(array2)) - - + # diff11 = 999 * numpy.ones(len(array2)) + # relativeDiffList = 999 * numpy.ones(len(array2)) + # ham1 = 999 * numpy.ones(len(array2)) + # ham2 = 999 * numpy.ones(len(array2)) + # min_valueList = 999 * numpy.ones(len(array2)) + # min_tagsList = 999 * numpy.ones(len(array2)) + # diff11_zeros = 999 * numpy.ones(len(array2)) + # min_tagsList_zeros = 999 * numpy.ones(len(array2)) + diff11 = [] relativeDiffList = [] ham1 = [] @@ -460,20 +469,20 @@ min_tagsList = [] diff11_zeros = [] min_tagsList_zeros = [] - i = 0 # counter, only used to see how many HDs of tags were already calculated - if mate_b is False: # HD calculation for all a's + i = 0 # counter, only used to see how many HDs of tags were already calculated + if mate_b is False: # HD calculation for all a's half1_mate1 = array1_half half2_mate1 = array1_half2 half1_mate2 = array2_half half2_mate2 = array2_half2 - elif mate_b is True: # HD calculation for all b's + elif mate_b is True: # HD calculation for all b's half1_mate1 = array1_half2 half2_mate1 = array1_half half1_mate2 = array2_half2 half2_mate2 = array2_half for a, b, tag in zip(half1_mate1, half2_mate1, array1): - ## exclude identical tag from array2, to prevent comparison to itself + # exclude identical tag from array2, to prevent comparison to itself sameTag = numpy.where(array2 == tag) indexArray2 = numpy.arange(0, len(array2), 1) index_withoutSame = numpy.delete(indexArray2, sameTag) # delete identical tag from the data @@ -481,63 +490,59 @@ # all tags without identical tag array2_half_withoutSame = half1_mate2[index_withoutSame] array2_half2_withoutSame = half2_mate2[index_withoutSame] - #array2_withoutSame = array2[index_withoutSame] # whole tag (=not splitted into 2 halfs) + # array2_withoutSame = array2[index_withoutSame] # whole tag (=not splitted into 2 halfs) dist = numpy.array([sum(itertools.imap(operator.ne, a, c)) for c in array2_half_withoutSame]) # calculate HD of "a" in the tag to all "a's" or "b" in the tag to all "b's" - min_index = numpy.where(dist == dist.min()) # get index of min HD + min_index = numpy.where(dist == dist.min()) # get index of min HD min_value = dist[min_index] # get minimum HDs min_tag_half2 = array2_half2_withoutSame[min_index] # get all "b's" of the tag or all "a's" of the tag with minimum HD - #min_tag = array2_withoutSame[min_index] # get whole tag with min HD + # min_tag = array2_withoutSame[min_index] # get whole tag with min HD dist2 = numpy.array([sum(itertools.imap(operator.ne, b, e)) for e in min_tag_half2]) # calculate HD of "b" to all "b's" or "a" to all "a's" - for d_1, d_2 in zip(min_value, dist2): + for d, d2 in zip(min_value, dist2): if mate_b is True: # half2, corrects the variable of the HD from both halfs if it is a or b - d = d_2 - d2 = d_1 ham2.append(d) ham2min.append(d2) else: # half1, corrects the variable of the HD from both halfs if it is a or b - d = d_1 - d2 = d_2 ham1.append(d) ham1min.append(d2) - + min_valueList.append(d + d2) min_tagsList.append(tag) - # ham1.append(d) - # ham2.append(d2) difference1 = abs(d - d2) diff11.append(difference1) rel_difference = round(float(difference1) / (d + d2), 1) relativeDiffList.append(rel_difference) - #### tags which have identical parts: + # tags which have identical parts: if d == 0 or d2 == 0: min_tagsList_zeros.append(tag) difference1_zeros = abs(d - d2) diff11_zeros.append(difference1_zeros) i += 1 - - #print(i) - #diff11 = [st for st in diff11 if st != 999] - #ham1 = [st for st in ham1 if st != 999] - #ham2 = [st for st in ham2 if st != 999] - #min_valueList = [st for st in min_valueList if st != 999] - #min_tagsList = [st for st in min_tagsList if st != 999] - #relativeDiffList = [st for st in relativeDiffList if st != 999] - #diff11_zeros = [st for st in diff11_zeros if st != 999] - #min_tagsList_zeros = [st for st in min_tagsList_zeros if st != 999] + + # print(i) + # diff11 = [st for st in diff11 if st != 999] + # ham1 = [st for st in ham1 if st != 999] + # ham2 = [st for st in ham2 if st != 999] + # min_valueList = [st for st in min_valueList if st != 999] + # min_tagsList = [st for st in min_tagsList if st != 999] + # relativeDiffList = [st