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1
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2 from __future__ import division
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3 import os
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4 import sys
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5 import pandas as pd
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6 import collections
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7 import pickle as pk
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8 import argparse
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9 from sklearn.cluster import KMeans
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10 import matplotlib.pyplot as plt
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11
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12 ########################## argparse ###########################################
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13
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14 def process_args(args):
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15 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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16 description = 'process some value\'s' +
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17 ' genes to create class.')
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18 parser.add_argument('-rs', '--rules_selector',
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19 type = str,
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20 default = 'HMRcore',
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21 choices = ['HMRcore', 'Recon', 'Custom'],
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22 help = 'chose which type of dataset you want use')
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23 parser.add_argument('-cr', '--custom',
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24 type = str,
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25 help='your dataset if you want custom rules')
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26 parser.add_argument('-ch', '--cond_hier',
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27 type = str,
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28 default = 'no',
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29 choices = ['no', 'yes'],
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30 help = 'chose if you wanna hierical dendrogram')
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31 parser.add_argument('-lk', '--k_min',
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32 type = int,
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33 help = 'min number of cluster')
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34 parser.add_argument('-uk', '--k_max',
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35 type = int,
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36 help = 'max number of cluster')
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37 parser.add_argument('-li', '--linkage',
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38 type = str,
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39 choices = ['single', 'complete', 'average'],
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40 help='linkage hierarchical cluster')
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41 parser.add_argument('-d', '--data',
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42 type = str,
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43 required = True,
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44 help = 'input dataset')
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45 parser.add_argument('-n', '--none',
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46 type = str,
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47 default = 'true',
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48 choices = ['true', 'false'],
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49 help = 'compute Nan values')
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50 parser.add_argument('-td', '--tool_dir',
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51 type = str,
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52 required = True,
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53 help = 'your tool directory')
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54 parser.add_argument('-na', '--name',
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55 type = str,
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56 help = 'name of dataset')
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57 parser.add_argument('-de', '--dendro',
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58 help = "Dendrogram out")
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59 parser.add_argument('-ol', '--out_log',
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60 help = "Output log")
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61 parser.add_argument('-el', '--elbow',
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62 help = "Out elbow")
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63 args = parser.parse_args()
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64 return args
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65
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66 ########################### warning ###########################################
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67
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68 def warning(s):
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69 args = process_args(sys.argv)
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70 with open(args.out_log, 'a') as log:
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71 log.write(s)
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72
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73 ############################ dataset input ####################################
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74
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75 def read_dataset(data, name):
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76 try:
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77 dataset = pd.read_csv(data, sep = '\t', header = 0)
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78 except pd.errors.EmptyDataError:
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79 sys.exit('Execution aborted: wrong format of '+name+'\n')
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80 if len(dataset.columns) < 2:
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81 sys.exit('Execution aborted: wrong format of '+name+'\n')
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82 return dataset
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83
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84 ############################ dataset name #####################################
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85
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86 def name_dataset(name_data, count):
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87 if str(name_data) == 'Dataset':
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88 return str(name_data) + '_' + str(count)
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89 else:
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90 return str(name_data)
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91
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92 ############################ load id e rules ##################################
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93
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94 def load_id_rules(reactions):
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95 ids, rules = [], []
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96 for key, value in reactions.items():
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97 ids.append(key)
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98 rules.append(value)
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99 return (ids, rules)
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100
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101 ############################ check_methods ####################################
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102
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103 def gene_type(l, name):
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104 if check_hgnc(l):
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105 return 'hugo_id'
