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0
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1 from __future__ import division
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2 import sys
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3 import pandas as pd
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4 import itertools as it
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5 import scipy.stats as st
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6 import collections
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7 import lxml.etree as ET
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8 import pickle as pk
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9 import math
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10 import os
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11 import argparse
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12 from svglib.svglib import svg2rlg
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13 from reportlab.graphics import renderPDF
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14
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16
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15 ########################## argparse ##########################################
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0
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16
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17 def process_args(args):
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18 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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19 description = 'process some value\'s'+
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20 ' genes to create a comparison\'s map.')
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48
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21 parser.add_argument('-cr', '--custom_rules',
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0
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22 type = str,
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48
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23 default = 'false',
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24 choices = ['true', 'false'],
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25 help = 'choose whether to use custom rules')
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26 parser.add_argument('-cc', '--custom_rule',
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27 type = str,
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48
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28 help='custom rules to use')
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29 parser.add_argument('-cm', '--custom_map',
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30 type = str,
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31 help='custom map to use')
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32 parser.add_argument('-n', '--none',
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33 type = str,
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34 default = 'true',
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35 choices = ['true', 'false'],
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36 help = 'compute Nan values')
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37 parser.add_argument('-pv' ,'--pValue',
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38 type = float,
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47
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39 default = 0.1,
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40 help = 'P-Value threshold (default: %(default)s)')
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41 parser.add_argument('-fc', '--fChange',
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42 type = float,
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43 default = 1.5,
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44 help = 'Fold-Change threshold (default: %(default)s)')
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45 parser.add_argument('-td', '--tool_dir',
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46 type = str,
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47 required = True,
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48 help = 'your tool directory')
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49 parser.add_argument('-op', '--option',
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50 type = str,
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47
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51 choices = ['datasets', 'dataset_class'],
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52 help='dataset or dataset and class')
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53 parser.add_argument('-ol', '--out_log',
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54 help = "Output log")
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55 parser.add_argument('-id', '--input_data',
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56 type = str,
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57 help = 'input dataset')
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58 parser.add_argument('-ic', '--input_class',
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59 type = str,
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60 help = 'sample group specification')
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16
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61 parser.add_argument('-gs', '--generate_svg',
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62 type = str,
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63 default = 'true',
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64 choices = ['true', 'false'],
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65 help = 'generate svg map')
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66 parser.add_argument('-gp', '--generate_pdf',
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67 type = str,
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68 default = 'true',
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69 choices = ['true', 'false'],
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70 help = 'generate pdf map')
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47
