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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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21 parser.add_argument('-rs', '--rules_selector',
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22 type = str,
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23 default = 'HMRcore',
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24 choices = ['HMRcore', 'Recon', 'Custom'],
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25 help = 'chose which type of dataset you want use')
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26 parser.add_argument('-cr', '--custom',
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27 type = str,
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28 help='your dataset if you want custom rules')
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29 parser.add_argument('-na', '--names',
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30 type = str,
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31 nargs = '+',
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32 help = 'input names')
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33 parser.add_argument('-n', '--none',
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34 type = str,
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35 default = 'true',
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36 choices = ['true', 'false'],
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37 help = 'compute Nan values')
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38 parser.add_argument('-pv' ,'--pValue',
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39 type = float,
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40 default = 0.05,
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41 help = 'P-Value threshold (default: %(default)s)')
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42 parser.add_argument('-fc', '--fChange',
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43 type = float,
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44 default = 1.5,
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45 help = 'Fold-Change threshold (default: %(default)s)')
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46 parser.add_argument('-td', '--tool_dir',
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47 type = str,
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48 required = True,
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49 help = 'your tool directory')
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50 parser.add_argument('-op', '--option',
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51 type = str,
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28
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52 choices = ['datasets', 'dataset_class', 'datasets_rasonly'],
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53 help='dataset or dataset and class')
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54 parser.add_argument('-ol', '--out_log',
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55 help = "Output log")
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56 parser.add_argument('-ids', '--input_datas',
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57 type = str,
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58 nargs = '+',
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59 help = 'input datasets')
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60 parser.add_argument('-id', '--input_data',
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61 type = str,
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62 help = 'input dataset')
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63 parser.add_argument('-ic', '--input_class',
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64 type = str,
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65 help = 'sample group specification')
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66 parser.add_argument('-cm', '--custom_map',
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67 type = str,
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68 help = 'custom map')
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69 parser.add_argument('-yn', '--yes_no',
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70 type = str,
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71 choices = ['yes', 'no'],
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72 help = 'if make or not custom map')
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16
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73 parser.add_argument('-gs', '--generate_svg',
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74 type = str,
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75 default = 'true',
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76 choices = ['true', 'false'],
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77 help = 'generate svg map')
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78 parser.add_argument('-gp', '--generate_pdf',
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79 type = str,
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80 default = 'true',
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81 choices = ['true', 'false'],
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82 help = 'generate pdf map')
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83 parser.add_argument('-gr', '--generate_ras',
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84 type = str,
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85 default = 'true',
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86 choices = ['true', 'false'],
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87 help = 'generate reaction activity score')
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28
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88 parser.add_argument('-sr', '--single_ras_file',
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89 type = str,
