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annotate fsd_regions.py @ 6:26014c24323a draft

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planemo upload for repository https://github.com/monikaheinzl/duplexanalysis_galaxy/tree/master/tools/fsd_regions commit 8833d1a8a49d7b6d4a9c849b0335d3260564b351-dirty
author mheinzl
date Fri, 26 Oct 2018 07:54:03 -0400
parents 52454637bc45
children 3b8a0e462021
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1 #!/usr/bin/env python
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3 # Family size distribution of tags which were aligned to the reference genome
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4 #
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5 # Author: Monika Heinzl, Johannes-Kepler University Linz (Austria)
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6 # Contact: monika.heinzl@edumail.at
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7 #
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8 # Takes at least one TABULAR file with tags before the alignment to the SSCS
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9 # and a TXT with tags of reads that overlap the regions of the reference genome as input.
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10 # The program produces a plot which shows the distribution of family sizes of the tags from the input files and
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11 # a tabular file with the data of the plot.
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13 # USAGE: python FSD_regions_1.6_FINAL.py --inputFile filenameSSCS --inputName1 filenameSSCS --ref_genome filenameRefGenome --output_tabular outptufile_name_tabular --output_pdf outptufile_name_pdf
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15 import argparse
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16 import re
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17 import sys
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18 from collections import OrderedDict
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20 import matplotlib.pyplot as plt
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21 import numpy
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22 from matplotlib.backends.backend_pdf import PdfPages
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24 plt.switch_backend('agg')
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27 def readFileReferenceFree(file, delim):
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28 with open(file, 'r') as dest_f:
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29 data_array = numpy.genfromtxt(dest_f, skip_header=0, delimiter=delim, comments='#', dtype='string')
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30 return(data_array)
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33 def make_argparser():
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34 parser = argparse.ArgumentParser(description='Family Size Distribution of tags which were aligned to regions of the reference genome')
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35 parser.add_argument('--inputFile', help='Tabular File with three columns: ab or ba, tag and family size.')
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36 parser.add_argument('--inputName1')
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37 parser.add_argument('--ref_genome', help='TXT File with tags of reads that overlap the region.')
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38 parser.add_argument('--output_pdf', default="data.pdf", type=str, help='Name of the pdf and tabular file.')
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39 parser.add_argument('--output_tabular', default="data.tabular", type=str, help='Name of the pdf and tabular file.')
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40 return parser
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43 def compare_read_families_refGenome(argv):
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44 parser = make_argparser()
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45 args = parser.parse_args(argv[1:])
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47 firstFile = args.inputFile
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48 name1 = args.inputName1
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49 name1 = name1.split(".tabular")[0]
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50 refGenome = args.ref_genome
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51 title_file = args.output_pdf
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52 title_file2 = args.output_tabular
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53 sep = "\t"
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55 with open(title_file2, "w") as output_file, PdfPages(title_file) as pdf:
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56 data_array = readFileReferenceFree(firstFile, "\t")
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58 mut_array = readFileReferenceFree(refGenome, " ")
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59 group = numpy.array(mut_array[:, 0])
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60 seq_mut = numpy.array(mut_array[:, 1])
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62 seq = numpy.array(data_array[:, 1])
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63 tags = numpy.array(data_array[:, 2])
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64 quant = numpy.array(data_array[:, 0]).astype(int)
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66 all_ab = seq[numpy.where(tags == "ab")[0]]
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67 all_ba = seq[numpy.where(tags == "ba")[0]]
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68 quant_ab = quant[numpy.where(tags == "ab")[0]]
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69 quant_ba = quant[numpy.where(tags == "ba")[0]]
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71 seqDic_ab = dict(zip(all_ab, quant_ab))
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72 seqDic_ba = dict(zip(all_ba, quant_ba))
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74 if re.search('_(\d)+_(\d)+$', str(mut_array[0,0])) is None:
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75 seq_mut, seqMut_index = numpy.unique(numpy.array(mut_array[:, 1]), return_index=True)
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76 group = mut_array[seqMut_index,0]
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77 mut_array = mut_array[seqMut_index,:]