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71 parser.add_argument('-on', '--control',
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72 type = str)
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73 parser.add_argument('-co', '--comparison',
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74 type = str,
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75 default = '1vs1',
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76 choices = ['manyvsmany', 'onevsrest', 'onevsmany'])
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77 parser.add_argument('-ids', '--input_datas',
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16
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78 type = str,
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47
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79 nargs = '+',
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80 help = 'input datasets')
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81 parser.add_argument('-na', '--names',
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82 type = str,
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83 nargs = '+',
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84 help = 'input names')
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85
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86 args = parser.parse_args()
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87 return args
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88
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89 ########################### warning ###########################################
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90
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91 def warning(s):
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92 args = process_args(sys.argv)
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93 with open(args.out_log, 'a') as log:
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94 log.write(s)
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95
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96 ############################ dataset input ####################################
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97
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98 def read_dataset(data, name):
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99 try:
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100 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
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101 except pd.errors.EmptyDataError:
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102 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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103 if len(dataset.columns) < 2:
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104 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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105 return dataset
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106
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107 ############################ dataset name #####################################
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108
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109 def name_dataset(name_data, count):
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110 if str(name_data) == 'Dataset':
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111 return str(name_data) + '_' + str(count)
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112 else:
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113 return str(name_data)
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114
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115 ############################ load id e rules ##################################
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116
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117 def load_id_rules(reactions):
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118 ids, rules = [], []
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119 for key, value in reactions.items():
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120 ids.append(key)
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121 rules.append(value)
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122 return (ids, rules)
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123
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124 ############################ check_methods ####################################
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125
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126 def gene_type(l, name):
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127 if check_hgnc(l):
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128 return 'hugo_id'
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129 elif check_ensembl(l):
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130 return 'ensembl_gene_id'
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131 elif check_symbol(l):
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132 return 'symbol'
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133 elif check_entrez(l):
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134 return 'entrez_id'
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135 else:
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136 sys.exit('Execution aborted:\n' +
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137 'gene ID type in ' + name + ' not supported. Supported ID'+
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138 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n')
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139
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140 def check_hgnc(l):
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141 if len(l) > 5:
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142 if (l.upper()).startswith('HGNC:'):
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143 return l[5:].isdigit()
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144 else:
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145 return False
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146 else:
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147 return False
