annotate signature.py @ 0:a35e6f9c1d34 draft

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
author artbio
date Mon, 28 Aug 2017 09:29:47 -0400
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a35e6f9c1d34 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
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1 import argparse
a35e6f9c1d34 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
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2 from collections import defaultdict
a35e6f9c1d34 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
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4 import numpy
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6 import pysam
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9 def Parser():
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10 the_parser = argparse.ArgumentParser()
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11 the_parser.add_argument(
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12 '--input', action="store", type=str, help="bam alignment file")
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13 the_parser.add_argument(
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14 '--minquery', type=int,
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15 help="Minimum readsize of query reads (nt) - must be an integer")
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16 the_parser.add_argument(
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17 '--maxquery', type=int,
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18 help="Maximum readsize of query reads (nt) - must be an integer")
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19 the_parser.add_argument(
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20 '--mintarget', type=int,
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21 help="Minimum readsize of target reads (nt) - must be an integer")
a35e6f9c1d34 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
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22 the_parser.add_argument(
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23 '--maxtarget', type=int,
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24 help="Maximum readsize of target reads (nt) - must be an integer")
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25 the_parser.add_argument(
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26 '--minscope', type=int,
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27 help="Minimum overlap analyzed (nt) - must be an integer")
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28 the_parser.add_argument(
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29 '--maxscope', type=int,
a35e6f9c1d34 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
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30 help="Maximum overlap analyzed (nt) - must be an integer")
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31 the_parser.add_argument(
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32 '--output_h', action="store", type=str,
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33 help="h-signature dataframe")
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34 the_parser.add_argument(
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35 '--output_z', action="store", type=str,
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36 help="z-signature dataframe")
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37 args = the_parser.parse_args()
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38 return args
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39
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40
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41 class Map:
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42
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43 def __init__(self, bam_file):
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44 self.bam_object = pysam.AlignmentFile(bam_file, 'rb')
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45 self.chromosomes = dict(zip(self.bam_object.references,
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46 self.bam_object.lengths))
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47 self.map_dict = self.create_map(self.bam_object)
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48
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49 def create_map(self, bam_object):
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50 '''
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51 Returns a map_dictionary {(chromosome,read_position,polarity):
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52 [read_length, ...]}
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53 '''
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54 map_dictionary = defaultdict(list)
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55 # get empty value for start and end of each chromosome
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56 for chrom in self.chromosomes:
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57 map_dictionary[(chrom, 1, 'F')] = []
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58 map_dictionary[(chrom, self.chromosomes[chrom], 'F')] = []
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59 for chrom in self.chromosomes:
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60 for read in bam_object.fetch(chrom):
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61 positions = read.positions # a list of covered positions
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62 if read.is_reverse:
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63 map_dictionary[(chrom, positions[-1]+1,
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64 'R')].append(read.query_alignment_length)
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65 else:
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66 map_dictionary[(chrom, positions[0]+1,
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67 'F')].append(read.query_alignment_length)
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68 return map_dictionary
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69
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70 def signature_tables(self, minquery, maxquery, mintarget, maxtarget):
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71 query_range = range(minquery, maxquery + 1)
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72 target_range = range(mintarget, maxtarget + 1)
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73 Query_table = defaultdict(dict)
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74 Target_table = defaultdict(dict)
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75 for key in self.map_dict:
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76 for size in self.map_dict[key]:
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77 if size in query_range or size in target_range:
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78 if key[2] == 'F':
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79 coordinate = key[1]
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80 else:
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81 coordinate = -key[1]
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82 if size in query_range:
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83 Query_table[key[0]][coordinate] = Query_table[key[0]].get(
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84 coordinate, 0) + 1
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85 if size in target_range:
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86 Target_table[key[0]][coordinate] = \
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artbio
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diff changeset
87 Target_table[key[0]].get(coordinate, 0) + 1
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artbio
parents:
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88 return Query_table, Target_table
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artbio
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diff changeset
89
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artbio
parents:
diff changeset
90 def compute_signature_z(self, minquery, maxquery, mintarget, maxtarget,
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artbio
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91 scope, zscore="no"):
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artbio
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92 Query_table, Target_table = self.signature_tables(minquery, maxquery,
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artbio
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93 mintarget, maxtarget)
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artbio
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94 frequency_table = defaultdict(dict)
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artbio
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95 for chrom in self.chromosomes:
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artbio
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diff changeset
96 for overlap in scope:
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artbio
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97 frequency_table[chrom][overlap] = 0
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artbio
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98 for chrom in Query_table:
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artbio
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99 for coord in Query_table[chrom]:
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artbio
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100 for overlap in scope:
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artbio
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101 frequency_table[chrom][overlap] += min(
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102 Query_table[chrom][coord],
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artbio
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103 Target_table[chrom].get(-coord - overlap + 1, 0))
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104 # since we want the number of pairs, not the number or paired reads
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105 # to do: what in complex cases
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106 # with query and target sizes partially overlap ?