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90 help = 'file that will contain ras')
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91
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92 args = parser.parse_args()
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93 return args
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94
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95 ########################### warning ###########################################
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96
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97 def warning(s):
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98 args = process_args(sys.argv)
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99 with open(args.out_log, 'a') as log:
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100 log.write(s)
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101
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102 ############################ dataset input ####################################
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103
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104 def read_dataset(data, name):
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105 try:
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106 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
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107 except pd.errors.EmptyDataError:
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108 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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109 if len(dataset.columns) < 2:
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110 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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111 return dataset
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112
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113 ############################ dataset name #####################################
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114
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115 def name_dataset(name_data, count):
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116 if str(name_data) == 'Dataset':
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117 return str(name_data) + '_' + str(count)
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118 else:
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119 return str(name_data)
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120
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121 ############################ load id e rules ##################################
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122
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123 def load_id_rules(reactions):
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124 ids, rules = [], []
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125 for key, value in reactions.items():
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126 ids.append(key)
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127 rules.append(value)
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128 return (ids, rules)
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129
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130 ############################ check_methods ####################################
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131
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132 def gene_type(l, name):
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133 if check_hgnc(l):
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134 return 'hugo_id'
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135 elif check_ensembl(l):
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136 return 'ensembl_gene_id'
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137 elif check_symbol(l):
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138 return 'symbol'
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139 elif check_entrez(l):
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140 return 'entrez_id'
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141 else:
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142 sys.exit('Execution aborted:\n' +
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143 'gene ID type in ' + name + ' not supported. Supported ID'+
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144 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n')
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145
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146 def check_hgnc(l):
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147 if len(l) > 5:
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148 if (l.upper()).startswith('HGNC:'):
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149 return l[5:].isdigit()
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150 else:
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151 return False
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152 else:
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153 return False
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154
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155 def check_ensembl(l):
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156 if len(l) == 15:
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157 if (l.upper()).startswith('ENS'):
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158 return l[4:].isdigit()
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159 else:
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160 return False
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161 else:
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162 return False
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163
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164 def check_symbol(l):
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165 if len(l) > 0:
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166 if l[0].isalpha() and l[1:].isalnum():
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167 return True
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168 else:
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169 return False
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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_entrez(l):
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174 if len(l) > 0:
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175 return l.isdigit()
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176 else:
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177 return False
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178
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179 def check_bool(b):
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180 if b == 'true':
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181 return True
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182 elif b == 'false':
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183 return False
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184
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185 ############################ resolve_methods ##################################
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186
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187 def replace_gene_value(l, d):
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188 tmp = []
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189 err = []
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190 while l:
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191 if isinstance(l[0], list):
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192 tmp_rules, tmp_err = replace_gene_value(l[0], d)
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193 tmp.append(tmp_rules)
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194 err.extend(tmp_err)
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195 else:
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196 value = replace_gene(l[0], d)
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197 tmp.append(value)
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198 if value == None:
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199 err.append(l[0])
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200 l = l[1:]
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201 return (tmp, err)
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202
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35
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203
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0
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204 def replace_gene(l, d):
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205 if l =='and' or l == 'or':
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206 return l
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207 else:
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208 value = d.get(l, None)
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209 if not(value == None or isinstance(value, (int, float))):
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210 sys.exit('Execution aborted: ' + value + ' value not valid\n')
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211 return value
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212
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213 def computes(val1, op, val2, cn):
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214 if val1 != None and val2 != None:
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215 if op == 'and':
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216 return min(val1, val2)
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217 else:
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218 return val1 + val2
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219 elif op == 'and':
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220 if cn is True:
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221 if val1 != None:
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222 return val1
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223 elif val2 != None:
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224 return val2
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225 else:
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226 return None
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227 else:
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228 return None
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229 else:
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230 if val1 != None:
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231 return val1
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232 elif val2 != None:
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233 return val2
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234 else:
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235 return None
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236
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237 def control(ris, l, cn):
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238 if len(l) == 1:
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239 if isinstance(l[0], (float, int)) or l[0] == None:
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240 return l[0]
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241 elif isinstance(l[0], list):
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242 return control(None, l[0], cn)
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243 else:
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244 return False
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245 elif len(l) > 2:
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246 return control_list(ris, l, cn)
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247 else:
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248 return False
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249
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250 def control_list(ris, l, cn):
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251 while l:
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252 if len(l) == 1:
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253 return False
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254 elif (isinstance(l[0], (float, int)) or
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255 l[0] == None) and l[1] in ['and', 'or']:
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256 if isinstance(l[2], (float, int)) or l[2] == None:
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257 ris = computes(l[0], l[1], l[2], cn)
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258 elif isinstance(l[2], list):
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259 tmp = control(None, l[2], cn)
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260 if tmp is False:
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261 return False
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262 else:
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263 ris = computes(l[0], l[1], tmp, cn)
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264 else:
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265 return False
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266 l = l[3:]
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267 elif l[0] in ['and', 'or']:
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268 if isinstance(l[1], (float, int)) or l[1] == None:
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269 ris = computes(ris, l[0], l[1], cn)
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270 elif isinstance(l[1], list):
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271 tmp = control(None,l[1], cn)
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272 if tmp is False:
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273 return False
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274 else:
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275 ris = computes(ris, l[0], tmp, cn)
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276 else:
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277 return False
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278 l = l[2:]
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279 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
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280 if isinstance(l[2], (float, int)) or l[2] == None:
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281 tmp = control(None, l[0], cn)
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282 if tmp is False:
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283 return False
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284 else:
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285 ris = computes(tmp, l[1], l[2], cn)
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286 elif isinstance(l[2], list):
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287 tmp = control(None, l[0], cn)
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288 tmp2 = control(None, l[2], cn)
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289 if tmp is False or tmp2 is False:
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290 return False
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291 else:
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292 ris = computes(tmp, l[1], tmp2, cn)
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293 else:
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294 return False
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295 l = l[3:]
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296 else:
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297 return False
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298 return ris
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299
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300 ############################ map_methods ######################################
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301
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302 def fold_change(avg1, avg2):
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303 if avg1 == 0 and avg2 == 0:
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304 return 0
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305 elif avg1 == 0:
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306 return '-INF'
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307 elif avg2 == 0:
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308 return 'INF'
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309 else:
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310 return math.log(avg1 / avg2, 2)
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311
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312 def fix_style(l, col, width, dash):
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313 tmp = l.split(';')
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314 flag_col = False
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315 flag_width = False
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316 flag_dash = False
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317 for i in range(len(tmp)):
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318 if tmp[i].startswith('stroke:'):
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319 tmp[i] = 'stroke:' + col
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320 flag_col = True
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321 if tmp[i].startswith('stroke-width:'):
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322 tmp[i] = 'stroke-width:' + width
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323 flag_width = True
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324 if tmp[i].startswith('stroke-dasharray:'):