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106 elif check_ensembl(l):
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107 return 'ensembl_gene_id'
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108 elif check_symbol(l):
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109 return 'symbol'
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110 elif check_entrez(l):
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111 return 'entrez_id'
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112 else:
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113 sys.exit('Execution aborted:\n' +
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114 'gene ID type in ' + name + ' not supported. Supported ID' +
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115 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n')
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116
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117 def check_hgnc(l):
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118 if len(l) > 5:
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119 if (l.upper()).startswith('HGNC:'):
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120 return l[5:].isdigit()
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121 else:
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122 return False
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123 else:
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124 return False
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125
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126 def check_ensembl(l):
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127 if len(l) == 15:
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128 if (l.upper()).startswith('ENS'):
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129 return l[4:].isdigit()
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130 else:
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131 return False
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132 else:
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133 return False
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134
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135 def check_symbol(l):
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136 if len(l) > 0:
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137 if l[0].isalpha() and l[1:].isalnum():
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138 return True
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139 else:
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140 return False
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141 else:
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142 return False
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143
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144 def check_entrez(l):
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145 if len(l) > 0:
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146 return l.isdigit()
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147 else:
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148 return False
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149
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150 def check_bool(b):
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151 if b == 'true':
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152 return True
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153 elif b == 'false':
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154 return False
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155
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156 ############################ make recon #######################################
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157
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158 def check_and_doWord(l):
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159 tmp = []
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160 tmp_genes = []
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161 count = 0
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162 while l:
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163 if count >= 0:
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164 if l[0] == '(':
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165 count += 1
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166 tmp.append(l[0])
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167 l.pop(0)
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168 elif l[0] == ')':
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169 count -= 1
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170 tmp.append(l[0])
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171 l.pop(0)
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172 elif l[0] == ' ':
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173 l.pop(0)
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174 else:
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175 word = []
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176 while l:
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177 if l[0] in [' ', '(', ')']:
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178 break
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179 else:
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180 word.append(l[0])
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181 l.pop(0)
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182 word = ''.join(word)
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183 tmp.append(word)
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184 if not(word in ['or', 'and']):
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185 tmp_genes.append(word)
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186 else:
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187 return False
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188 if count == 0:
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189 return (tmp, tmp_genes)
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190 else:
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191 return False
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192
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193 def brackets_to_list(l):
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194 tmp = []
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195 while l:
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196 if l[0] == '(':
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197 l.pop(0)
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198 tmp.append(resolve_brackets(l))
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199 else:
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200 tmp.append(l[0])
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201 l.pop(0)
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202 return tmp
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203
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204 def resolve_brackets(l):
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205 tmp = []
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206 while l[0] != ')':
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207 if l[0] == '(':
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208 l.pop(0)
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209 tmp.append(resolve_brackets(l))
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210 else:
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211 tmp.append(l[0])
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212 l.pop(0)
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213 l.pop(0)
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214 return tmp
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215
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216 def priorityAND(l):
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217 tmp = []
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218 flag = True
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219 while l:
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220 if len(l) == 1:
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221 if isinstance(l[0], list):
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222 tmp.append(priorityAND(l[0]))
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223 else:
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224 tmp.append(l[0])
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225 l = l[1:]
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226 elif l[0] == 'or':