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107 for chrom in frequency_table:
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108 for overlap in frequency_table[chrom]:
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109 frequency_table[chrom][overlap] /= 2
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110 # compute overlaps for all chromosomes merged
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111 for overlap in scope:
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112 accumulator = []
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113 for chrom in frequency_table:
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114 if chrom != 'all_chromosomes':
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115 accumulator.append(frequency_table[chrom][overlap])
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artbio
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116 frequency_table['all_chromosomes'][overlap] = sum(accumulator)
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117 return self.stringify_table(frequency_table)
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118
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artbio
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119 def compute_signature_h(self, minquery, maxquery, mintarget,
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120 maxtarget, scope):
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121 Query_table, Target_table = self.signature_tables(minquery, maxquery,
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122 mintarget, maxtarget)
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123 frequency_table = defaultdict(dict)
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124 for chrom in self.chromosomes:
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125 for overlap in scope:
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126 frequency_table[chrom][overlap] = 0
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127 for chrom in Query_table:
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128 Total_Query_Numb = 0
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129 for coord in Query_table[chrom]:
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artbio
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130 Total_Query_Numb += Query_table[chrom][coord]
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131 for coord in Query_table[chrom]:
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artbio
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132 local_table = dict([(overlap, 0) for overlap in scope])
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133 number_of_targets = 0
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134 for overlap in scope:
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diff changeset
135 local_table[overlap] += Query_table[chrom][coord] * \
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136 Target_table[chrom].get(-coord - overlap + 1, 0)
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137 number_of_targets += Target_table[chrom].get(
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138 -coord - overlap + 1, 0)
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139 for overlap in scope:
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140 try:
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141 frequency_table[chrom][overlap] += \
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142 local_table[overlap] / number_of_targets \
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143 / float(Total_Query_Numb)
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144 except ZeroDivisionError:
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145 continue
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146 # compute overlap probabilities for all chromosomes merged
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147 general_frequency_table = dict([(overlap, 0) for overlap in scope])
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148 total_aligned_reads = 0
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149 for chrom in frequency_table:
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diff changeset
150 for overlap in frequency_table[chrom]:
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151 total_aligned_reads += self.bam_object.count(chrom)
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152 for chrom in frequency_table:
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diff changeset
153 for overlap in frequency_table[chrom]:
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diff changeset
154 try:
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155 general_frequency_table[overlap] += \
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156 frequency_table[chrom][overlap] / total_aligned_reads \
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157 * self.bam_object.count(chrom)
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diff changeset
158 except ZeroDivisionError:
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diff changeset
159 continue
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artbio
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diff changeset
160 for overlap in general_frequency_table:
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diff changeset
161 frequency_table['all_chromosomes'][overlap] = \
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diff changeset
162 general_frequency_table[overlap]
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163 return self.stringify_table(frequency_table)
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diff changeset
164
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165 def stringify_table(self, frequency_table):
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166 '''
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diff changeset
167 method both to compute z-score and to return a writable string
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168 '''
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diff changeset
169 tablestring = []
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170 for chrom in sorted(frequency_table):
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diff changeset
171 accumulator = []
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artbio
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diff changeset
172 for overlap in frequency_table[chrom]:
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173 accumulator.append(frequency_table[chrom][overlap])
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174 z_mean = numpy.mean(accumulator)
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175 z_std = numpy.std(accumulator)
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176 if z_std == 0:
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177 for overlap in sorted(frequency_table[chrom]):
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178 tablestring.append('%s\t%s\t%s\t%s\n' % (
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179 chrom, str(overlap),
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180 str(frequency_table[chrom][overlap]), str(0)))
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181 else:
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182 for overlap in sorted(frequency_table[chrom]):
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183 tablestring.append('%s\t%s\t%s\t%s\n' % (
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184 chrom, str(overlap),
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185 str(frequency_table[chrom][overlap]),
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186 str((frequency_table[chrom][overlap] - z_mean)/z_std)))
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187 return ''.join(tablestring)
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188
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189
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190
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191 def main(input, minquery, maxquery, mintarget, maxtarget, minscope, maxscope,
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192 output_h, output_z, genome_wide=False, zscore="no"):
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193 H = open(output_h, 'w')
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194 Z = open(output_z, 'w')
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195 mapobj = Map(input)
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196 scope = range(minscope, maxscope + 1)
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197 Z.write(mapobj.compute_signature_z(minquery, maxquery, mintarget,
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198 maxtarget, scope, zscore="no"))
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199 H.write(mapobj.compute_signature_h(minquery, maxquery, mintarget,
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200 maxtarget, scope))
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201 H.close()
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202 Z.close()
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203
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204
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205 if __name__ == "__main__":
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206 args = Parser()
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207 main(args.input, args.minquery, args.maxquery, args.mintarget,
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208 args.maxtarget, args.minscope, args.maxscope, args.output_h,
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209 args.output_z)