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325 tmp[i] = 'stroke-dasharray:' + dash
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326 flag_dash = True
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327 if not flag_col:
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328 tmp.append('stroke:' + col)
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329 if not flag_width:
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330 tmp.append('stroke-width:' + width)
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331 if not flag_dash:
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332 tmp.append('stroke-dasharray:' + dash)
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333 return ';'.join(tmp)
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334
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335 def fix_map(d, core_map, threshold_P_V, threshold_F_C, max_F_C):
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336 maxT = 12
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337 minT = 2
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338 grey = '#BEBEBE'
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339 blue = '#0000FF'
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340 red = '#E41A1C'
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341 for el in core_map.iter():
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342 el_id = str(el.get('id'))
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343 if el_id.startswith('R_'):
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344 tmp = d.get(el_id[2:])
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345 if tmp != None:
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346 p_val = tmp[0]
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347 f_c = tmp[1]
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348 if p_val < threshold_P_V:
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349 if not isinstance(f_c, str):
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350 if abs(f_c) < math.log(threshold_F_C, 2):
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351 col = grey
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352 width = str(minT)
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353 else:
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354 if f_c < 0:
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355 col = blue
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356 elif f_c > 0:
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357 col = red
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358 width = str(max((abs(f_c) * maxT) / max_F_C, minT))
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359 else:
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360 if f_c == '-INF':
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361 col = blue
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362 elif f_c == 'INF':
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363 col = red
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364 width = str(maxT)
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365 dash = 'none'
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366 else:
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367 dash = '5,5'
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368 col = grey
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369 width = str(minT)
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370 el.set('style', fix_style(el.get('style'), col, width, dash))
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371 return core_map
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372
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373 ############################ make recon #######################################
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374
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375 def check_and_doWord(l):
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376 tmp = []
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377 tmp_genes = []
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378 count = 0
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379 while l:
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380 if count >= 0:
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381 if l[0] == '(':
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382 count += 1
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383 tmp.append(l[0])
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384 l.pop(0)
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385 elif l[0] == ')':
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386 count -= 1
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387 tmp.append(l[0])
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388 l.pop(0)
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389 elif l[0] == ' ':
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390 l.pop(0)
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391 else:
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392 word = []
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393 while l:
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394 if l[0] in [' ', '(', ')']:
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395 break
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396 else:
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397 word.append(l[0])
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398 l.pop(0)
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399 word = ''.join(word)
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400 tmp.append(word)
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401 if not(word in ['or', 'and']):
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402 tmp_genes.append(word)
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403 else:
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404 return False
