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489
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1 """
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2 Generate Reaction Activity Scores (RAS) from a gene expression dataset and GPR rules.
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3
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4 The script reads a tabular dataset (genes x samples) and a rules file (GPRs),
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5 computes RAS per reaction for each sample/cell line, and writes a tabular output.
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6 """
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93
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7 from __future__ import division
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8 import sys
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9 import argparse
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10 import collections
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11 import pandas as pd
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12 import pickle as pk
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13 import utils.general_utils as utils
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14 import utils.rule_parsing as ruleUtils
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15 from typing import Union, Optional, List, Dict, Tuple, TypeVar
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16
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17 ERRORS = []
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18 ########################## argparse ##########################################
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19 ARGS :argparse.Namespace
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147
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20 def process_args(args:List[str] = None) -> argparse.Namespace:
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93
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21 """
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22 Processes command-line arguments.
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23
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24 Args:
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25 args (list): List of command-line arguments.
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26
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27 Returns:
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28 Namespace: An object containing parsed arguments.
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29 """
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30 parser = argparse.ArgumentParser(
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31 usage = '%(prog)s [options]',
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32 description = "process some value's genes to create a comparison's map.")
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33
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489
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34 parser.add_argument("-rl", "--model_upload", type = str,
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35 help = "path to input file containing the rules")
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93
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36
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489
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37 parser.add_argument("-rn", "--model_upload_name", type = str, help = "custom rules name")
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38 # Galaxy converts files into .dat, this helps infer the original extension when needed.
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93
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39
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40 parser.add_argument(
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41 '-n', '--none',
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42 type = utils.Bool("none"), default = True,
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43 help = 'compute Nan values')
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44
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45 parser.add_argument(
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46 '-td', '--tool_dir',
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47 type = str,
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48 required = True, help = 'your tool directory')
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49
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50 parser.add_argument(
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51 '-ol', '--out_log',
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52 type = str,
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53 help = "Output log")
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54
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55 parser.add_argument(
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489
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56 '-in', '--input',
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93
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57 type = str,
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58 help = 'input dataset')
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59
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60 parser.add_argument(
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61 '-ra', '--ras_output',
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62 type = str,
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63 required = True, help = 'ras output')
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147
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64
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93
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65
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147
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66 return parser.parse_args(args)
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93
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67
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68 ############################ dataset input ####################################
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69 def read_dataset(data :str, name :str) -> pd.DataFrame:
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70 """
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71 Read a dataset from a CSV file and return it as a pandas DataFrame.
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72
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73 Args:
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74 data (str): Path to the CSV file containing the dataset.
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75 name (str): Name of the dataset, used in error messages.
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76
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77 Returns:
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78 pandas.DataFrame: DataFrame containing the dataset.
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79
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80 Raises:
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81 pd.errors.EmptyDataError: If the CSV file is empty.
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82 sys.exit: If the CSV file has the wrong format, the execution is aborted.
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83 """
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84 try:
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85 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
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86 except pd.errors.EmptyDataError:
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87 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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88 if len(dataset.columns) < 2:
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89 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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90 return dataset
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91
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92 ############################ load id e rules ##################################
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93 def load_id_rules(reactions :Dict[str, Dict[str, List[str]]]) -> Tuple[List[str], List[Dict[str, List[str]]]]:
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94 """
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95 Load IDs and rules from a dictionary of reactions.
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96
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97 Args:
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98 reactions (dict): A dictionary where keys are IDs and values are rules.
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99
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100 Returns:
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101 tuple: A tuple containing two lists, the first list containing IDs and the second list containing rules.
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102 """
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103 ids, rules = [], []
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104 for key, value in reactions.items():
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105 ids.append(key)
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106 rules.append(value)
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107 return (ids, rules)
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108
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109
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110 ############################ gene #############################################
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111 def data_gene(gene: pd.DataFrame, type_gene: str, name: str, gene_custom: Optional[Dict[str, str]]) -> Dict[str, str]:
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112 """
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113 Process gene data to ensure correct formatting and handle duplicates.