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148
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149 def check_ensembl(l):
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150 if len(l) == 15:
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151 if (l.upper()).startswith('ENS'):
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152 return l[4:].isdigit()
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153 else:
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154 return False
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155 else:
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156 return False
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157
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158 def check_symbol(l):
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159 if len(l) > 0:
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160 if l[0].isalpha() and l[1:].isalnum():
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161 return True
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162 else:
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163 return False
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164 else:
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165 return False
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166
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167 def check_entrez(l):
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168 if len(l) > 0:
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169 return l.isdigit()
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170 else:
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171 return False
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172
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173 def check_bool(b):
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174 if b == 'true':
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175 return True
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176 elif b == 'false':
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177 return False
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178
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179 ############################ resolve_methods ##################################
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180
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181 def replace_gene_value(l, d):
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182 tmp = []
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183 err = []
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184 while l:
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185 if isinstance(l[0], list):
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186 tmp_rules, tmp_err = replace_gene_value(l[0], d)
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187 tmp.append(tmp_rules)
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188 err.extend(tmp_err)
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189 else:
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190 value = replace_gene(l[0], d)
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191 tmp.append(value)
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192 if value == None:
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193 err.append(l[0])
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194 l = l[1:]
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195 return (tmp, err)
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196
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35
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197
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0
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198 def replace_gene(l, d):
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199 if l =='and' or l == 'or':
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200 return l
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201 else:
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202 value = d.get(l, None)
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203 if not(value == None or isinstance(value, (int, float))):
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204 sys.exit('Execution aborted: ' + value + ' value not valid\n')
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205 return value
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206
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207 def computes(val1, op, val2, cn):
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208 if val1 != None and val2 != None:
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209 if op == 'and':
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210 return min(val1, val2)
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211 else:
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212 return val1 + val2
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213 elif op == 'and':
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214 if cn is True:
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215 if val1 != None:
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216 return val1
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217 elif val2 != None:
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218 return val2
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219 else:
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220 return None
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221 else:
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222 return None
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223 else:
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224 if val1 != None:
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225 return val1
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226 elif val2 != None:
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227 return val2
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228 else:
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229 return None
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230
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231 def control(ris, l, cn):
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232 if len(l) == 1:
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233 if isinstance(l[0], (float, int)) or l[0] == None:
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234 return l[0]
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235 elif isinstance(l[0], list):
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236 return control(None, l[0], cn)
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237 else:
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238 return False
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239 elif len(l) > 2:
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240 return control_list(ris, l, cn)
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241 else:
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242 return False
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243
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244 def control_list(ris, l, cn):
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245 while l:
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246 if len(l) == 1:
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247 return False
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248 elif (isinstance(l[0], (float, int)) or
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249 l[0] == None) and l[1] in ['and', 'or']:
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250 if isinstance(l[2], (float, int)) or l[2] == None:
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251 ris = computes(l[0], l[1], l[2], cn)
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252 elif isinstance(l[2], list):
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253 tmp = control(None, l[2], cn)
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254 if tmp is False:
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255 return False
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256 else:
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257 ris = computes(l[0], l[1], tmp, cn)
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258 else:
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259 return False
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260 l = l[3:]
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261 elif l[0] in ['and', 'or']:
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262 if isinstance(l[1], (float, int)) or l[1] == None:
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263 ris = computes(ris, l[0], l[1], cn)
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264 elif isinstance(l[1], list):
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265 tmp = control(None,l[1], cn)
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266 if tmp is False:
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267 return False
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268 else:
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269 ris = computes(ris, l[0], tmp, cn)
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270 else:
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271 return False
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272 l = l[2:]
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273 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
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274 if isinstance(l[2], (float, int)) or l[2] == None:
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275 tmp = control(None, l[0], cn)
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276 if tmp is False:
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277 return False
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278 else:
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279 ris = computes(tmp, l[1], l[2], cn)
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280 elif isinstance(l[2], list):
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281 tmp = control(None, l[0], cn)
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282 tmp2 = control(None, l[2], cn)
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283 if tmp is False or tmp2 is False:
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284 return False
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285 else:
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286 ris = computes(tmp, l[1], tmp2, cn)
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287 else:
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288 return False
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289 l = l[3:]
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290 else:
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291 return False
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292 return ris
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293
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294 ############################ map_methods ######################################
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295
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296 def fold_change(avg1, avg2):
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297 if avg1 == 0 and avg2 == 0:
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298 return 0
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299 elif avg1 == 0:
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300 return '-INF'
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301 elif avg2 == 0:
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302 return 'INF'
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303 else:
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304 return math.log(avg1 / avg2, 2)
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305
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306 def fix_style(l, col, width, dash):
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307 tmp = l.split(';')
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308 flag_col = False
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309 flag_width = False
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310 flag_dash = False
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311 for i in range(len(tmp)):
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312 if tmp[i].startswith('stroke:'):
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313 tmp[i] = 'stroke:' + col
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314 flag_col = True
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315 if tmp[i].startswith('stroke-width:'):
