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1 import argparse
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2 import utils.general_utils as utils
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3 from typing import Optional, List
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4 import os
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5 import numpy as np
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6 import pandas as pd
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7 import cobra
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8 import sys
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9 import csv
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10 from joblib import Parallel, delayed, cpu_count
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11
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12 ################################# process args ###############################
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13 def process_args(args :List[str]) -> argparse.Namespace:
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14 """
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15 Processes command-line arguments.
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16
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17 Args:
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18 args (list): List of command-line arguments.
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19
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20 Returns:
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21 Namespace: An object containing parsed arguments.
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22 """
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23 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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24 description = 'process some value\'s')
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25
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26 parser.add_argument(
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27 '-ms', '--model_selector',
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28 type = utils.Model, default = utils.Model.ENGRO2, choices = [utils.Model.ENGRO2, utils.Model.Custom],
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29 help = 'chose which type of model you want use')
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30
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31 parser.add_argument("-mo", "--model", type = str,
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32 help = "path to input file with custom rules, if provided")
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33
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34 parser.add_argument("-mn", "--model_name", type = str, help = "custom mode name")
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35
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36 parser.add_argument(
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37 '-mes', '--medium_selector',
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38 default = "allOpen",
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39 help = 'chose which type of medium you want use')
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40
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41 parser.add_argument("-meo", "--medium", type = str,
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42 help = "path to input file with custom medium, if provided")
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43
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44 parser.add_argument('-ol', '--out_log',
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45 help = "Output log")
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46
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47 parser.add_argument('-td', '--tool_dir',
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48 type = str,
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49 required = True,
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50 help = 'your tool directory')
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51
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52 parser.add_argument('-ir', '--input_ras',
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53 type=str,
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54 required = False,
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55 help = 'input ras')
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56
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57 parser.add_argument('-rs', '--ras_selector',
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58 required = True,
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59 type=utils.Bool("using_RAS"),
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60 help = 'ras selector')
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48
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61
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62 parser.add_argument('-cc', '--cell_class',
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63 type = str,
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64 help = 'output of cell class')
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65
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4
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66 ARGS = parser.parse_args()
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67 return ARGS
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68
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69 ########################### warning ###########################################
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70 def warning(s :str) -> None:
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71 """
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72 Log a warning message to an output log file and print it to the console.
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73
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74 Args:
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75 s (str): The warning message to be logged and printed.
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76
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77 Returns:
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78 None
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79 """
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80 with open(ARGS.out_log, 'a') as log:
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81 log.write(s + "\n\n")
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82 print(s)
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83
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84 ############################ dataset input ####################################
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85 def read_dataset(data :str, name :str) -> pd.DataFrame:
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86 """
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87 Read a dataset from a CSV file and return it as a pandas DataFrame.
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88
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89 Args:
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90 data (str): Path to the CSV file containing the dataset.
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91 name (str): Name of the dataset, used in error messages.
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92
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93 Returns:
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94 pandas.DataFrame: DataFrame containing the dataset.
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95
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96 Raises:
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97 pd.errors.EmptyDataError: If the CSV file is empty.
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98 sys.exit: If the CSV file has the wrong format, the execution is aborted.
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99 """
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100 try:
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101 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
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102 except pd.errors.EmptyDataError:
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103 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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104 if len(dataset.columns) < 2:
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105 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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106 return dataset
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107
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108
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109 def apply_ras_bounds(model, ras_row, rxns_ids):
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110 """
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111 Adjust the bounds of reactions in the model based on RAS values.
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112
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113 Args:
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114 model (cobra.Model): The metabolic model to be modified.
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115 ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
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116 rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
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117
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118 Returns:
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119 None
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120 """
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121 for reaction in rxns_ids:
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48
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122 if reaction in ras_row.index:
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4
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123 scaling_factor = ras_row[reaction]
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48
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124 lower_bound=model.reactions.get_by_id(reaction).lower_bound
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125 upper_bound=model.reactions.get_by_id(reaction).upper_bound
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63
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126 warning("Reaction: "+reaction+" Lower Bound: "+str(lower_bound)+" Upper Bound: "+str(upper_bound)+" Scaling Factor: "+str(scaling_factor))
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48
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127 valMax=float((upper_bound)*scaling_factor)
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128 valMin=float((lower_bound)*scaling_factor)
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129 if upper_bound!=0 and lower_bound==0:
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130 model.reactions.get_by_id(reaction).upper_bound=valMax
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131 if upper_bound==0 and lower_bound!=0:
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132 model.reactions.get_by_id(reaction).lower_bound=valMin
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133 if upper_bound!=0 and lower_bound!=0:
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134 model.reactions.get_by_id(reaction).lower_bound=valMin
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135 model.reactions.get_by_id(reaction).upper_bound=valMax
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136 pass
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4
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137
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138 def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder):
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139 """
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140 Process a single RAS cell, apply bounds, and save the bounds to a CSV file.
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141
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142 Args:
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143 cellName (str): The name of the RAS cell (used for naming the output file).