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114
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115 Args:
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116 gene (DataFrame): DataFrame containing gene data.
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117 type_gene (str): Type of gene data (e.g., 'hugo_id', 'ensembl_gene_id', 'symbol', 'entrez_id').
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118 name (str): Name of the dataset.
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119 gene_custom (dict or None): Custom gene data dictionary if provided.
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120
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121 Returns:
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122 dict: A dictionary containing gene data with gene IDs as keys and corresponding values.
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123 """
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309
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124
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93
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125 for i in range(len(gene)):
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126 tmp = gene.iloc[i, 0]
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127 gene.iloc[i, 0] = tmp.strip().split('.')[0]
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128
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129 gene_dup = [item for item, count in
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130 collections.Counter(gene[gene.columns[0]]).items() if count > 1]
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131 pat_dup = [item for item, count in
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132 collections.Counter(list(gene.columns)).items() if count > 1]
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260
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133
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134 gene_in_rule = None
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259
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135
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93
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136 if gene_dup:
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137 if gene_custom == None:
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264
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138
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309
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139 if str(ARGS.rules_selector) == 'HMRcore':
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140 gene_in_rule = pk.load(open(ARGS.tool_dir + '/local/pickle files/HMRcore_genes.p', 'rb'))
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93
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141
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309
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142 elif str(ARGS.rules_selector) == 'Recon':
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143 gene_in_rule = pk.load(open(ARGS.tool_dir + '/local/pickle files/Recon_genes.p', 'rb'))
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93
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144
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309
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145 elif str(ARGS.rules_selector) == 'ENGRO2':
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146 gene_in_rule = pk.load(open(ARGS.tool_dir + '/local/pickle files/ENGRO2_genes.p', 'rb'))
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263
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147
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309
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148 utils.logWarning(f"{ARGS.tool_dir}'/local/pickle files/ENGRO2_genes.p'", ARGS.out_log)
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259
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149
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93
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150 gene_in_rule = gene_in_rule.get(type_gene)
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151
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152 else:
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153 gene_in_rule = gene_custom
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260
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154
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93
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155 tmp = []
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156 for i in gene_dup:
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157 if gene_in_rule.get(i) == 'ok':
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158 tmp.append(i)
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159 if tmp:
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160 sys.exit('Execution aborted because gene ID '
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161 +str(tmp)+' in '+name+' is duplicated\n')
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162
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163 if pat_dup: utils.logWarning(f"Warning: duplicated label\n{pat_dup} in {name}", ARGS.out_log)
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164 return (gene.set_index(gene.columns[0])).to_dict()
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165
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166 ############################ resolve ##########################################
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167 def replace_gene_value(l :str, d :str) -> Tuple[Union[int, float], list]:
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168 """
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489
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169 Replace gene identifiers in a parsed rule expression with values from a dict.
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93
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170
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171 Args:
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489
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172 l: Parsed rule as a nested list structure (strings, lists, and operators).
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173 d: Dict mapping gene IDs to numeric values.
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93
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174
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175 Returns:
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489
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176 tuple: (new_expression, not_found_genes)
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93
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177 """
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178 tmp = []
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179 err = []
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180 while l:
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181 if isinstance(l[0], list):
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182 tmp_rules, tmp_err = replace_gene_value(l[0], d)
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183 tmp.append(tmp_rules)
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184 err.extend(tmp_err)
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185 else:
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186 value = replace_gene(l[0], d)
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187 tmp.append(value)
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188 if value == None:
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189 err.append(l[0])
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190 l = l[1:]
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191 return (tmp, err)
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192
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489
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193 def replace_gene(l: str, d: Dict[str, Union[int, float]]) -> Union[int, float, None]:
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93
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194 """
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195 Replace a single gene identifier with its corresponding value from a dictionary.
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196
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197 Args:
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198 l (str): Gene identifier to replace.