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316 tmp[i] = 'stroke-width:' + width
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317 flag_width = True
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318 if tmp[i].startswith('stroke-dasharray:'):
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319 tmp[i] = 'stroke-dasharray:' + dash
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320 flag_dash = True
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321 if not flag_col:
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322 tmp.append('stroke:' + col)
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323 if not flag_width:
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324 tmp.append('stroke-width:' + width)
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325 if not flag_dash:
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326 tmp.append('stroke-dasharray:' + dash)
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327 return ';'.join(tmp)
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328
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329 def fix_map(d, core_map, threshold_P_V, threshold_F_C, max_F_C):
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330 maxT = 12
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331 minT = 2
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332 grey = '#BEBEBE'
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333 blue = '#0000FF'
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334 red = '#E41A1C'
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335 for el in core_map.iter():
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336 el_id = str(el.get('id'))
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337 if el_id.startswith('R_'):
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338 tmp = d.get(el_id[2:])
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339 if tmp != None:
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340 p_val = tmp[0]
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341 f_c = tmp[1]
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342 if p_val < threshold_P_V:
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343 if not isinstance(f_c, str):
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344 if abs(f_c) < math.log(threshold_F_C, 2):
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345 col = grey
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346 width = str(minT)
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347 else:
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348 if f_c < 0:
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349 col = blue
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350 elif f_c > 0:
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351 col = red
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352 width = str(max((abs(f_c) * maxT) / max_F_C, minT))
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353 else:
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354 if f_c == '-INF':
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355 col = blue
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356 elif f_c == 'INF':
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357 col = red
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358 width = str(maxT)
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359 dash = 'none'
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360 else:
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361 dash = '5,5'
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362 col = grey
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363 width = str(minT)
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364 el.set('style', fix_style(el.get('style'), col, width, dash))
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365 return core_map
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366
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367 ############################ make recon #######################################
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368
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369 def check_and_doWord(l):
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370 tmp = []
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371 tmp_genes = []
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372 count = 0
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373 while l:
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374 if count >= 0:
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375 if l[0] == '(':
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376 count += 1
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377 tmp.append(l[0])
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378 l.pop(0)
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379 elif l[0] == ')':
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380 count -= 1
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381 tmp.append(l[0])
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382 l.pop(0)
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383 elif l[0] == ' ':
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384 l.pop(0)
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385 else:
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386 word = []
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387 while l:
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388 if l[0] in [' ', '(', ')']:
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389 break
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390 else:
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391 word.append(l[0])
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392 l.pop(0)
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393 word = ''.join(word)
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394 tmp.append(word)
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395 if not(word in ['or', 'and']):
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396 tmp_genes.append(word)
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397 else:
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398 return False
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399 if count == 0:
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400 return (tmp, tmp_genes)