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227 tmp.append(l[0])
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228 flag = False
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229 l = l[1:]
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230 elif l[1] == 'or':
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231 if isinstance(l[0], list):
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232 tmp.append(priorityAND(l[0]))
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233 else:
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234 tmp.append(l[0])
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235 tmp.append(l[1])
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236 flag = False
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237 l = l[2:]
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238 elif l[1] == 'and':
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239 tmpAnd = []
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240 if isinstance(l[0], list):
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241 tmpAnd.append(priorityAND(l[0]))
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242 else:
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243 tmpAnd.append(l[0])
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244 tmpAnd.append(l[1])
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245 if isinstance(l[2], list):
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246 tmpAnd.append(priorityAND(l[2]))
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247 else:
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248 tmpAnd.append(l[2])
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249 l = l[3:]
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250 while l:
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251 if l[0] == 'and':
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252 tmpAnd.append(l[0])
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253 if isinstance(l[1], list):
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254 tmpAnd.append(priorityAND(l[1]))
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255 else:
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256 tmpAnd.append(l[1])
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257 l = l[2:]
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258 elif l[0] == 'or':
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259 flag = False
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260 break
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261 if flag == True: #se ci sono solo AND nella lista
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262 tmp.extend(tmpAnd)
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263 elif flag == False:
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264 tmp.append(tmpAnd)
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265 return tmp
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266
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267 def checkRule(l):
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268 if len(l) == 1:
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269 if isinstance(l[0], list):
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270 if checkRule(l[0]) is False:
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271 return False
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272 elif len(l) > 2:
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273 if checkRule2(l) is False:
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274 return False
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275 else:
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276 return False
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277 return True
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278
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279 def checkRule2(l):
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280 while l:
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281 if len(l) == 1:
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282 return False
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283 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
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284 if checkRule(l[0]) is False:
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285 return False
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286 if isinstance(l[2], list):
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287 if checkRule(l[2]) is False:
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288 return False
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289 l = l[3:]
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290 elif l[1] in ['and', 'or']:
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291 if isinstance(l[2], list):
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292 if checkRule(l[2]) is False:
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293 return False
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294 l = l[3:]
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295 elif l[0] in ['and', 'or']:
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296 if isinstance(l[1], list):
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297 if checkRule(l[1]) is False:
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298 return False
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299 l = l[2:]
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300 else:
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301 return False
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302 return True
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303
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304 def do_rules(rules):
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305 split_rules = []
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306 err_rules = []
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307 tmp_gene_in_rule = []
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308 for i in range(len(rules)):
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309 tmp = list(rules[i])
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310 if tmp:
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311 tmp, tmp_genes = check_and_doWord(tmp)
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312 tmp_gene_in_rule.extend(tmp_genes)
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313 if tmp is False:
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314 split_rules.append([])
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315 err_rules.append(rules[i])
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316 else:
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317 tmp = brackets_to_list(tmp)
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318 if checkRule(tmp):
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319 split_rules.append(priorityAND(tmp))
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320 else:
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321 split_rules.append([])
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322 err_rules.append(rules[i])
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323 else:
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324 split_rules.append([])
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325 if err_rules:
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326 warning('Warning: wrong format rule in ' + str(err_rules) + '\n')
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327 return (split_rules, list(set(tmp_gene_in_rule)))
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328
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329 def make_recon(data):
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330 try:
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331 import cobra as cb
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332 import warnings
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333 with warnings.catch_warnings():
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334 warnings.simplefilter('ignore')
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335 recon = cb.io.read_sbml_model(data)
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336 react = recon.reactions
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337 rules = [react[i].gene_reaction_rule for i in range(len(react))]
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338 ids = [react[i].id for i in range(len(react))]
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339 except cb.io.sbml3.CobraSBMLError:
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340 try:
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341 data = (pd.read_csv(data, sep = '\t', dtype = str)).fillna('')
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342 if len(data.columns) < 2:
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343 sys.exit('Execution aborted: wrong format of ' +
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344 'custom GPR rules\n')
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345 if not len(data.columns) == 2:
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346 warning('WARNING: more than 2 columns in custom GPR rules.\n' +
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347 'Extra columns have been disregarded\n')
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348 ids = list(data.iloc[:, 0])
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349 rules = list(data.iloc[:, 1])
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350 except pd.errors.EmptyDataError:
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351 sys.exit('Execution aborted: wrong format of custom GPR rules\n')
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352 except pd.errors.ParserError:
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353 sys.exit('Execution aborted: wrong format of custom GPR rules\n')
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354 split_rules, tmp_genes = do_rules(rules)
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355 gene_in_rule = {}
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356 for i in tmp_genes:
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357 gene_in_rule[i] = 'ok'
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358 return (ids, split_rules, gene_in_rule)
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359
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360 ############################ resolve_methods ##################################
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361
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362 def replace_gene_value(l, d):
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363 tmp = []
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364 err = []
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365 while l:
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366 if isinstance(l[0], list):
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367 tmp_rules, tmp_err = replace_gene_value(l[0], d)
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368 tmp.append(tmp_rules)
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369 err.extend(tmp_err)
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370 else:
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371 value = replace_gene(l[0],d)
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372 tmp.append(value)
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373 if value == None:
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374 err.append(l[0])
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375 l = l[1:]
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376 return (tmp, err)
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377
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378 def replace_gene(l, d):
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379 if l =='and' or l == 'or':
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380 return l
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381 else:
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382 value = d.get(l, None)
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383 if not(value == None or isinstance(value, (int, float))):
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384 sys.exit('Execution aborted: ' + value + ' value not valid\n')
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385 return value
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386
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387 def compute(val1, op, val2, cn):
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388 if val1 != None and val2 != None:
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389 if op == 'and':
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390 return min(val1, val2)
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391 else:
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392 return val1 + val2
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393 elif op == 'and':
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394 if cn is True:
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395 if val1 != None:
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396 return val1
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397 elif val2 != None:
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398 return val2
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399 else:
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400 return None
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401 else:
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402 return None
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403 else:
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404 if val1 != None:
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405 return val1
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406 elif val2 != None:
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407 return val2
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408 else:
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409 return None
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410
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411 def control(ris, l, cn):
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412 if len(l) == 1:
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413 if isinstance(l[0], (float, int)) or l[0] == None:
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414 return l[0]
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415 elif isinstance(l[0], list):
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416 return control(None, l[0], cn)
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417 else:
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418 return False
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419 elif len(l) > 2:
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420 return control_list(ris, l, cn)
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421 else:
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422 return False
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423
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424 def control_list(ris, l, cn):
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425 while l:
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426 if len(l) == 1:
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427 return False
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428 elif (isinstance(l[0], (float, int)) or
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429 l[0] == None) and l[1] in ['and', 'or']:
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430 if isinstance(l[2], (float, int)) or l[2] == None:
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431 ris = compute(l[0], l[1], l[2], cn)
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432 elif isinstance(l[2], list):
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433 tmp = control(None, l[2], cn)
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434 if tmp is False:
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435 return False
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436 else:
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437 ris = compute(l[0], l[1], tmp, cn)
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438 else:
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439 return False
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440 l = l[3:]
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441 elif l[0] in ['and', 'or']:
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442 if isinstance(l[1], (float, int)) or l[1] == None:
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443 ris = compute(ris, l[0], l[1], cn)
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444 elif isinstance(l[1], list):
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445 tmp = control(None,l[1], cn)
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446 if tmp is False:
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447 return False
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448 else:
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449 ris = compute(ris, l[0], tmp, cn)