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489
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199 d (dict): Dict mapping gene IDs to numeric values.
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93
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200
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201 Returns:
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489
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202 float/int/None: Corresponding value from the dictionary if found, None otherwise.
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93
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203
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204 Raises:
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205 sys.exit: If the value associated with the gene identifier is not valid.
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206 """
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207 if l =='and' or l == 'or':
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208 return l
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209 else:
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210 value = d.get(l, None)
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211 if not(value == None or isinstance(value, (int, float))):
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212 sys.exit('Execution aborted: ' + value + ' value not valid\n')
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213 return value
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214
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215 T = TypeVar("T", bound = Optional[Union[int, float]])
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216 def computes(val1 :T, op :str, val2 :T, cn :bool) -> T:
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217 """
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218 Compute the RAS value between two value and an operator ('and' or 'or').
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219
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220 Args:
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221 val1(Optional(Union[float, int])): First value.
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222 op (str): Operator ('and' or 'or').
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223 val2(Optional(Union[float, int])): Second value.
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224 cn (bool): Control boolean value.
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225
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226 Returns:
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227 Optional(Union[float, int]): Result of the computation.
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228 """
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229 if val1 != None and val2 != None:
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230 if op == 'and':
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231 return min(val1, val2)
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232 else:
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233 return val1 + val2
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234 elif op == 'and':
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235 if cn is True:
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236 if val1 != None:
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237 return val1
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238 elif val2 != None:
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239 return val2
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240 else:
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241 return None
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242 else:
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243 return None
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244 else:
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245 if val1 != None:
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246 return val1
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247 elif val2 != None:
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248 return val2
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249 else:
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250 return None
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251
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252 # ris should be Literal[None] but Literal is not supported in Python 3.7
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253 def control(ris, l :List[Union[int, float, list]], cn :bool) -> Union[bool, int, float]: #Union[Literal[False], int, float]:
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254 """
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255 Control the format of the expression.
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256
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257 Args:
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258 ris: Intermediate result.
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259 l (list): Expression to control.
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260 cn (bool): Control boolean value.
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261
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262 Returns:
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263 Union[Literal[False], int, float]: Result of the control.
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264 """
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265 if len(l) == 1:
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266 if isinstance(l[0], (float, int)) or l[0] == None:
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267 return l[0]
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268 elif isinstance(l[0], list):
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269 return control(None, l[0], cn)
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270 else:
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271 return False
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272 elif len(l) > 2:
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273 return control_list(ris, l, cn)
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274 else:
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275 return False
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276
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277 def control_list(ris, l :List[Optional[Union[float, int, list]]], cn :bool) -> Optional[bool]: #Optional[Literal[False]]:
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278 """
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279 Control the format of a list of expressions.
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280
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281 Args:
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282 ris: Intermediate result.
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283 l (list): List of expressions to control.
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284 cn (bool): Control boolean value.
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285
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286 Returns:
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287 Optional[Literal[False]]: Result of the control.