for st in relativeDiffList if st != 999] + # diff11_zeros = [st for st in diff11_zeros if st != 999] + # min_tagsList_zeros = [st for st in min_tagsList_zeros if st != 999] return ([diff11, ham1, ham2, min_valueList, min_tagsList, relativeDiffList, diff11_zeros, min_tagsList_zeros, ham1min, ham2min]) + def readFileReferenceFree(file): with open(file, 'r') as dest_f: data_array = numpy.genfromtxt(dest_f, skip_header=0, delimiter='\t', comments='#', dtype='string') integers = numpy.array(data_array[:, 0]).astype(int) return(integers, data_array) + def hammingDistanceWithFS(fs, ham): fs = numpy.asarray(fs) maximum = max(ham) @@ -565,7 +570,8 @@ list1 = [data, data2, data3, data4, data5, data6] return(list1, maximum, minimum) -def familySizeDistributionWithHD(fs, ham, diff=False, rel = True): + +def familySizeDistributionWithHD(fs, ham, diff=False, rel=True): hammingDistances = numpy.unique(ham) fs = numpy.asarray(fs) @@ -616,49 +622,49 @@ data6 = fs[hd6] if diff is True: - list1 = [data0,data, data2, data3, data4, data5, data6] + list1 = [data0, data, data2, data3, data4, data5, data6] else: list1 = [data, data2, data3, data4, data5, data6] return(list1, hammingDistances, maximum, minimum) + def make_argparser(): parser = argparse.ArgumentParser(description='Hamming distance analysis of duplex sequencing data') parser.add_argument('--inputFile', help='Tabular File with three columns: ab or ba, tag and family size.') parser.add_argument('--inputName1') - parser.add_argument('--inputFile2',default=None, + 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('--sample_size', default=1000,type=int, + parser.add_argument('--sample_size', default=1000, type=int, help='Sample size of Hamming distance analysis.') - parser.add_argument('--sep', default=",", - help='Separator in the csv file.') - parser.add_argument('--subset_tag', default=0,type=int, + parser.add_argument('--subset_tag', default=0, type=int, help='The tag is shortened to the given number.') - parser.add_argument('--nproc', default=4,type=int, + parser.add_argument('--nproc', default=4, type=int, help='The tool runs with the given number of processors.') - parser.add_argument('--only_DCS', action="store_false", # default=False, type=bool, + parser.add_argument('--only_DCS', action="store_false", help='Only tags of the DCSs are included in the HD analysis') parser.add_argument('--minFS', default=1, type=int, help='Only tags, which have a family size greater or equal than specified, are included in the HD analysis') parser.add_argument('--maxFS', default=0, type=int, help='Only tags, which have a family size smaller or equal than specified, are included in the HD analysis') - parser.add_argument('--nr_above_bars', action="store_true", # default=False, type=bool, + parser.add_argument('--nr_above_bars', action="store_true", help='If no, values above bars in the histrograms are removed') - - parser.add_argument('--output_csv', default="data.csv", type=str, - help='Name of the csv file.') + + parser.add_argument('--output_tabular', default="data.tabular", type=str, + help='Name of the tabular file.') parser.add_argument('--output_pdf', default="data.pdf", type=str, help='Name of the pdf file.') parser.add_argument('--output_pdf2', default="data2.pdf", type=str, help='Name of the pdf file.') - parser.add_argument('--output_csv2', default="data2.csv", type=str, - help='Name of the csv file.') + parser.add_argument('--output_tabular2', default="data2.tabular", type=str, + help='Name of the tabular file.') return parser + def Hamming_Distance_Analysis(argv): parser = make_argparser() args = parser.parse_args(argv[1:]) @@ -673,20 +679,19 @@ title_savedFile_pdf = args.output_pdf title_savedFile_pdf2 = args.output_pdf2 - title_savedFile_csv = args.output_csv - title_savedFile_csv2 = args.output_csv2 + title_savedFile_csv = args.output_tabular + title_savedFile_csv2 = args.output_tabular2 - sep = args.sep + sep = "\t" onlyDuplicates = args.only_DCS minFS = args.minFS maxFS = args.maxFS nr_above_bars = args.nr_above_bars - subset = args.subset_tag nproc = args.nproc - ### input checks + # input checks if index_size < 0: print("index_size is a