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401 else:
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402 return False
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403
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404 def brackets_to_list(l):
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405 tmp = []
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406 while l:
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407 if l[0] == '(':
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408 l.pop(0)
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409 tmp.append(resolve_brackets(l))
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410 else:
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411 tmp.append(l[0])
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412 l.pop(0)
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413 return tmp
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414
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415 def resolve_brackets(l):
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416 tmp = []
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417 while l[0] != ')':
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418 if l[0] == '(':
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419 l.pop(0)
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420 tmp.append(resolve_brackets(l))
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421 else:
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422 tmp.append(l[0])
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423 l.pop(0)
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424 l.pop(0)
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425 return tmp
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426
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427 def priorityAND(l):
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|
428 tmp = []
|
|
|
429 flag = True
|
|
|
430 while l:
|
|
|
431 if len(l) == 1:
|
|
|
432 if isinstance(l[0], list):
|
|
|
433 tmp.append(priorityAND(l[0]))
|
|
|
434 else:
|
|
|
435 tmp.append(l[0])
|
|
|
436 l = l[1:]
|
|
|
437 elif l[0] == 'or':
|
|
|
438 tmp.append(l[0])
|
|
|
439 flag = False
|
|
|
440 l = l[1:]
|
|
|
441 elif l[1] == 'or':
|
|
|
442 if isinstance(l[0], list):
|
|
|
443 tmp.append(priorityAND(l[0]))
|
|
|
444 else:
|
|
|
445 tmp.append(l[0])
|
|
|
446 tmp.append(l[1])
|
|
|
447 flag = False
|
|
|
448 l = l[2:]
|
|
|
449 elif l[1] == 'and':
|
|
|
450 tmpAnd = []
|
|
|
451 if isinstance(l[0], list):
|
|
|
452 tmpAnd.append(priorityAND(l[0]))
|
|
|
453 else:
|
|
|
454 tmpAnd.append(l[0])
|
|
|
455 tmpAnd.append(l[1])
|
|
|
456 if isinstance(l[2], list):
|
|
|
457 tmpAnd.append(priorityAND(l[2]))
|
|
|
458 else:
|
|
|
459 tmpAnd.append(l[2])
|
|
|
460 l = l[3:]
|
|
|
461 while l:
|
|
|
462 if l[0] == 'and':
|
|
|
463 tmpAnd.append(l[0])
|
|
|
464 if isinstance(l[1], list):
|
|
|
465 tmpAnd.append(priorityAND(l[1]))
|
|
|
466 else:
|
|
|
467 tmpAnd.append(l[1])
|
|
|
468 l = l[2:]
|
|
|
469 elif l[0] == 'or':
|
|
|
470 flag = False
|
|
|
471 break
|
|
13
|
472 if flag == True: #when there are only AND in list
|
|
0
|
473 tmp.extend(tmpAnd)
|
|
|
474 elif flag == False:
|
|
|
475 tmp.append(tmpAnd)
|
|
|
476 return tmp
|
|
|
477
|
|
|
478 def checkRule(l):
|
|
|
479 if len(l) == 1:
|
|
|
480 if isinstance(l[0], list):
|
|
|
481 if checkRule(l[0]) is False:
|
|
|
482 return False
|
|
|
483 elif len(l) > 2:
|
|
|
484 if checkRule2(l) is False:
|
|
|
485 return False
|
|
|
486 else:
|
|
|
487 return False
|
|
|
488 return True
|
|
|
489
|
|
|
490 def checkRule2(l):
|
|
|
491 while l:
|
|
|
492 if len(l) == 1:
|
|
|
493 return False
|
|
|
494 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
|
|
|
495 if checkRule(l[0]) is False:
|
|
|
496 return False
|
|
|
497 if isinstance(l[2], list):
|
|
|
498 if checkRule(l[2]) is False:
|
|
|
499 return False
|
|
|
500 l = l[3:]
|
|
|
501 elif l[1] in ['and', 'or']:
|
|
|
502 if isinstance(l[2], list):
|
|
|
503 if checkRule(l[2]) is False:
|
|
|
504 return False
|
|
|
505 l = l[3:]
|
|
|
506 elif l[0] in ['and', 'or']:
|
|
|
507 if isinstance(l[1], list):
|
|
|
508 if checkRule(l[1]) is False:
|
|
|
509 return False
|
|
|
510 l = l[2:]
|
|
|
511 else:
|
|
|
512 return False
|
|
|
513 return True
|
|
|
514
|
|
|
515 def do_rules(rules):
|
|
|
516 split_rules = []
|
|
|
517 err_rules = []
|
|
|
518 tmp_gene_in_rule = []
|
|
|
519 for i in range(len(rules)):
|
|
|
520 tmp = list(rules[i])
|
|
|
521 if tmp:
|
|
|
522 tmp, tmp_genes = check_and_doWord(tmp)
|
|
|
523 tmp_gene_in_rule.extend(tmp_genes)
|
|
|
524 if tmp is False:
|
|
|
525 split_rules.append([])
|
|
|
526 err_rules.append(rules[i])
|
|
|
527 else:
|
|
|
528 tmp = brackets_to_list(tmp)
|
|
|
529 if checkRule(tmp):
|
|
|
530 split_rules.append(priorityAND(tmp))
|
|
|
531 else:
|
|
|
532 split_rules.append([])
|
|
|
533 err_rules.append(rules[i])
|
|
|
534 else:
|
|
|
535 split_rules.append([])
|
|
|
536 if err_rules:
|
|
|
537 warning('Warning: wrong format rule in ' + str(err_rules) + '\n')
|
|
|
538 return (split_rules, list(set(tmp_gene_in_rule)))
|
|
|
539
|
|
|
540 def make_recon(data):
|
|
|
541 try:
|
|
|
542 import cobra as cb
|
|
|
543 import warnings
|
|
|
544 with warnings.catch_warnings():
|
|
|
545 warnings.simplefilter('ignore')
|
|
|
546 recon = cb.io.read_sbml_model(data)
|
|
|
547 react = recon.reactions
|
|
|
548 rules = [react[i].gene_reaction_rule for i in range(len(react))]
|
|
|
549 ids = [react[i].id for i in range(len(react))]
|
|
48
|
550 except cb.io.sbml.CobraSBMLError:
|
|
0
|
551 try:
|
|
16
|
552 data = (pd.read_csv(data, sep = '\t', dtype = str, engine='python')).fillna('')
|
|
0
|
553 if len(data.columns) < 2:
|
|
|
554 sys.exit('Execution aborted: wrong format of '+
|
|
|
555 'custom datarules\n')
|
|
|
556 if not len(data.columns) == 2:
|
|
|
557 warning('Warning: more than 2 columns in custom datarules.\n' +
|
|
|
558 'Extra columns have been disregarded\n')
|
|
|
559 ids = list(data.iloc[:, 0])
|
|
|
560 rules = list(data.iloc[:, 1])
|
|
|
561 except pd.errors.EmptyDataError:
|
|
|
562 sys.exit('Execution aborted: wrong format of custom datarules\n')
|
|
|
563 except pd.errors.ParserError:
|
|
|
564 sys.exit('Execution aborted: wrong format of custom datarules\n')
|
|
|
565 split_rules, tmp_genes = do_rules(rules)
|
|
|
566 gene_in_rule = {}
|
|
|
567 for i in tmp_genes:
|
|
|
568 gene_in_rule[i] = 'ok'
|
|
|
569 return (ids, split_rules, gene_in_rule)
|
|
|
570
|
|
|
571 ############################ gene #############################################
|
|
|
572
|
|
|
573 def data_gene(gene, type_gene, name, gene_custom):
|
|
35
|
574 args = process_args(sys.argv)
|
|
0
|
575 for i in range(len(gene)):
|
|
|
576 tmp = gene.iloc[i, 0]
|
|
|
577 if tmp.startswith(' ') or tmp.endswith(' '):
|
|
|
578 gene.iloc[i, 0] = (tmp.lstrip()).rstrip()
|
|
|
579 gene_dup = [item for item, count in
|
|
|
580 collections.Counter(gene[gene.columns[0]]).items() if count > 1]
|
|
|
581 pat_dup = [item for item, count in
|
|
|
582 collections.Counter(list(gene.columns)).items() if count > 1]
|
|
35
|
583
|
|
0
|
584 if gene_dup:
|
|
|
585 if gene_custom == None:
|
|
|
586 if args.rules_selector == 'HMRcore':
|
|
|
587 gene_in_rule = pk.load(open(args.tool_dir +
|
|
|
588 '/local/HMRcore_genes.p', 'rb'))
|
|
|
589 elif args.rules_selector == 'Recon':
|
|
|
590 gene_in_rule = pk.load(open(args.tool_dir +
|
|
|
591 '/local/Recon_genes.p', 'rb'))
|
|
|
592 gene_in_rule = gene_in_rule.get(type_gene)
|
|
|
593 else:
|
|
|
594 gene_in_rule = gene_custom
|
|
|
595 tmp = []
|
|
|
596 for i in gene_dup:
|
|
|
597 if gene_in_rule.get(i) == 'ok':
|
|
|
598 tmp.append(i)
|
|
|
599 if tmp:
|
|
|
600 sys.exit('Execution aborted because gene ID '
|
|
|
601 +str(tmp)+' in '+name+' is duplicated\n')
|
|
|
602 if pat_dup:
|
|
|
603 warning('Warning: duplicated label\n' + str(pat_dup) + 'in ' + name +
|
|
|
604 '\n')
|
|
35
|
605
|
|
0
|
606 return (gene.set_index(gene.columns[0])).to_dict()
|
|
|
607
|
|
|
608 ############################ resolve ##########################################
|
|
|
609
|
|
|
610 def resolve(genes, rules, ids, resolve_none, name):
|
|
|
611 resolve_rules = {}
|
|
|
612 not_found = []
|
|
|
613 flag = False
|
|
|
614 for key, value in genes.items():
|
|
|
615 tmp_resolve = []
|
|
|
616 for i in range(len(rules)):
|
|
|
617 tmp = rules[i]
|
|
|
618 if tmp:
|
|
|
619 tmp, err = replace_gene_value(tmp, value)
|
|
|
620 if err:
|
|
|
621 not_found.extend(err)
|
|
|
622 ris = control(None, tmp, resolve_none)
|
|
|
623 if ris is False or ris == None:
|
|
|
624 tmp_resolve.append(None)
|
|
|
625 else:
|
|
|
626 tmp_resolve.append(ris)
|
|
|
627 flag = True
|
|
|
628 else:
|
|
35
|
629 tmp_resolve.append(None)
|
|
0
|
630 resolve_rules[key] = tmp_resolve
|
|
|
631 if flag is False:
|
|
|
632 warning('Warning: no computable score (due to missing gene values)' +
|
|
|
633 'for class ' + name + ', the class has been disregarded\n')
|
|
|
634 return (None, None)
|
|
|
635 return (resolve_rules, list(set(not_found)))
|
|
|
636
|
|
|
637 ############################ split class ######################################
|
|
|
638
|
|
|
639 def split_class(classes, resolve_rules):
|
|
|
640 class_pat = {}
|
|
|
641 for i in range(len(classes)):
|
|
|
642 classe = classes.iloc[i, 1]
|
|
|
643 if not pd.isnull(classe):
|
|
|
644 l = []
|
|
|
645 for j in range(i, len(classes)):
|
|
|
646 if classes.iloc[j, 1] == classe:
|
|
|
647 pat_id = classes.iloc[j, 0]
|
|
|
648 tmp = resolve_rules.get(pat_id, None)
|
|
|
649 if tmp != None:
|
|
|
650 l.append(tmp)
|
|
|
651 classes.iloc[j, 1] = None
|
|
|
652 if l:
|
|
|
653 class_pat[classe] = list(map(list, zip(*l)))
|
|
|
654 else:
|
|
|
655 warning('Warning: no sample found in class ' + classe +
|
|
|
656 ', the class has been disregarded\n')
|
|
|
657 return class_pat
|
|
|
658
|
|
|
659 ############################ map ##############################################
|
|
|
660
|
|
47
|
661 def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C, create_svg, create_pdf, comparison, control):
|
|
0
|
662 args = process_args(sys.argv)
|
|
|
663 if (not class_pat) or (len(class_pat.keys()) < 2):
|
|
|
664 sys.exit('Execution aborted: classes provided for comparisons are ' +
|
|
|
665 'less than two\n')
|
|
47
|
666
|
|
|
667 if comparison == "manyvsmany":
|
|
|
668 for i, j in it.combinations(class_pat.keys(), 2):
|
|
|
669
|
|
|
670 tmp = {}
|
|
|
671 count = 0
|
|
|
672 max_F_C = 0
|
|
|
673 for l1, l2 in zip(class_pat.get(i), class_pat.get(j)):
|
|
|
674 try:
|
|
|
675 stat_D, p_value = st.ks_2samp(l1, l2)
|
|
|
676 #sum(l1) da errore secondo me perchè ha null
|
|
|
677 avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2))
|
|
|
678 if not isinstance(avg, str):
|
|
|
679 if max_F_C < abs(avg):
|
|
|
680 max_F_C = abs(avg)
|
|
|
681 tmp[ids[count]] = [float(p_value), avg]
|
|
|
682 count += 1
|
|
|
683 except (TypeError, ZeroDivisionError):
|
|
|
684 count += 1
|
|
|
685 tab = 'result/' + i + '_vs_' + j + ' (Tabular Result).tsv'
|
|
|
686 tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
|
|
|
687 tmp_csv = tmp_csv.reset_index()
|
|
|
688 header = ['ids', 'P_Value', 'Log2(fold change)']
|
|
|
689 tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
|
|
|
690
|
|
|
691 if create_svg or create_pdf:
|
|
48
|
692 if args.custom_rules == 'false' or (args.custom_rules == 'true'
|
|
|
693 and args.custom_map != ''):
|
|
47
|
694 fix_map(tmp, core_map, threshold_P_V, threshold_F_C, max_F_C)
|
|
|
695 file_svg = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'
|
|
|
696 with open(file_svg, 'wb') as new_map:
|
|
|
697 new_map.write(ET.tostring(core_map))
|
|
|
698
|
|
|
699
|
|
|
700 if create_pdf:
|
|
|
701 file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'
|
|
|
702 renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
|
|
|
703
|
|
|
704 if not create_svg:
|
|
|
705 #Ho utilizzato il file svg per generare il pdf,
|
|
|
706 #ma l'utente non ne ha richiesto il ritorno, quindi
|
|
|
707 #lo elimino
|
|
|
708
|
|
|
709 os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')
|
|
|
710 elif comparison == "onevsrest":
|
|
|
711 for single_cluster in class_pat.keys():
|
|
|
712 t = []
|
|
|
713 for k in class_pat.keys():
|
|
|
714 if k != single_cluster:
|
|
|
715 t.append(class_pat.get(k))
|
|
|
716 rest = []
|
|
|
717 for i in t:
|
|
|
718 rest = rest + i
|
|
|
719
|
|
|
720 tmp = {}
|
|
|
721 count = 0
|
|
|
722 max_F_C = 0
|
|
|
723
|
|
|
724 for l1, l2 in zip(rest, class_pat.get(single_cluster)):
|
|
|
725 try:
|
|
|
726 stat_D, p_value = st.ks_2samp(l1, l2)
|
|
|
727 avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2))
|
|
|
728 if not isinstance(avg, str):
|
|
|
729 if max_F_C < abs(avg):
|
|
|
730 max_F_C = abs(avg)
|
|
|