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288 """
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289 while l:
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290 if len(l) == 1:
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291 return False
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292 elif (isinstance(l[0], (float, int)) or
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293 l[0] == None) and l[1] in ['and', 'or']:
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294 if isinstance(l[2], (float, int)) or l[2] == None:
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295 ris = computes(l[0], l[1], l[2], cn)
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296 elif isinstance(l[2], list):
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297 tmp = control(None, l[2], cn)
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298 if tmp is False:
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299 return False
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300 else:
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301 ris = computes(l[0], l[1], tmp, cn)
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302 else:
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303 return False
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304 l = l[3:]
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305 elif l[0] in ['and', 'or']:
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306 if isinstance(l[1], (float, int)) or l[1] == None:
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307 ris = computes(ris, l[0], l[1], cn)
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308 elif isinstance(l[1], list):
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309 tmp = control(None,l[1], cn)
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310 if tmp is False:
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311 return False
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312 else:
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313 ris = computes(ris, l[0], tmp, cn)
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314 else:
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315 return False
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316 l = l[2:]
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317 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
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318 if isinstance(l[2], (float, int)) or l[2] == None:
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319 tmp = control(None, l[0], cn)
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320 if tmp is False:
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321 return False
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322 else:
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323 ris = computes(tmp, l[1], l[2], cn)
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324 elif isinstance(l[2], list):
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325 tmp = control(None, l[0], cn)
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326 tmp2 = control(None, l[2], cn)
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327 if tmp is False or tmp2 is False:
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328 return False
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329 else:
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330 ris = computes(tmp, l[1], tmp2, cn)
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331 else:
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332 return False
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333 l = l[3:]
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334 else:
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335 return False
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336 return ris
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337
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338 ResolvedRules = Dict[str, List[Optional[Union[float, int]]]]
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339 def resolve(genes: Dict[str, str], rules: List[str], ids: List[str], resolve_none: bool, name: str) -> Tuple[Optional[ResolvedRules], Optional[list]]:
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340 """
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341 Resolve rules using gene data to compute scores for each rule.
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342
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343 Args:
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344 genes (dict): Dictionary containing gene data with gene IDs as keys and corresponding values.
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345 rules (list): List of rules to resolve.
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346 ids (list): List of IDs corresponding to the rules.
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347 resolve_none (bool): Flag indicating whether to resolve None values in the rules.
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348 name (str): Name of the dataset.
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349
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350 Returns:
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351 tuple: A tuple containing resolved rules as a dictionary and a list of gene IDs not found in the data.
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352 """
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353 resolve_rules = {}
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354 not_found = []
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355 flag = False
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356 for key, value in genes.items():
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357 tmp_resolve = []
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358 for i in range(len(rules)):
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359 tmp = rules[i]
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360 if tmp:
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361 tmp, err = replace_gene_value(tmp, value)
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362 if err:
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363 not_found.extend(err)
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364 ris = control(None, tmp, resolve_none)
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365 if ris is False or ris == None:
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366 tmp_resolve.append(None)
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367 else:
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368 tmp_resolve.append(ris)
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369 flag = True
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370 else:
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371 tmp_resolve.append(None)
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372 resolve_rules[key] = tmp_resolve
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373
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374 if flag is False:
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375 utils.logWarning(
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376 f"Warning: no computable score (due to missing gene values) for class {name}, the class has been disregarded",
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377 ARGS.out_log)
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378
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379 return (None, None)
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380
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381 return (resolve_rules, list(set(not_found)))
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382 ############################ create_ras #######################################
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383 def create_ras(resolve_rules: Optional[ResolvedRules], dataset_name: str, rules: List[str], ids: List[str], file: str) -> None:
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384 """
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385 Create a RAS (Reaction Activity Score) file from resolved rules.
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386
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387 Args:
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388 resolve_rules (dict): Dictionary containing resolved rules.
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389 dataset_name (str): Name of the dataset.
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390 rules (list): List of rules.
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391 file (str): Path to the output RAS file.
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392
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393 Returns:
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394 None
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395 """
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396 if resolve_rules is None:
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397 utils.logWarning(f"Couldn't generate RAS for current dataset: {dataset_name}", ARGS.out_log)
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398
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399 for geni in resolve_rules.values():
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400 for i, valori in enumerate(geni):
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401 if valori == None:
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402 geni[i] = 'None'
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403
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404 output_ras = pd.DataFrame.from_dict(resolve_rules)
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405
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406 output_ras.insert(0, 'Reactions', ids)
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407 output_to_csv = pd.DataFrame.to_csv(output_ras, sep = '\t', index = False)
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408
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409 text_file = open(file, "w")
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410
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411 text_file.write(output_to_csv)
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412 text_file.close()
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413
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414 ################################- NEW RAS COMPUTATION -################################
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415 Expr = Optional[Union[int, float]]
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416 Ras = Expr
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417 def ras_for_cell_lines(dataset: pd.DataFrame, rules: Dict[str, ruleUtils.OpList]) -> Dict[str, Dict[str, Ras]]:
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418 """
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419 Generates the RAS scores for each cell line found in the dataset.