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78 length_regions = len(seq_mut)*2
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80 groupUnique, group_index = numpy.unique(group, return_index=True)
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81 groupUnique = groupUnique[numpy.argsort(group_index)]
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83 lst_ab = []
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84 lst_ba = []
0
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85 for i in seq_mut:
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86 lst_ab.append(seqDic_ab.get(i))
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87 lst_ba.append(seqDic_ba.get(i))
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88
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89 quant_ab = numpy.array(lst_ab)
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90 quant_ba = numpy.array(lst_ba)
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91
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92 quantAfterRegion = []
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93
0
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94 for i in groupUnique:
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95 dataAB = quant_ab[numpy.where(group == i)[0]]
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96 dataBA = quant_ba[numpy.where(group == i)[0]]
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97 bigFamilies = numpy.where(dataAB > 20)[0]
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98 dataAB[bigFamilies] = 22
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99 bigFamilies = numpy.where(dataBA > 20)[0]
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100 dataBA[bigFamilies] = 22
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101
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102 quantAll = numpy.concatenate((dataAB, dataBA))
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103 quantAfterRegion.append(quantAll)
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104
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105 maximumX = numpy.amax(numpy.concatenate(quantAfterRegion))
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106 minimumX = numpy.amin(numpy.concatenate(quantAfterRegion))
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107
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108 # PLOT
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109 plt.rc('figure', figsize=(11.69, 8.27)) # A4 format
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110 plt.rcParams['axes.facecolor'] = "E0E0E0" # grey background color
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111 plt.rcParams['xtick.labelsize'] = 14
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112 plt.rcParams['ytick.labelsize'] = 14
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113 plt.rcParams['patch.edgecolor'] = "black"
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114 fig = plt.figure()
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115 plt.subplots_adjust(bottom=0.3)
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116
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117 colors = ["#6E6E6E", "#0431B4", "#5FB404", "#B40431", "#F4FA58", "#DF7401", "#81DAF5"]
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118
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119 col = []
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120 for i in range(0, len(groupUnique)):
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121 col.append(colors[i])
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122
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123 counts = plt.hist(quantAfterRegion, bins=range(minimumX, maximumX + 1), stacked=False, label=groupUnique,
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124 align="left", alpha=1, color=col, edgecolor="black", linewidth=1)
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125 ticks = numpy.arange(minimumX - 1, maximumX, 1)
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126
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127 ticks1 = map(str, ticks)
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128 ticks1[len(ticks1) - 1] = ">20"
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129 plt.xticks(numpy.array(ticks), ticks1)
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130 count = numpy.bincount(map(int, quant_ab)) # original counts
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131
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132 legend = "max. family size =\nabsolute frequency=\nrelative frequency=\n\ntotal nr. of reads="
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133 plt.text(0.15, 0.105, legend, size=11, transform=plt.gcf().transFigure)
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134
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135 legend = "AB\n{}\n{}\n{:.5f}\n\n{:,}" \
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136 .format(max(map(int, quant_ab)), count[len(count) - 1], float(count[len(count) - 1]) / sum(count),
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137 sum(numpy.array(data_array[:, 0]).astype(int)))
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138 plt.text(0.35, 0.105, legend, size=11, transform=plt.gcf().transFigure)
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139
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140 count2 = numpy.bincount(map(int, quant_ba)) # original counts
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141
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142 legend = "BA\n{}\n{}\n{:.5f}" \
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143 .format(max(map(int, quant_ba)), count2[len(count2) - 1], float(count2[len(count2) - 1]) / sum(count2))
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144 plt.text(0.45, 0.15, legend, size=11, transform=plt.gcf().transFigure)
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145
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146 plt.text(0.55, 0.22, "total nr. of tags=", size=11, transform=plt.gcf().transFigure)
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147 plt.text(0.75, 0.22, "{:,} ({:,})".format(length_regions, length_regions/2), size=11, transform=plt.gcf().transFigure)
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148
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149 # legend4 = '* The total numbers indicate the count of the ab and ba tags per region.\nAn equal sign ("=") is used in the column ba tags, if the counts and the region are identical to the ab tags.'