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144 ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
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145 model (cobra.Model): The metabolic model to be modified.
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146 rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
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147 output_folder (str): Folder path where the output CSV file will be saved.
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148
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149 Returns:
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150 None
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151 """
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152 model_new = model.copy()
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153 apply_ras_bounds(model_new, ras_row, rxns_ids)
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154 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
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155 bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
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48
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156 pass
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4
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157
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158 def generate_bounds(model: cobra.Model, medium: dict, ras=None, output_folder='output/') -> pd.DataFrame:
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159 """
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160 Generate reaction bounds for a metabolic model based on medium conditions and optional RAS adjustments.
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161
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162 Args:
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163 model (cobra.Model): The metabolic model for which bounds will be generated.
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164 medium (dict): A dictionary where keys are reaction IDs and values are the medium conditions.
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48
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165 ras (pd.DataFrame, optional): RAS pandas dataframe. Defaults to None.
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4
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166 output_folder (str, optional): Folder path where output CSV files will be saved. Defaults to 'output/'.
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167
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168 Returns:
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169 pd.DataFrame: DataFrame containing the bounds of reactions in the model.
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170 """
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171 rxns_ids = [rxn.id for rxn in model.reactions]
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172
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173 # Set medium conditions
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174 for reaction, value in medium.items():
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175 if value is not None:
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176 model.reactions.get_by_id(reaction).lower_bound = -float(value)
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177
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178 # Perform Flux Variability Analysis (FVA)
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179 df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
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180
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181 # Set FVA bounds
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182 for reaction in rxns_ids:
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183 rxn = model.reactions.get_by_id(reaction)
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184 rxn.lower_bound = float(df_FVA.loc[reaction, "minimum"])
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185 rxn.upper_bound = float(df_FVA.loc[reaction, "maximum"])
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186
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187 if ras is not None:
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62
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188 #Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
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189 for cellName, ras_row in ras.iterrows():
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190 process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder)
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4
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191 else:
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192 model_new = model.copy()
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193 apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids)
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194 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
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195 bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
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48
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196 pass
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197
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4
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198
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199
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200 ############################# main ###########################################
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201 def main() -> None:
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202 """
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203 Initializes everything and sets the program in motion based on the fronted input arguments.
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204
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205 Returns:
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206 None
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207 """
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208 if not os.path.exists('ras_to_bounds'):
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209 os.makedirs('ras_to_bounds')
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210
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211
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212 global ARGS
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213 ARGS = process_args(sys.argv)
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214
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215 ARGS.output_folder = 'ras_to_bounds/'
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216
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217 if(ARGS.ras_selector == True):
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54
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218 ras_file_list = ARGS.input_ras.split(",")
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60
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219 ras_class_names = []
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220 for file in ras_file_list:
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221 ras_class_names.append(file.split(".")[0])
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48
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222 ras_list = []
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223 class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
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56
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224 for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
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225 ras = read_dataset(ras_matrix, "ras dataset")
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226 ras.replace("None", None, inplace=True)
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227 ras.set_index("Reactions", drop=True, inplace=True)
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228 ras = ras.T
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229 ras = ras.astype(float)
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230 ras_list.append(ras)
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231 for patient_id in ras.index:
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61
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232 class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name]
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58
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233
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48
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234
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235 # Concatenate all ras DataFrames into a single DataFrame
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236 ras_combined = pd.concat(ras_list, axis=1)
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237 # Normalize the RAS values by max RAS
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238 ras_combined = ras_combined.div(ras_combined.max(axis=0))
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239 ras_combined = ras_combined.fillna(0)
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240
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241
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4
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242
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243 model_type :utils.Model = ARGS.model_selector
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244 if model_type is utils.Model.Custom:
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245 model = model_type.getCOBRAmodel(customPath = utils.FilePath.fromStrPath(ARGS.model), customExtension = utils.FilePath.fromStrPath(ARGS.model_name).ext)
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246 else:
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247 model = model_type.getCOBRAmodel(toolDir=ARGS.tool_dir)
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248
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249 if(ARGS.medium_selector == "Custom"):
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250 medium = read_dataset(ARGS.medium, "medium dataset")
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251 medium.set_index(medium.columns[0], inplace=True)
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252 medium = medium.astype(float)
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253 medium = medium[medium.columns[0]].to_dict()
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254 else:
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255 df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
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256 ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
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257 medium = df_mediums[[ARGS.medium_selector]]
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258 medium = medium[ARGS.medium_selector].to_dict()
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259
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260 if(ARGS.ras_selector == True):
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48
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261 generate_bounds(model, medium, ras = ras_combined, output_folder=ARGS.output_folder)
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262 if(len(ras_list)>1):
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263 class_assignments.to_csv(ARGS.cell_class, sep = '\t', index = False)
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4
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264 else:
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265 generate_bounds(model, medium, output_folder=ARGS.output_folder)
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266
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267 pass
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268
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269 ##############################################################################
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270 if __name__ == "__main__":
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271 main() |