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405 if count == 0:
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406 return (tmp, tmp_genes)
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407 else:
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408 return False
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409
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410 def brackets_to_list(l):
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411 tmp = []
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412 while l:
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413 if l[0] == '(':
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414 l.pop(0)
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415 tmp.append(resolve_brackets(l))
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416 else:
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417 tmp.append(l[0])
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418 l.pop(0)
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419 return tmp
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420
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421 def resolve_brackets(l):
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422 tmp = []
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423 while l[0] != ')':
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424 if l[0] == '(':
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425 l.pop(0)
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426 tmp.append(resolve_brackets(l))
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427 else:
|
|
|
428 tmp.append(l[0])
|
|
|
429 l.pop(0)
|
|
|
430 l.pop(0)
|
|
|
431 return tmp
|
|
|
432
|
|
|
433 def priorityAND(l):
|
|
|
434 tmp = []
|
|
|
435 flag = True
|
|
|
436 while l:
|
|
|
437 if len(l) == 1:
|
|
|
438 if isinstance(l[0], list):
|
|
|
439 tmp.append(priorityAND(l[0]))
|
|
|
440 else:
|
|
|
441 tmp.append(l[0])
|
|
|
442 l = l[1:]
|
|
|
443 elif l[0] == 'or':
|
|
|
444 tmp.append(l[0])
|
|
|
445 flag = False
|
|
|
446 l = l[1:]
|
|
|
447 elif l[1] == 'or':
|
|
|
448 if isinstance(l[0], list):
|
|
|
449 tmp.append(priorityAND(l[0]))
|
|
|
450 else:
|
|
|
451 tmp.append(l[0])
|
|
|
452 tmp.append(l[1])
|
|
|
453 flag = False
|
|
|
454 l = l[2:]
|
|
|
455 elif l[1] == 'and':
|
|
|
456 tmpAnd = []
|
|
|
457 if isinstance(l[0], list):
|
|
|
458 tmpAnd.append(priorityAND(l[0]))
|
|
|
459 else:
|
|
|
460 tmpAnd.append(l[0])
|
|
|
461 tmpAnd.append(l[1])
|
|
|
462 if isinstance(l[2], list):
|
|
|
463 tmpAnd.append(priorityAND(l[2]))
|
|
|
464 else:
|
|
|
465 tmpAnd.append(l[2])
|
|
|
466 l = l[3:]
|
|
|
467 while l:
|
|
|
468 if l[0] == 'and':
|
|
|
469 tmpAnd.append(l[0])
|
|
|
470 if isinstance(l[1], list):
|
|
|
471 tmpAnd.append(priorityAND(l[1]))
|
|
|
472 else:
|
|
|
473 tmpAnd.append(l[1])
|
|
|
474 l = l[2:]
|
|
|
475 elif l[0] == 'or':
|
|
|
476 flag = False
|
|
|
477 break
|
|
13
|
478 if flag == True: #when there are only AND in list
|
|
0
|
479 tmp.extend(tmpAnd)
|
|
|
480 elif flag == False:
|
|
|
481 tmp.append(tmpAnd)
|
|
|
482 return tmp
|
|
|
483
|
|
|
484 def checkRule(l):
|
|
|
485 if len(l) == 1:
|
|
|
486 if isinstance(l[0], list):
|
|
|
487 if checkRule(l[0]) is False:
|
|
|
488 return False
|
|
|
489 elif len(l) > 2:
|
|
|
490 if checkRule2(l) is False:
|
|
|
491 return False
|
|
|
492 else:
|
|
|
493 return False
|
|
|
494 return True
|
|
|
495
|
|
|
496 def checkRule2(l):
|
|
|
497 while l:
|
|
|
498 if len(l) == 1:
|
|
|
499 return False
|
|
|
500 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
|
|
|
501 if checkRule(l[0]) is False:
|
|
|
502 return False
|
|
|
503 if isinstance(l[2], list):
|
|
|
504 if checkRule(l[2]) is False:
|
|
|
505 return False
|
|
|
506 l = l[3:]
|
|
|
507 elif l[1] in ['and', 'or']:
|
|
|
508 if isinstance(l[2], list):
|
|
|
509 if checkRule(l[2]) is False:
|
|
|
510 return False
|
|
|
511 l = l[3:]
|
|
|
512 elif l[0] in ['and', 'or']:
|
|
|
513 if isinstance(l[1], list):
|
|
|
514 if checkRule(l[1]) is False:
|
|
|
515 return False
|
|
|
516 l = l[2:]
|
|
|
517 else:
|
|
|
518 return False
|
|
|
519 return True
|
|
|
520
|
|
|
521 def do_rules(rules):
|
|
|
522 split_rules = []
|
|
|
523 err_rules = []
|
|
|
524 tmp_gene_in_rule = []
|
|
|
525 for i in range(len(rules)):
|
|
|
526 tmp = list(rules[i])
|
|
|
527 if tmp:
|
|
|
528 tmp, tmp_genes = check_and_doWord(tmp)
|
|
|
529 tmp_gene_in_rule.extend(tmp_genes)
|
|
|
530 if tmp is False:
|
|
|
531 split_rules.append([])
|
|
|
532 err_rules.append(rules[i])
|
|
|
533 else:
|
|
|
534 tmp = brackets_to_list(tmp)
|
|
|
535 if checkRule(tmp):
|
|
|
536 split_rules.append(priorityAND(tmp))
|
|
|
537 else:
|
|
|
538 split_rules.append([])
|
|
|
539 err_rules.append(rules[i])
|
|
|
540 else:
|
|
|
541 split_rules.append([])
|
|
|
542 if err_rules:
|
|
|
543 warning('Warning: wrong format rule in ' + str(err_rules) + '\n')
|
|
|
544 return (split_rules, list(set(tmp_gene_in_rule)))
|
|
|
545
|
|
|
546 def make_recon(data):
|
|
|
547 try:
|
|
|
548 import cobra as cb
|
|
|
549 import warnings
|
|
|
550 with warnings.catch_warnings():
|
|
|
551 warnings.simplefilter('ignore')
|
|
|
552 recon = cb.io.read_sbml_model(data)
|
|
|
553 react = recon.reactions
|
|
|
554 rules = [react[i].gene_reaction_rule for i in range(len(react))]
|
|
|
555 ids = [react[i].id for i in range(len(react))]
|
|
|
556 except cb.io.sbml3.CobraSBMLError:
|
|
|
557 try:
|
|
16
|
558 data = (pd.read_csv(data, sep = '\t', dtype = str, engine='python')).fillna('')
|
|
0
|
559 if len(data.columns) < 2:
|
|
|
560 sys.exit('Execution aborted: wrong format of '+
|
|
|
561 'custom datarules\n')
|
|
|
562 if not len(data.columns) == 2:
|
|
|
563 warning('Warning: more than 2 columns in custom datarules.\n' +
|
|
|
564 'Extra columns have been disregarded\n')
|
|
|
565 ids = list(data.iloc[:, 0])
|
|
|
566 rules = list(data.iloc[:, 1])
|
|
|
567 except pd.errors.EmptyDataError:
|
|
|
568 sys.exit('Execution aborted: wrong format of custom datarules\n')
|
|
|
569 except pd.errors.ParserError:
|
|
|