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420
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421 Args:
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422 dataset (pd.DataFrame): Dataset containing gene values.
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423 rules (dict): The dict containing reaction ids as keys and rules as values.
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489
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424
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425 Note:
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426 Modifies dataset in place by setting the first column as index.
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93
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427
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428 Returns:
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429 dict: A dictionary where each key corresponds to a cell line name and each value is a dictionary
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430 where each key corresponds to a reaction ID and each value is its computed RAS score.
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431 """
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432 ras_values_by_cell_line = {}
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433 dataset.set_index(dataset.columns[0], inplace=True)
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489
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434
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435 for cell_line_name in dataset.columns: #[1:]:
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93
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436 cell_line = dataset[cell_line_name].to_dict()
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437 ras_values_by_cell_line[cell_line_name]= get_ras_values(rules, cell_line)
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438 return ras_values_by_cell_line
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439
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440 def get_ras_values(value_rules: Dict[str, ruleUtils.OpList], dataset: Dict[str, Expr]) -> Dict[str, Ras]:
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441 """
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442 Computes the RAS (Reaction Activity Score) values for each rule in the given dict.
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443
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444 Args:
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445 value_rules (dict): A dictionary where keys are reaction ids and values are OpLists.
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446 dataset : gene expression data of one cell line.
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447
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448 Returns:
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449 dict: A dictionary where keys are reaction ids and values are the computed RAS values for each rule.
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|
|
450 """
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|
|
451 return {key: ras_op_list(op_list, dataset) for key, op_list in value_rules.items()}
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452
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|
453 def get_gene_expr(dataset :Dict[str, Expr], name :str) -> Expr:
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454 """
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455 Extracts the gene expression of the given gene from a cell line dataset.
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456
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457 Args:
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|
458 dataset : gene expression data of one cell line.
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|
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459 name : gene name.
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460
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|
461 Returns:
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|
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462 Expr : the gene's expression value.
|
|
|
463 """
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|
|
464 expr = dataset.get(name, None)
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465 if expr is None: ERRORS.append(name)
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466
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|
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467 return expr
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468
|
|
|
469 def ras_op_list(op_list: ruleUtils.OpList, dataset: Dict[str, Expr]) -> Ras:
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|
470 """
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|
471 Computes recursively the RAS (Reaction Activity Score) value for the given OpList, considering the specified flag to control None behavior.
|
|
|
472
|
|
|
473 Args:
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|
|
474 op_list (OpList): The OpList representing a rule with gene values.
|
|
|
475 dataset : gene expression data of one cell line.
|
|
|
476
|
|
|
477 Returns:
|
|
|
478 Ras: The computed RAS value for the given OpList.
|
|
|
479 """
|
|
|
480 op = op_list.op
|
|
|
481 ras_value :Ras = None
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|
|
482 if not op: return get_gene_expr(dataset, op_list[0])
|
|
|
483 if op is ruleUtils.RuleOp.AND and not ARGS.none and None in op_list: return None
|
|
|
484
|
|
|
485 for i in range(len(op_list)):
|
|
|
486 item = op_list[i]
|
|
|
487 if isinstance(item, ruleUtils.OpList):
|
|
|
488 item = ras_op_list(item, dataset)
|
|
|
489
|
|
|
490 else:
|
|
|
491 item = get_gene_expr(dataset, item)
|
|
|
492
|
|
|
493 if item is None:
|
|
|
494 if op is ruleUtils.RuleOp.AND and not ARGS.none: return None
|
|
|
495 continue
|
|
|
496
|
|
|
497 if ras_value is None:
|
|
|
498 ras_value = item
|
|
|
499 else:
|
|
|
500 ras_value = ras_value + item if op is ruleUtils.RuleOp.OR else min(ras_value, item)
|
|
|
501
|
|
|
502 return ras_value
|
|
|
503
|
|
|
504 def save_as_tsv(rasScores: Dict[str, Dict[str, Ras]], reactions :List[str]) -> None:
|
|
|
505 """
|
|
489
|
506 Save computed RAS scores to ARGS.ras_output as a TSV file.
|
|
93
|
507
|
|
|
508 Args:
|
|
|
509 rasScores : the computed ras scores.