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150 # plt.text(0.1, 0.02, legend4, size=11, transform=plt.gcf().transFigure)
0
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151
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152 plt.text(0.75, 0.18, "total nr. of tags per region", size=11, transform=plt.gcf().transFigure)
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153 #space = numpy.arange(0, len(groupUnique), 0.02)
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154 s = 0
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155 index_array = 0
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156 for i, count in zip(groupUnique, quantAfterRegion):
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157 index_of_current_region = numpy.where(group == i)[0]
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158 plt.text(0.55, 0.14 - s, "{}=\n".format(i), size=11, transform=plt.gcf().transFigure)
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159 if re.search('_(\d)+_(\d)+$', str(mut_array[0, 0])) is None:
5
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160 nr_tags_ab = len(numpy.unique(mut_array[index_of_current_region, 1]))
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161 else:
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162 nr_tags_ab = len(mut_array[index_of_current_region, 1])
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163 plt.text(0.75, 0.14 - s, "{:,}\n".format(nr_tags_ab), size=11, transform=plt.gcf().transFigure)
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164 s = s + 0.02
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165
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166 plt.legend(loc='upper right', fontsize=14, bbox_to_anchor=(0.9, 1), frameon=True)
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167 plt.xlabel("Family size", fontsize=14)
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168 plt.ylabel("Absolute Frequency", fontsize=14)
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169 plt.grid(b=True, which="major", color="#424242", linestyle=":")
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170 plt.margins(0.01, None)
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171
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172 pdf.savefig(fig, bbox_inch="tight")
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173 plt.close()
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174
1
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175 output_file.write("Dataset:{}{}\n".format(sep, name1))
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176 output_file.write("{}AB{}BA\n".format(sep, sep))
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177 output_file.write("max. family size:{}{}{}{}\n".format(sep, max(map(int, quant_ab)), sep, max(map(int, quant_ba))))
b82fdb006304 planemo upload for repository https://github.com/monikaheinzl/galaxyProject/tree/master/tools/fsd_regions commit 6055f8c5c052f528ff85fb5e0d43b4500830637a
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178 output_file.write("absolute frequency:{}{}{}{}\n".format(sep, count[len(count) - 1], sep, count2[len(count2) - 1]))
b82fdb006304 planemo upload for repository https://github.com/monikaheinzl/galaxyProject/tree/master/tools/fsd_regions commit 6055f8c5c052f528ff85fb5e0d43b4500830637a
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179 output_file.write("relative frequency:{}{:.3f}{}{:.3f}\n\n".format(sep, float(count[len(count) - 1]) / sum(count), sep, float(count2[len(count2) - 1]) / sum(count2)))
b82fdb006304 planemo upload for repository https://github.com/monikaheinzl/galaxyProject/tree/master/tools/fsd_regions commit 6055f8c5c052f528ff85fb5e0d43b4500830637a
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180 output_file.write("total nr. of reads{}{}\n".format(sep, sum(numpy.array(data_array[:, 0]).astype(int))))
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181 output_file.write("total nr. of tags{}{} ({})\n".format(sep, length_regions, length_regions/2))
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182
0
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183 output_file.write("\n\nValues from family size distribution\n")
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184 output_file.write("{}".format(sep))
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185 for i in groupUnique:
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186 output_file.write("{}{}".format(i, sep))
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187 output_file.write("\n")
4
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188 j = 0
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189 for fs in counts[1][0:len(counts[1]) - 1]:
0
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190 if fs == 21:
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191 fs = ">20"
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192 else:
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193 fs = "={}".format(fs)
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194 output_file.write("FS{}{}".format(fs, sep))
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195
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196 if len(groupUnique) == 1:
52454637bc45 planemo upload for repository https://github.com/monikaheinzl/duplexanalysis_galaxy/tree/master/tools/fsd_regions commit 8833d1a8a49d7b6d4a9c849b0335d3260564b351-dirty
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197 output_file.write("{}{}".format(int(counts[0][j]), sep))
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198 else:
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199 for n in range(len(groupUnique)):
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200 output_file.write("{}{}".format(int(counts[0][n][j]), sep))
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201
0
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202 output_file.write("\n")
4
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203 j += 1
0
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204 output_file.write("sum{}".format(sep))
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205 if len(groupUnique) == 1:
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206 output_file.write("{}{}".format(int(sum(counts[0])), sep))
52454637bc45 planemo upload for repository https://github.com/monikaheinzl/duplexanalysis_galaxy/tree/master/tools/fsd_regions commit 8833d1a8a49d7b6d4a9c849b0335d3260564b351-dirty
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207 else:
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208 for i in counts[0]:
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209 output_file.write("{}{}".format(int(sum(i)), sep))
0
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210 output_file.write("\n")
6
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211 output_file.write("\n\nIn the plot, both family sizes of the ab and ba strands were used.\nWhereas the total numbers indicate only the count of the tags per region.\n")
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212 output_file.write("\n\nRegion{}total nr. of tags per region\n".format(sep, sep))
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213
6
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214 for i, count in zip(groupUnique, quantAfterRegion):
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215 output_file.write("{}{}{}\n".format(i,sep,len(count) / 2))
0
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216 print("Files successfully created!")
4
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217
0
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218
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219 if __name__ == '__main__':
5
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220 sys.exit(compare_read_families_refGenome(sys.argv))