570 sys.exit('Execution aborted: wrong format of custom datarules\n')
|
|
|
571 split_rules, tmp_genes = do_rules(rules)
|
|
|
572 gene_in_rule = {}
|
|
|
573 for i in tmp_genes:
|
|
|
574 gene_in_rule[i] = 'ok'
|
|
|
575 return (ids, split_rules, gene_in_rule)
|
|
|
576
|
|
|
577 ############################ gene #############################################
|
|
|
578
|
|
|
579 def data_gene(gene, type_gene, name, gene_custom):
|
|
35
|
580 args = process_args(sys.argv)
|
|
0
|
581 for i in range(len(gene)):
|
|
|
582 tmp = gene.iloc[i, 0]
|
|
|
583 if tmp.startswith(' ') or tmp.endswith(' '):
|
|
|
584 gene.iloc[i, 0] = (tmp.lstrip()).rstrip()
|
|
|
585 gene_dup = [item for item, count in
|
|
|
586 collections.Counter(gene[gene.columns[0]]).items() if count > 1]
|
|
|
587 pat_dup = [item for item, count in
|
|
|
588 collections.Counter(list(gene.columns)).items() if count > 1]
|
|
35
|
589
|
|
0
|
590 if gene_dup:
|
|
|
591 if gene_custom == None:
|
|
|
592 if args.rules_selector == 'HMRcore':
|
|
|
593 gene_in_rule = pk.load(open(args.tool_dir +
|
|
|
594 '/local/HMRcore_genes.p', 'rb'))
|
|
|
595 elif args.rules_selector == 'Recon':
|
|
|
596 gene_in_rule = pk.load(open(args.tool_dir +
|
|
|
597 '/local/Recon_genes.p', 'rb'))
|
|
|
598 gene_in_rule = gene_in_rule.get(type_gene)
|
|
|
599 else:
|
|
|
600 gene_in_rule = gene_custom
|
|
|
601 tmp = []
|
|
|
602 for i in gene_dup:
|
|
|
603 if gene_in_rule.get(i) == 'ok':
|
|
|
604 tmp.append(i)
|
|
|
605 if tmp:
|
|
|
606 sys.exit('Execution aborted because gene ID '
|
|
|
607 +str(tmp)+' in '+name+' is duplicated\n')
|
|
|
608 if pat_dup:
|
|
|
609 warning('Warning: duplicated label\n' + str(pat_dup) + 'in ' + name +
|
|
|
610 '\n')
|
|
35
|
611
|
|
0
|
612 return (gene.set_index(gene.columns[0])).to_dict()
|
|
|
613
|
|
|
614 ############################ resolve ##########################################
|
|
|
615
|
|
|
616 def resolve(genes, rules, ids, resolve_none, name):
|
|
|
617 resolve_rules = {}
|
|
35
|
618 names_array = []
|
|
0
|
619 not_found = []
|
|
|
620 flag = False
|
|
|
621 for key, value in genes.items():
|
|
|
622 tmp_resolve = []
|
|
|
623 for i in range(len(rules)):
|
|
|
624 tmp = rules[i]
|
|
|
625 if tmp:
|
|
|
626 tmp, err = replace_gene_value(tmp, value)
|
|
|
627 if err:
|
|
|
628 not_found.extend(err)
|
|
|
629 ris = control(None, tmp, resolve_none)
|
|
|
630 if ris is False or ris == None:
|
|
|
631 tmp_resolve.append(None)
|
|
|
632 else:
|
|
|
633 tmp_resolve.append(ris)
|
|
|
634 flag = True
|
|
|
635 else:
|
|
35
|
636 tmp_resolve.append(None)
|
|
0
|
637 resolve_rules[key] = tmp_resolve
|
|
|
638 if flag is False:
|
|
|
639 warning('Warning: no computable score (due to missing gene values)' +
|
|
|
640 'for class ' + name + ', the class has been disregarded\n')
|
|
|
641 return (None, None)
|
|
|
642 return (resolve_rules, list(set(not_found)))
|
|
|
643
|
|
|
644 ############################ split class ######################################
|
|
|
645
|
|
|
646 def split_class(classes, resolve_rules):
|
|
|
647 class_pat = {}
|
|
|
648 for i in range(len(classes)):
|
|
|
649 classe = classes.iloc[i, 1]
|
|
|
650 if not pd.isnull(classe):
|
|
|
651 l = []
|
|
|
652 for j in range(i, len(classes)):
|
|
|
653 if classes.iloc[j, 1] == classe:
|
|
|
654 pat_id = classes.iloc[j, 0]
|
|
|
655 tmp = resolve_rules.get(pat_id, None)
|
|
|
656 if tmp != None:
|
|
|
657 l.append(tmp)
|
|
|
658 classes.iloc[j, 1] = None
|
|
|
659 if l:
|
|
|
660 class_pat[classe] = list(map(list, zip(*l)))
|
|
|
661 else:
|
|
|
662 warning('Warning: no sample found in class ' + classe +
|
|
|
663 ', the class has been disregarded\n')
|
|
|
664 return class_pat
|
|
|
665
|
|
16
|
666 ############################ create_ras #######################################
|
|
|
667
|
|
35
|
668 def create_ras (resolve_rules, dataset_name, single_ras, rules, ids):
|
|
16
|
669
|
|
|
670 if resolve_rules == None:
|
|
|
671 warning("Couldn't generate RAS for current dataset: " + dataset_name)
|
|
|
672
|
|
|
673 for geni in resolve_rules.values():
|
|
|
674 for i, valori in enumerate(geni):
|
|
|
675 if valori == None:
|
|
|
676 geni[i] = 'None'
|
|
|
677
|
|
|
678 output_ras = pd.DataFrame.from_dict(resolve_rules)
|
|
35
|
679
|
|
|
680 output_ras.insert(0, 'Reactions', ids)
|
|
16
|
681 output_to_csv = pd.DataFrame.to_csv(output_ras, sep = '\t', index = False)
|
|
28
|
682
|
|
|
683 if (single_ras):
|
|
|
684 args = process_args(sys.argv)
|
|
|
685 text_file = open(args.single_ras_file, "w")
|
|
|
686 else:
|
|
|
687 text_file = open("ras/Reaction_Activity_Score_Of_" + dataset_name + ".tsv", "w")
|
|
|
688
|
|
16
|
689 text_file.write(output_to_csv)
|
|
|
690 text_file.close()
|
|
|
691
|
|
0
|
692 ############################ map ##############################################
|
|
|
693
|
|
16
|
694 def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C, create_svg, create_pdf):
|
|
0
|
695 args = process_args(sys.argv)
|
|
|
696 if (not class_pat) or (len(class_pat.keys()) < 2):
|
|
|
697 sys.exit('Execution aborted: classes provided for comparisons are ' +
|
|
|
698 'less than two\n')
|
|
|
699 for i, j in it.combinations(class_pat.keys(), 2):
|
|
|
700 tmp = {}
|
|
|
701 count = 0
|
|
|
702 max_F_C = 0
|
|
|
703 for l1, l2 in zip(class_pat.get(i), class_pat.get(j)):
|
|
|
704 try:
|
|
|
705 stat_D, p_value = st.ks_2samp(l1, l2)
|
|
|
706 avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2))
|
|
|
707 if not isinstance(avg, str):
|
|
|
708 if max_F_C < abs(avg):
|
|
|
709 max_F_C = abs(avg)
|
|
|
710 tmp[ids[count]] = [float(p_value), avg]
|
|
|
711 count += 1
|
|
|
712 except (TypeError, ZeroDivisionError):
|
|
|
713 count += 1
|
|
16
|
714 tab = 'result/' + i + '_vs_' + j + ' (Tabular Result).tsv'
|
|
0
|
715 tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
|
|
|
716 tmp_csv = tmp_csv.reset_index()
|
|
31
|
717 header = ['ids', 'P_Value', 'Log2(fold change)']
|
|
0
|
718 tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
|
|
16
|
719
|
|
|
720 if create_svg or create_pdf:
|
|
|