731 tmp[ids[count]] = [float(p_value), avg]
|
|
|
732 count += 1
|
|
|
733 except (TypeError, ZeroDivisionError):
|
|
|
734 count += 1
|
|
|
735 tab = 'result/' + single_cluster + '_vs_rest (Tabular Result).tsv'
|
|
|
736 tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
|
|
|
737 tmp_csv = tmp_csv.reset_index()
|
|
|
738 header = ['ids', 'P_Value', 'Log2(fold change)']
|
|
|
739 tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
|
|
|
740
|
|
|
741 if create_svg or create_pdf:
|
|
|
742 if args.rules_selector == 'HMRcore' or (args.rules_selector == 'Custom'
|
|
|
743 and args.yes_no == 'yes'):
|
|
|
744 fix_map(tmp, core_map, threshold_P_V, threshold_F_C, max_F_C)
|
|
|
745 file_svg = 'result/' + single_cluster + '_vs_ rest (SVG Map).svg'
|
|
|
746 with open(file_svg, 'wb') as new_map:
|
|
|
747 new_map.write(ET.tostring(core_map))
|
|
|
748
|
|
|
749
|
|
|
750 if create_pdf:
|
|
|
751 file_pdf = 'result/' + single_cluster + '_vs_ rest (PDF Map).pdf'
|
|
|
752 renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
|
|
|
753
|
|
|
754 if not create_svg:
|
|
|
755 os.remove('result/' + single_cluster + '_vs_ rest (SVG Map).svg')
|
|
|
756
|
|
|
757 elif comparison == "onevsmany":
|
|
|
758 for i, j in it.combinations(class_pat.keys(), 2):
|
|
|
759 tmp = {}
|
|
|
760 count = 0
|
|
|
761 max_F_C = 0
|
|
|
762 for l1, l2 in zip(class_pat.get(i), class_pat.get(j)):
|
|
|
763 try:
|
|
|
764 stat_D, p_value = st.ks_2samp(l1, l2)
|
|
|
765 #sum(l1) da errore secondo me perchè ha null
|
|
|
766 avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2))
|
|
|
767 if not isinstance(avg, str):
|
|
|
768 if max_F_C < abs(avg):
|
|
|
769 max_F_C = abs(avg)
|
|
|
770 tmp[ids[count]] = [float(p_value), avg]
|
|
|
771 count += 1
|
|
|
772 except (TypeError, ZeroDivisionError):
|
|
|
773 count += 1
|
|
|
774 tab = 'result/' + i + '_vs_' + j + ' (Tabular Result).tsv'
|
|
|
775 tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
|
|
|
776 tmp_csv = tmp_csv.reset_index()
|
|
|
777 header = ['ids', 'P_Value', 'Log2(fold change)']
|
|
|
778 tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
|
|
|
779
|
|
|
780 if create_svg or create_pdf:
|
|
|
781 if args.rules_selector == 'HMRcore' or (args.rules_selector == 'Custom'
|
|
|
782 and args.yes_no == 'yes'):
|
|
|
783 fix_map(tmp, core_map, threshold_P_V, threshold_F_C, max_F_C)
|
|
|
784 file_svg = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'
|
|
|
785 with open(file_svg, 'wb') as new_map:
|
|
|
786 new_map.write(ET.tostring(core_map))
|
|
|
787
|
|
|
788
|
|
|
789 if create_pdf:
|
|
|
790 file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'
|
|
|
791 renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
|
|
|
792
|
|
|
793 if not create_svg:
|
|
|
794 #Ho utilizzato il file svg per generare il pdf,
|
|
|
795 #ma l'utente non ne ha richiesto il ritorno, quindi
|
|
|
796 #lo elimino
|
|
|
797
|
|
|
798 os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')
|
|
16
|
799
|
|
47
|
800
|
|
|
801
|
|
|
802
|
|
0
|
803 return None
|
|
|
804
|
|
|
805 ############################ MAIN #############################################
|
|
|
806
|
|
|
807 def main():
|
|
|
808 args = process_args(sys.argv)
|
|
16
|
809
|
|
|
810 create_svg = check_bool(args.generate_svg)
|
|
|
811 create_pdf = check_bool(args.generate_pdf)
|
|
35
|
812
|
|
47
|
813 if os.path.isdir('result') == False:
|
|
|
814 os.makedirs('result')
|
|
48
|
815
|
|
|
816 if args.custom_rules == 'true':
|
|
|
817 ids, rules, gene_in_rule = make_recon(args.custom_rule)
|
|
47
|
818
|
|
0
|
819 class_pat = {}
|
|
16
|
820
|
|
47
|
821 if args.option == 'datasets':
|
|
0
|
822 num = 1
|
|
|
823 for i, j in zip(args.input_datas, args.names):
|
|
|
824 name = name_dataset(j, num)
|
|
47
|
825 resolve_rules = read_dataset(i, name)
|
|
|
826
|
|
|
827 resolve_rules.iloc[:, 0] = (resolve_rules.iloc[:, 0]).astype(str)
|
|
16
|
828
|
|
47
|
829 ids = pd.Series.tolist(resolve_rules.iloc[:, 0])
|
|
16
|
830
|
|
47
|
831 resolve_rules = resolve_rules.drop(resolve_rules.columns[[0]], axis=1)
|
|
|
832 resolve_rules = resolve_rules.replace({'None': None})
|
|
|
833 resolve_rules = resolve_rules.to_dict('list')
|
|
16
|
834
|
|
47
|
835 #Converto i valori da str a float
|
|
|
836 to_float = lambda x: float(x) if (x != None) else None
|
|
|
837
|
|
|
838 resolve_rules_float = {}
|
|
|
839
|
|
|
840 for k in resolve_rules:
|
|
|
841 resolve_rules_float[k] = list(map(to_float, resolve_rules[k])); resolve_rules_float
|
|
|
842
|
|
0
|
843 if resolve_rules != None:
|
|
47
|
844 class_pat[name] = list(map(list, zip(*resolve_rules_float.values())))
|
|
|
845
|
|
0
|
846 num += 1
|
|
47
|
847
|
|
|
848 if args.option == 'dataset_class':
|
|
|
849 name = 'RAS'
|
|
|
850 resolve_rules = read_dataset(args.input_data, name)
|
|
|
851 resolve_rules.iloc[:, 0] = (resolve_rules.iloc[:, 0]).astype(str)
|
|
|
852
|
|
|
853 ids = pd.Series.tolist(resolve_rules.iloc[:, 0])
|
|
|
854
|
|
|
855 resolve_rules = resolve_rules.drop(resolve_rules.columns[[0]], axis=1)
|
|
|
856 resolve_rules = resolve_rules.replace({'None': None})
|
|
|
857 resolve_rules = resolve_rules.to_dict('list')
|
|
|
858
|
|
|
859 #Converto i valori da str a float
|
|
|
860 to_float = lambda x: float(x) if (x != None) else None
|
|
|
861
|
|
|
862 resolve_rules_float = {}
|
|
|
863
|
|
|
864 for k in resolve_rules:
|
|
|
865 resolve_rules_float[k] = list(map(to_float, resolve_rules[k])); resolve_rules_float
|
|
|
866
|
|
0
|
867 classes = read_dataset(args.input_class, 'class')
|
|
|
868 classes = classes.astype(str)
|
|
47
|
869
|
|
|
870 if resolve_rules_float != None:
|
|
|
871 class_pat = split_class(classes, resolve_rules_float)
|
|
28
|
872
|
|
48
|
873
|
|
|
874 if args.custom_rules == 'true':
|
|
|
875 try:
|
|
|
876 core_map = ET.parse(args.custom_map)
|
|
|
877 except (ET.XMLSyntaxError, ET.XMLSchemaParseError):
|
|
|
878 sys.exit('Execution aborted: custom map in wrong format')
|
|
|
879 else:
|
|
0
|
880 core_map = ET.parse(args.tool_dir+'/local/HMRcoreMap.svg')
|
|
16
|
881
|
|
47
|
882 maps(core_map, class_pat, ids, args.pValue, args.fChange, create_svg, create_pdf, args.comparison, args.control)
|
|
16
|
883
|
|
|
884 print('Execution succeded')
|
|
47
|
885
|
|
|
886 return None
|
|
16
|
887
|
|
0
|
888
|
|
|
889 ###############################################################################
|
|
|
890
|
|
|
891 if __name__ == "__main__":
|
|
|
892 main()
|