|
|
489
|
510 reactions : the list of reaction IDs, used as the first column.
|
|
93
|
511
|
|
|
512 Returns:
|
|
|
513 None
|
|
|
514 """
|
|
|
515 for scores in rasScores.values(): # this is actually a lot faster than using the ootb dataframe metod, sadly
|
|
|
516 for reactId, score in scores.items():
|
|
|
517 if score is None: scores[reactId] = "None"
|
|
|
518
|
|
|
519 output_ras = pd.DataFrame.from_dict(rasScores)
|
|
|
520 output_ras.insert(0, 'Reactions', reactions)
|
|
|
521 output_ras.to_csv(ARGS.ras_output, sep = '\t', index = False)
|
|
|
522
|
|
|
523 ############################ MAIN #############################################
|
|
|
524 #TODO: not used but keep, it will be when the new translator dicts will be used.
|
|
|
525 def translateGene(geneName :str, encoding :str, geneTranslator :Dict[str, Dict[str, str]]) -> str:
|
|
|
526 """
|
|
|
527 Translate gene from any supported encoding to HugoID.
|
|
|
528
|
|
|
529 Args:
|
|
|
530 geneName (str): the name of the gene in its current encoding.
|
|
|
531 encoding (str): the encoding.
|
|
|
532 geneTranslator (Dict[str, Dict[str, str]]): the dict containing all supported gene names
|
|
|
533 and encodings in the current model, mapping each to the corresponding HugoID encoding.
|
|
|
534
|
|
|
535 Raises:
|
|
|
536 ValueError: When the gene isn't supported in the model.
|
|
|
537
|
|
|
538 Returns:
|
|
|
539 str: the gene in HugoID encoding.
|
|
|
540 """
|
|
|
541 supportedGenesInEncoding = geneTranslator[encoding]
|
|
|
542 if geneName in supportedGenesInEncoding: return supportedGenesInEncoding[geneName]
|
|
489
|
543 raise ValueError(f"Gene '{geneName}' not found. Please verify you are using the correct model.")
|
|
93
|
544
|
|
|
545 def load_custom_rules() -> Dict[str, ruleUtils.OpList]:
|
|
|
546 """
|
|
|
547 Opens custom rules file and extracts the rules. If the file is in .csv format an additional parsing step will be
|
|
|
548 performed, significantly impacting the runtime.
|
|
|
549
|
|
|
550 Returns:
|
|
|
551 Dict[str, ruleUtils.OpList] : dict mapping reaction IDs to rules.
|
|
|
552 """
|
|
489
|
553 datFilePath = utils.FilePath.fromStrPath(ARGS.model_upload) # actual file, stored in Galaxy as a .dat
|
|
|
554
|
|
|
555 dict_rule = {}
|
|
|
556
|
|
|
557 try:
|
|
|
558 rows = utils.readCsv(datFilePath, delimiter = "\t", skipHeader=False)
|
|
|
559 if len(rows) <= 1:
|
|
|
560 raise ValueError("Model tabular with 1 column is not supported.")
|
|
381
|
561
|
|
489
|
562 if not rows:
|
|
|
563 raise ValueError("Model tabular is file is empty.")