721 if args.rules_selector == 'HMRcore' or (args.rules_selector == 'Custom'
|
|
|
722 and args.yes_no == 'yes'):
|
|
|
723 fix_map(tmp, core_map, threshold_P_V, threshold_F_C, max_F_C)
|
|
|
724 file_svg = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'
|
|
|
725 with open(file_svg, 'wb') as new_map:
|
|
|
726 new_map.write(ET.tostring(core_map))
|
|
|
727
|
|
|
728
|
|
|
729 if create_pdf:
|
|
|
730 file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'
|
|
|
731 renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
|
|
|
732
|
|
|
733 if not create_svg:
|
|
|
734 #Ho utilizzato il file svg per generare il pdf,
|
|
|
735 #ma l'utente non ne ha richiesto il ritorno, quindi
|
|
|
736 #lo elimino
|
|
|
737 os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')
|
|
|
738
|
|
0
|
739 return None
|
|
|
740
|
|
|
741 ############################ MAIN #############################################
|
|
|
742
|
|
|
743 def main():
|
|
|
744 args = process_args(sys.argv)
|
|
16
|
745
|
|
|
746 create_svg = check_bool(args.generate_svg)
|
|
|
747 create_pdf = check_bool(args.generate_pdf)
|
|
|
748 generate_ras = check_bool(args.generate_ras)
|
|
35
|
749
|
|
16
|
750 os.makedirs('result')
|
|
|
751
|
|
|
752 if generate_ras:
|
|
|
753 os.makedirs('ras')
|
|
|
754
|
|
|
755 if args.rules_selector == 'HMRcore':
|
|
0
|
756 recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb'))
|
|
|
757 elif args.rules_selector == 'Recon':
|
|
|
758 recon = pk.load(open(args.tool_dir + '/local/Recon_rules.p', 'rb'))
|
|
|
759 elif args.rules_selector == 'Custom':
|
|
|
760 ids, rules, gene_in_rule = make_recon(args.custom)
|
|
16
|
761
|
|
0
|
762 resolve_none = check_bool(args.none)
|
|
16
|
763
|
|
0
|
764 class_pat = {}
|
|
16
|
765
|
|
28
|
766 if args.option == 'datasets_rasonly':
|
|
|
767 name = "RAS Dataset"
|
|
|
768 dataset = read_dataset(args.input_datas[0],"dataset")
|
|
|
769
|
|
|
770 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
|
|
|
771
|
|
|
772 type_gene = gene_type(dataset.iloc[0, 0], name)
|
|
|
773
|
|
|
774 if args.rules_selector != 'Custom':
|
|
|
775 genes = data_gene(dataset, type_gene, name, None)
|
|
|
776 ids, rules = load_id_rules(recon.get(type_gene))
|
|
|
777 elif args.rules_selector == 'Custom':
|
|
|
778 genes = data_gene(dataset, type_gene, name, gene_in_rule)
|
|
35
|
779
|
|
28
|
780 resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)
|
|
|
781
|
|
35
|
782 create_ras(resolve_rules, name, True, rules, ids)
|
|
28
|
783
|
|
|
784 if err != None and err:
|
|
|
785 warning('Warning: gene\n' + str(err) + '\nnot found in class '
|
|
|
786 + name + ', the expression level for this gene ' +
|
|
|
787 'will be considered NaN\n')
|
|
|
788
|
|
|
789 print('execution succeded')
|
|
|
790 return None
|
|
|
791
|
|
|
792
|
|
|
793 elif args.option == 'datasets':
|
|
0
|
794 num = 1
|
|
|
795 for i, j in zip(args.input_datas, args.names):
|
|
16
|
796
|
|
0
|
797 name = name_dataset(j, num)
|
|
|
798 dataset = read_dataset(i, name)
|
|
16
|
799
|
|
0
|
800 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
|
|
16
|
801
|
|
|
802 type_gene = gene_type(dataset.iloc[0, 0], name)
|
|
|
803
|
|
0
|
804 if args.rules_selector != 'Custom':
|
|
|
805 genes = data_gene(dataset, type_gene, name, None)
|
|
|
806 ids, rules = load_id_rules(recon.get(type_gene))
|
|
|
807 elif args.rules_selector == 'Custom':
|
|
|
808 genes = data_gene(dataset, type_gene, name, gene_in_rule)
|
|
16
|
809
|
|
35
|
810
|
|
0
|
811 resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)
|
|
16
|
812
|
|
|
813 if generate_ras:
|
|
35
|
814 create_ras(resolve_rules, name, False, rules, ids)
|
|
16
|
815
|
|
0
|
816 if err != None and err:
|
|
|
817 warning('Warning: gene\n' + str(err) + '\nnot found in class '
|
|
|
818 + name + ', the expression level for this gene ' +
|
|
|
819 'will be considered NaN\n')
|
|
|
820 if resolve_rules != None:
|
|
|
821 class_pat[name] = list(map(list, zip(*resolve_rules.values())))
|
|
|
822 num += 1
|
|
|
823 elif args.option == 'dataset_class':
|
|
|
824 name = 'RNAseq'
|
|
|
825 dataset = read_dataset(args.input_data, name)
|
|
|
826 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
|
|
|
827 type_gene = gene_type(dataset.iloc[0, 0], name)
|
|
|
828 classes = read_dataset(args.input_class, 'class')
|
|
|
829 if not len(classes.columns) == 2:
|
|
|
830 warning('Warning: more than 2 columns in class file. Extra' +
|
|
|
831 'columns have been disregarded\n')
|
|
|
832 classes = classes.astype(str)
|
|
|
833 if args.rules_selector != 'Custom':
|
|
|
834 genes = data_gene(dataset, type_gene, name, None)
|
|
|
835 ids, rules = load_id_rules(recon.get(type_gene))
|
|
|
836 elif args.rules_selector == 'Custom':
|
|
|
837 genes = data_gene(dataset, type_gene, name, gene_in_rule)
|
|
|
838 resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)
|
|
|
839 if err != None and err:
|
|
|
840 warning('Warning: gene\n'+str(err)+'\nnot found in class '
|
|
|
841 + name + ', the expression level for this gene ' +
|
|
|
842 'will be considered NaN\n')
|
|
|
843 if resolve_rules != None:
|
|
|
844 class_pat = split_class(classes, resolve_rules)
|
|
34
|
845 if generate_ras:
|
|
35
|
846 create_ras(resolve_rules, name, False, rules, ids)
|
|
28
|
847
|
|
|
848
|
|
0
|
849 if args.rules_selector == 'Custom':
|
|
|
850 if args.yes_no == 'yes':
|
|
|
851 try:
|
|
|
852 core_map = ET.parse(args.custom_map)
|
|
|
853 except (ET.XMLSyntaxError, ET.XMLSchemaParseError):
|
|
|
854 sys.exit('Execution aborted: custom map in wrong format')
|
|
|
855 elif args.yes_no == 'no':
|
|
|
856 core_map = ET.parse(args.tool_dir + '/local/HMRcoreMap.svg')
|
|
|
857 else:
|
|
|
858 core_map = ET.parse(args.tool_dir+'/local/HMRcoreMap.svg')
|
|
16
|
859
|
|
|
860 maps(core_map, class_pat, ids, args.pValue, args.fChange, create_svg, create_pdf)
|
|
|
861
|
|
|
862 print('Execution succeded')
|
|
|
863
|
|
0
|
864 return None
|
|
|
865
|
|
|
866 ###############################################################################
|
|
|
867
|
|
|
868 if __name__ == "__main__":
|
|
|
869 main()
|