negative integer.") exit(2) @@ -695,15 +700,11 @@ print("nproc is smaller or equal zero") exit(3) - if type(sep) is not str or len(sep)>1: - print("sep must be a single character.") - exit(4) - if subset < 0: print("subset_tag is smaller or equal zero.") exit(5) - ### PLOT ### + # PLOT plt.rcParams['axes.facecolor'] = "E0E0E0" # grey background color plt.rcParams['xtick.labelsize'] = 14 plt.rcParams['ytick.labelsize'] = 14 @@ -763,7 +764,7 @@ integers = numpy.array(data_array[:, 0]).astype(int) print("DCS in whole dataset", len(data_array)) - ## HD analysis for a subset of the tag + # HD analysis for a subset of the tag if subset > 0: tag1 = numpy.array([i[0:(len(i)) / 2] for i in data_array[:, 1]]) tag2 = numpy.array([i[len(i) / 2:len(i)] for i in data_array[:, 1]]) @@ -789,11 +790,10 @@ if index_size == 0: result = numpy.arange(0, len(data_array), 1) else: - result = numpy.random.choice(len(integers), size=index_size, - replace=False) # array of random sequences of size=index.size + result = numpy.random.choice(len(integers), size=index_size, replace=False) # array of random sequences of size=index.size - # with open("index_result1_{}.pkl".format(app_f), "wb") as o: - # pickle.dump(result, o, pickle.HIGHEST_PROTOCOL) + # with open("index_result1_{}.pkl".format(app_f), "wb") as o: + # pickle.dump(result, o, pickle.HIGHEST_PROTOCOL) # comparison random tags to whole dataset result1 = data_array[result, 1] # random tags @@ -808,9 +808,9 @@ proc_pool.close() proc_pool.join() ham = numpy.concatenate(ham).astype(int) - # with open("HD_whole dataset_{}.txt".format(app_f), "w") as output_file1: - # for h, tag in zip(ham, result1): - # output_file1.write("{}\t{}\n".format(tag, h)) + # with open("HD_whole dataset_{}.txt".format(app_f), "w") as output_file1: + # for h, tag in zip(ham, result1): + # output_file1.write("{}\t{}\n".format(tag, h)) # HD analysis for chimeric reads proc_pool_b = Pool(nproc) @@ -834,14 +834,9 @@ numpy.concatenate([item_b[6] for item_b in diff_list_b]))).astype(int) minHD_tags_zeros = numpy.concatenate((numpy.concatenate([item[7] for item in diff_list_a]), numpy.concatenate([item_b[7] for item_b in diff_list_b]))) - HDhalf1min = numpy.concatenate((numpy.concatenate([item[8] for item in diff_list_a]), - numpy.concatenate([item_b[8] for item_b in diff_list_b]))).astype(int) + HDhalf1min = numpy.concatenate((numpy.concatenate([item[8] for item in diff_list_a]), numpy.concatenate([item_b[8] for item_b in diff_list_b]))).astype(int) HDhalf2min = numpy.concatenate((numpy.concatenate([item[9] for item in diff_list_a]), numpy.concatenate([item_b[9] for item_b in diff_list_b]))).astype(int) - # with open("HD_within tag_{}.txt".format(app_f), "w") as output_file2: - # for d, s1, s2, hd, rel_d, tag in zip(diff, HDhalf1, HDhalf2, minHDs, rel_Diff, minHD_tags): - # output_file2.write( - # "{}\t{}\t{}\t{}\t{}\t{}\n".format(tag, hd, s1, s2, d, rel_d)) lenTags = len(data_array) @@ -856,10 +851,10 @@ # prepare data for different kinds of plots # distribution of FSs separated after HD - familySizeList1, hammingDistances, maximumXFS, minimumXFS = familySizeDistributionWithHD(quant, ham,rel=False) + familySizeList1, hammingDistances, maximumXFS, minimumXFS = familySizeDistributionWithHD(quant, ham, rel=False) list1, maximumX, minimumX = hammingDistanceWithFS(quant, ham) # histogram of HDs separated after FS - - ## get FS for all tags with min HD of analysis of chimeric reads + + # get FS for all tags with min HD of analysis of chimeric reads # there are more tags than sample size in the plot, because one tag can have multiple minimas seqDic = dict(zip(seq, quant)) lst_minHD_tags = [] @@ -883,34 +878,23 @@ for i in minHD_tags_zeros: lst_minHD_tags_zeros.append(seqDic.get(i)) # get family size for tags of chimeric reads - # histogram with HD of non-identical half - listDifference1_zeros, maximumXDifference_zeros, minimumXDifference_zeros = hammingDistanceWithFS( - lst_minHD_tags_zeros, diff_zeros) - # family size distribution of non-identical half - familySizeList1_diff_zeros, hammingDistances_diff_zeros, maximumXFS_diff_zeros, minimumXFS_diff_zeros = familySizeDistributionWithHD( - lst_minHD_tags_zeros, diff_zeros, diff=False, rel=False) - - ##################################################################################################################### - ################## plot Hamming Distance with Family size distribution ############################## - ##################################################################################################################### - plotHDwithFSD(list1=list1, maximumX=maximumX, minimumX=minimumX, pdf=pdf, - subtitle="Hamming distance separated by family size", title_file1=name_file, - lenTags=lenTags,xlabel="HD", nr_above_bars=nr_above_bars) + # histogram with HD of non-identical half + listDifference1_zeros, maximumXDifference_zeros, minimumXDifference_zeros = hammingDistanceWithFS(lst_minHD_tags_zeros, diff_zeros) + # family size distribution of non-identical half + familySizeList1_diff_zeros, hammingDistances_diff_zeros, maximumXFS_diff_zeros, minimumXFS_diff_zeros = familySizeDistributionWithHD(lst_minHD_tags_zeros, diff_zeros, diff=False, rel=False) - ########################## Plot FSD with separation after ############################################### - ###################################################################################################################### + # plot Hamming Distance with Family size distribution + plotHDwithFSD(list1=list1, maximumX=maximumX, minimumX=minimumX, pdf=pdf, subtitle="Hamming distance separated by family size", title_file1=name_file, lenTags=lenTags, xlabel="HD", nr_above_bars=nr_above_bars) + + # Plot FSD with separation after plotFSDwithHD2(familySizeList1, maximumXFS, minimumXFS, originalCounts=quant, subtitle="Family size distribution separated by Hamming distance", - pdf=pdf,relative=False, title_file1=name_file, diff=False) + pdf=pdf, relative=False, title_file1=name_file, diff=False) - ########################## Plot HD within tags ######################################################## - ###################################################################################################################### - # plotHDwithinSeq_Sum2(HDhalf1, HDhalf2, minHDs, pdf=pdf, lenTags=lenTags, title_file1=name_file) - plotHDwithinSeq_Sum2(HDhalf1, HDhalf1min, HDhalf2min , HDhalf2, minHDs, pdf=pdf, lenTags=lenTags, title_file1=name_file) - - - ########################## Plot difference between HD's separated after FSD #################################### - ###################################################################################################################### + # Plot HD within tags + plotHDwithinSeq_Sum2(HDhalf1, HDhalf1min, HDhalf2, HDhalf2min, minHDs, pdf=pdf, lenTags=lenTags, title_file1=name_file) + + # Plot difference between HD's separated after FSD plotHDwithFSD(listDifference1, maximumXDifference, minimumXDifference, pdf=pdf, subtitle="Delta Hamming distance within tags", title_file1=name_file, lenTags=lenTags, @@ -921,44 +905,27 @@ title_file1=name_file, lenTags=lenTags, xlabel="relative delta HD", relative=True, nr_above_bars=nr_above_bars) - #################### Plot FSD separated after difference between HD's ##################################### - ######################################################################################################################## - # plotFSDwithHD2(familySizeList1_diff, maximumXFS_diff, minimumXFS_diff, - # subtitle="Family size distribution separated by delta Hamming distances within the tags", - # pdf=pdf,relative=False, diff=True, title_file1=name_file, originalCounts=quant) - - # plotFSDwithHD2(familySizeList1_reldiff, maximumXFS_reldiff, minimumXFS_reldiff, originalCounts=quant, pdf=pdf, - # subtitle="Family size distribution separated by delta Hamming distances within the tags", -# relative=True, diff=True, title_file1=name_file) - - # plots for chimeric reads if len(minHD_tags_zeros) != 0: - ## HD + # HD plotHDwithFSD(listDifference1_zeros, maximumXDifference_zeros, minimumXDifference_zeros, pdf=pdf, subtitle="Hamming distance of the non-identical half of chimeras", - title_file1=name_file, lenTags=lenTags,xlabel="HD", relative=False, nr_above_bars=nr_above_bars) + title_file1=name_file, lenTags=lenTags, xlabel="HD", relative=False, nr_above_bars=nr_above_bars) - ## FSD - # plotFSDwithHD2(familySizeList1_diff_zeros, maximumXFS_diff_zeros, minimumXFS_diff_zeros, - # originalCounts=quant, pdf=pdf, - # subtitle="Family size distribution separated by Hamming distance of the non-identical half of chimeras", - # relative=False, diff=False, title_file1=name_file) - - ### print all data to a CSV file - #### HD #### + # print all data to a CSV file + # HD summary, sumCol = createTableHD(list1, "HD=") overallSum = sum(sumCol) # sum of columns in table - #### FSD #### + # FSD summary5, sumCol5 = createTableFSD2(familySizeList1, diff=False) overallSum5 = sum(sumCol5) - ### HD of both parts of the tag #### - summary9, sumCol9 = createTableHDwithTags([HDhalf1, HDhalf2,numpy.array(minHDs)]) + # HD of both parts of the tag + summary9, sumCol9 = createTableHDwithTags([HDhalf1, HDhalf1min, HDhalf2, HDhalf2min, numpy.array(minHDs)]) overallSum9 = sum(sumCol9) - ## HD + # HD # absolute difference summary11, sumCol11 = createTableHD(listDifference1, "diff=") overallSum11 = sum(sumCol11) @@ -966,44 +933,33 @@ summary13, sumCol13 = createTableHD(listRelDifference1, "diff=") overallSum13 = sum(sumCol13) - ## FSD - # absolute difference - # summary19, sumCol19 = createTableFSD2(familySizeList1_diff) - # overallSum19 = sum(sumCol19) - # relative difference - # summary21, sumCol21 = createTableFSD2(familySizeList1_reldiff) - # overallSum21 = sum(sumCol21) - # chimeric reads if len(minHD_tags_zeros) != 0: # absolute difference and tags where at least one half has HD=0 summary15, sumCol15 = createTableHD(listDifference1_zeros, "HD=") overallSum15 = sum(sumCol15) - # absolute difference and tags where at least one half has HD=0 - # summary23, sumCol23 = createTableFSD2(familySizeList1_diff_zeros, diff=False) - # overallSum23 = sum(sumCol23) output_file.write("{}\n".format(name_file)) output_file.write("number of tags per file{}{:,} (from {:,}) against {:,}\n\n".format(sep, len( numpy.concatenate(list1)), lenTags, lenTags)) - ### HD ### + # HD createFileHD(summary, sumCol, overallSum, output_file, "Hamming distance separated by family size", sep) - ### FSD ### + # FSD createFileFSD2(summary5, sumCol5, overallSum5, output_file, "Family size distribution separated by Hamming distance", sep, diff=False) count = numpy.bincount(quant) - #output_file.write("{}{}\n".format(sep, name_file)) + # output_file.write("{}{}\n".format(sep, name_file)) output_file.write("\n") output_file.write("max. family size:{}{}\n".format(sep, max(quant))) output_file.write("absolute frequency:{}{}\n".format(sep, count[len(count) - 1])) output_file.write( "relative frequency:{}{}\n\n".format(sep, float(count[len(count) - 1]) / sum(count))) - ### HD within tags ### + # HD within tags output_file.write( "The hamming distances were calculated by comparing each half of all tags against the tag(s) with the minimum Hamming distance per half.\n" "It is possible that one tag can have the minimum HD from multiple tags, so the sample size in this calculation differs from the sample size entered by the user.\n") @@ -1019,24 +975,13 @@ createFileHD(summary13, sumCol13, overallSum13, output_file, "Chimera analysis: relative delta Hamming distances", sep) - # createFileFSD2(summary19, sumCol19, overallSum19, output_file, - # "Family size distribution separated by absolute delta Hamming distance", - # sep) - # createFileFSD2(summary21, sumCol21, overallSum21, output_file, - # "Family size distribution separated by relative delta Hamming distance", - # sep, rel=True) - if len(minHD_tags_zeros) != 0: output_file.write( "Chimeras:\nAll tags were filtered: only those tags where at least one half is identical with the half of the min. tag are kept.\nSo the hamming distance of the non-identical half is compared.\n") createFileHD(summary15, sumCol15, overallSum15, output_file, "Hamming distances of non-zero half", sep) - # createFileFSD2(summary23, sumCol23, overallSum23, output_file, - # "Family size distribution separated by Hamming distance of non-zero half", - # sep, diff=False) output_file.write("\n") - if __name__ == '__main__': sys.exit(Hamming_Distance_Analysis(sys.argv))