|
|
|
564
|
|
|
565 id_idx, idx_gpr = utils.findIdxByName(rows[0], "GPR")
|
|
|
566
|
|
|
567 # First, try using a tab delimiter
|
|
|
568 for line in rows[1:]:
|
|
|
569 if len(line) <= idx_gpr:
|
|
|
570 utils.logWarning(f"Skipping malformed line: {line}", ARGS.out_log)
|
|
|
571 continue
|
|
|
572
|
|
|
573 if line[idx_gpr] == "":
|
|
|
574 dict_rule[line[id_idx]] = ruleUtils.OpList([""])
|
|
|
575 else:
|
|
|
576 dict_rule[line[id_idx]] = ruleUtils.parseRuleToNestedList(line[idx_gpr])
|
|
|
577
|
|
|
578 except Exception as e:
|
|
|
579 # If parsing with tabs fails, try comma delimiter
|
|
|
580 try:
|
|
|
581 rows = utils.readCsv(datFilePath, delimiter = ",", skipHeader=False)
|
|
|
582
|
|
|
583 if len(rows) <= 1:
|
|
|
584 raise ValueError("Model tabular with 1 column is not supported.")
|
|
|
585
|
|
|
586 if not rows:
|
|
|
587 raise ValueError("Model tabular is file is empty.")
|
|
|
588
|
|
|
589 id_idx, idx_gpr = utils.findIdxByName(rows[0], "GPR")
|
|
|
590
|
|
|
591 # Try again parsing row content with the GPR column using comma-separated values
|
|
|
592 for line in rows[1:]:
|
|
|
593 if len(line) <= idx_gpr:
|
|
|
594 utils.logWarning(f"Skipping malformed line: {line}", ARGS.out_log)
|
|
|
595 continue
|
|
|
596
|
|
|
597 if line[idx_gpr] == "":
|
|
|
598 dict_rule[line[id_idx]] = ruleUtils.OpList([""])
|
|
|
599 else:
|
|
|
600 dict_rule[line[id_idx]] = ruleUtils.parseRuleToNestedList(line[idx_gpr])
|
|
|
601
|
|
|
602 except Exception as e2:
|
|
|
603 raise ValueError(f"Unable to parse rules file. Tried both tab and comma delimiters. Original errors: Tab: {e}, Comma: {e2}")
|
|
|
604
|
|
|
605 if not dict_rule:
|
|
|
606 raise ValueError("No valid rules found in the uploaded file. Please check the file format.")
|
|
93
|
607 # csv rules need to be parsed, those in a pickle format are taken to be pre-parsed.
|
|
489
|
608 return dict_rule
|
|
|
609
|
|
401
|
610
|
|
147
|
611 def main(args:List[str] = None) -> None:
|
|
93
|
612 """
|
|
|
613 Initializes everything and sets the program in motion based on the fronted input arguments.
|
|
|
614
|
|
|
615 Returns:
|
|
|
616 None
|
|
|
617 """
|
|
|
618 # get args from frontend (related xml)
|
|
|
619 global ARGS
|
|
147
|
620 ARGS = process_args(args)
|
|
309
|
621
|
|
93
|
622 # read dataset
|
|
|
623 dataset = read_dataset(ARGS.input, "dataset")
|
|
|
624 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
|
|
|
625
|
|
|
626 # remove versioning from gene names
|
|
|
627 dataset.iloc[:, 0] = dataset.iloc[:, 0].str.split('.').str[0]
|
|
|
628
|
|
489
|
629 rules = load_custom_rules()
|
|
|
630 reactions = list(rules.keys())
|
|
93
|
631
|
|
489
|
632 save_as_tsv(ras_for_cell_lines(dataset, rules), reactions)
|
|
|
633 if ERRORS: utils.logWarning(
|
|
|
634 f"The following genes are mentioned in the rules but don't appear in the dataset: {ERRORS}",
|
|
|
635 ARGS.out_log)
|
|
381
|
636
|
|
489
|
637
|
|
|
638 print("Execution succeeded")
|
|
93
|
639
|
|
|
640 ###############################################################################
|
|
|
641 if __name__ == "__main__":
|
|
309
|
642 main()
|