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450 else:
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451 return False
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452 l = l[2:]
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453 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
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454 if isinstance(l[2], (float, int)) or l[2] == None:
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455 tmp = control(None, l[0], cn)
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456 if tmp is False:
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457 return False
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458 else:
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459 ris = compute(tmp, l[1], l[2], cn)
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460 elif isinstance(l[2], list):
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461 tmp = control(None, l[0], cn)
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462 tmp2 = control(None, l[2], cn)
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463 if tmp is False or tmp2 is False:
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464 return False
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465 else:
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466 ris = compute(tmp, l[1], tmp2, cn)
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467 else:
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468 return False
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469 l = l[3:]
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470 else:
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471 return False
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472 return ris
|
|
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473
|
|
|
474 ############################ gene #############################################
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475
|
|
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476 def data_gene(gene, type_gene, name, gene_custom):
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477 args = process_args(sys.argv)
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|
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478 for i in range(len(gene)):
|
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479 tmp = gene.iloc[i, 0]
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480 if tmp.startswith(' ') or tmp.endswith(' '):
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481 gene.iloc[i, 0] = (tmp.lstrip()).rstrip()
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482 gene_dup = [item for item, count in
|
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483 collections.Counter(gene[gene.columns[0]]).items() if count > 1]
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|
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484 pat_dup = [item for item, count in
|
|
|
485 collections.Counter(list(gene.columns)).items() if count > 1]
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486 if gene_dup:
|
|
|
487 if gene_custom == None:
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|
|
488 if args.rules_selector == 'HMRcore':
|
|
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489 gene_in_rule = pk.load(open(args.tool_dir +
|
|
|
490 '/local/HMRcore_genes.p', 'rb'))
|
|
|
491 elif args.rules_selector == 'Recon':
|
|
|
492 gene_in_rule = pk.load(open(args.tool_dir +
|
|
|
493 '/local/Recon_genes.p', 'rb'))
|
|
|
494 gene_in_rule = gene_in_rule.get(type_gene)
|
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495 else:
|
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|
496 gene_in_rule = gene_custom
|
|
|
497 tmp = []
|
|
|
498 for i in gene_dup:
|
|
|
499 if gene_in_rule.get(i) == 'ok':
|
|
|
500 tmp.append(i)
|
|
|
501 if tmp:
|
|
|
502 sys.exit('Execution aborted because gene ID '
|
|
|
503 + str(tmp) + ' in ' + name + ' is duplicated\n')
|
|
|
504 if pat_dup:
|
|
|
505 sys.exit('Execution aborted: duplicated label\n'
|
|
|
506 + str(pat_dup) + 'in ' + name + '\n')
|
|
|
507 return (gene.set_index(gene.columns[0])).to_dict()
|
|
|
508
|
|
|
509 ############################ resolve ##########################################
|
|
|
510
|
|
|
511 def resolve(genes, rules, ids, resolve_none, name):
|
|
|
512 resolve_rules = {}
|
|
|
513 not_found = []
|
|
|
514 flag = False
|
|
|
515 for key, value in genes.items():
|
|
|
516 tmp_resolve = []
|
|
|
517 for i in range(len(rules)):
|
|
|
518 tmp = rules[i]
|
|
|
519 if tmp:
|
|
|
520 tmp, err = replace_gene_value(tmp, value)
|
|
|
521 if err:
|
|
|
522 not_found.extend(err)
|
|
|
523 ris = control(None, tmp, resolve_none)
|
|
|
524 if ris is False or ris == None:
|
|
|
525 tmp_resolve.append(None)
|
|
|
526 else:
|
|
|
527 tmp_resolve.append(ris)
|
|
|
528 flag = True
|
|
|
529 else:
|
|
|
530 tmp_resolve.append(None)
|
|
|
531 resolve_rules[key] = tmp_resolve
|
|
|
532 if flag is False:
|
|
|
533 sys.exit('Execution aborted: no computable score' +
|
|
|
534 ' (due to missing gene values) for class '
|
|
|
535 + name + ', the class has been disregarded\n')
|
|
|
536 return (resolve_rules, list(set(not_found)))
|
|
|
537
|
|
|
538 ################################# clustering ##################################
|
|
|
539
|
|
|
540 def f_cluster(resolve_rules):
|
|
|
541 os.makedirs('cluster_out')
|
|
|
542 args = process_args(sys.argv)
|
|
10
|
543 k_min = args.k_min
|
|
|
544 k_max = args.k_max
|
|
|
545 if k_min > k_max:
|
|
|
546 warning('k range boundaries inverted.\n')
|
|
|
547 tmp = k_min
|
|
|
548 k_min = k_max
|
|
|
549 k_max = tmp
|
|
|
550 else:
|
|
|
551 warning('k range correct.\n')
|
|
0
|
552 cluster_data = pd.DataFrame.from_dict(resolve_rules, orient = 'index')
|
|
|
553 for i in cluster_data.columns:
|
|
|
554 tmp = cluster_data[i][0]
|
|
|
555 if tmp == None:
|
|
|
556 cluster_data = cluster_data.drop(columns=[i])
|
|
|
557 distorsion = []
|
|
10
|
558 for i in range(k_min, k_max+1):
|
|
0
|
559 tmp_kmeans = KMeans(n_clusters = i,
|
|
|
560 n_init = 100,
|
|
|
561 max_iter = 300,
|
|
|
562 random_state = 0).fit(cluster_data)
|
|
|
563 distorsion.append(tmp_kmeans.inertia_)
|
|
|
564 predict = tmp_kmeans.predict(cluster_data)
|
|
|
565 predict = [x+1 for x in predict]
|
|
7
|
566 classe = (pd.DataFrame(list(zip(cluster_data.index, predict)))).astype(str)
|
|
0
|
567 dest = 'cluster_out/K=' + str(i) + '_' + args.name+'.tsv'
|
|
|
568 classe.to_csv(dest, sep = '\t', index = False,
|
|
|
569 header = ['Patient_ID', 'Class'])
|
|
|
570 plt.figure(0)
|
|
10
|
571 plt.plot(range(k_min, k_max+1), distorsion, marker = 'o')
|
|
0
|
572 plt.xlabel('Number of cluster')
|
|
|
573 plt.ylabel('Distorsion')
|
|
|
574 plt.savefig(args.elbow, dpi = 240, format = 'pdf')
|
|
|
575 if args.cond_hier == 'yes':
|
|
|
576 import scipy.cluster.hierarchy as hier
|
|
|
577 lin = hier.linkage(cluster_data, args.linkage)
|
|
|
578 plt.figure(1)
|
|
|
579 plt.figure(figsize=(10, 5))
|
|
|
580 hier.dendrogram(lin, leaf_font_size = 2, labels = cluster_data.index)
|
|
|
581 plt.savefig(args.dendro, dpi = 480, format = 'pdf')
|
|
|
582 return None
|
|
|
583
|
|
|
584 ################################# main ########################################
|
|
|
585
|
|
|
586 def main():
|
|
|
587 args = process_args(sys.argv)
|
|
|
588 if args.rules_selector == 'HMRcore':
|
|
|
589 recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb'))
|
|
|
590 elif args.rules_selector == 'Recon':
|
|
|
591 recon = pk.load(open(args.tool_dir + '/local/Recon_rules.p', 'rb'))
|
|
|
592 elif args.rules_selector == 'Custom':
|
|
|
593 ids, rules, gene_in_rule = make_recon(args.custom)
|
|
|
594 resolve_none = check_bool(args.none)
|
|
|
595 dataset = read_dataset(args.data, args.name)
|
|
|
596 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
|
|
|
597 type_gene = gene_type(dataset.iloc[0, 0], args.name)
|
|
|
598 if args.rules_selector != 'Custom':
|
|
|
599 genes = data_gene(dataset, type_gene, args.name, None)
|
|
|
600 ids, rules = load_id_rules(recon.get(type_gene))
|
|
|
601 elif args.rules_selector == 'Custom':
|
|
|
602 genes = data_gene(dataset, type_gene, args.name, gene_in_rule)
|
|
|
603 resolve_rules, err = resolve(genes, rules, ids, resolve_none, args.name)
|
|
|
604 if err:
|
|
|
605 warning('WARNING: gene\n' + str(err) + '\nnot found in class '
|
|
|
606 + args.name + ', the expression level for this gene ' +
|
|
|
607 'will be considered NaN\n')
|
|
|
608 f_cluster(resolve_rules)
|
|
|
609 warning('Execution succeeded')
|
|
|
610 return None
|
|
|
611
|
|
|
612 ###############################################################################
|
|
|
613
|
|
|
614 if __name__ == "__main__":
|
|
6
|
615 main()
|
