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456
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1 """
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2 Flux sampling and analysis utilities for COBRA models.
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3
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4 This script supports two modes:
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5 - Mode 1 (model_and_bounds=True): load a base model and apply bounds from
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6 separate files before sampling.
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7 - Mode 2 (model_and_bounds=False): load complete models and sample directly.
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8
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9 Sampling algorithms supported: OPTGP and CBS. Outputs include flux samples
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10 and optional analyses (pFBA, FVA, sensitivity), saved as tabular files.
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11 """
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12
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410
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13 import argparse
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14 import utils.general_utils as utils
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456
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15 from typing import List
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410
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16 import os
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17 import pandas as pd
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18 import cobra
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19 import utils.CBS_backend as CBS_backend
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20 from joblib import Parallel, delayed, cpu_count
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21 from cobra.sampling import OptGPSampler
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22 import sys
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419
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23 import utils.model_utils as model_utils
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410
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24
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25
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26 ################################# process args ###############################
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27 def process_args(args :List[str] = None) -> argparse.Namespace:
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28 """
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29 Processes command-line arguments.
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30
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31 Args:
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32 args (list): List of command-line arguments.
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33
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34 Returns:
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35 Namespace: An object containing parsed arguments.
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36 """
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37 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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38 description = 'process some value\'s')
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39
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40 parser.add_argument("-mo", "--model_upload", type = str,
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41 help = "path to input file with custom rules, if provided")
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42
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419
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43 parser.add_argument("-mab", "--model_and_bounds", type = str,
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44 choices = ['True', 'False'],
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45 required = True,
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46 help = "upload mode: True for model+bounds, False for complete models")
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47
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48
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410
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49 parser.add_argument('-ol', '--out_log',
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50 help = "Output log")
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51
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52 parser.add_argument('-td', '--tool_dir',
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53 type = str,
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54 required = True,
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55 help = 'your tool directory')
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56
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57 parser.add_argument('-in', '--input',
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419
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58 required = True,
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59 type=str,
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60 help = 'input bounds files or complete model files')
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410
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61
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419
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62 parser.add_argument('-ni', '--name',
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410
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63 required = True,
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64 type=str,
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65 help = 'cell names')
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66
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67 parser.add_argument('-a', '--algorithm',
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68 type = str,
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69 choices = ['OPTGP', 'CBS'],
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70 required = True,
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71 help = 'choose sampling algorithm')
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72
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73 parser.add_argument('-th', '--thinning',
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74 type = int,
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75 default= 100,
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76 required=False,
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77 help = 'choose thinning')
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78
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79 parser.add_argument('-ns', '--n_samples',
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80 type = int,
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81 required = True,
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82 help = 'choose how many samples')
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83
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84 parser.add_argument('-sd', '--seed',
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85 type = int,
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86 required = True,
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87 help = 'seed')
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88
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89 parser.add_argument('-nb', '--n_batches',
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90 type = int,
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91 required = True,
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92 help = 'choose how many batches')
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93
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430
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94 parser.add_argument('-opt', '--perc_opt',
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95 type = float,
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96 default=0.9,
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97 required = False,
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98 help = 'choose the fraction of optimality for FVA (0-1)')
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99
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410
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100 parser.add_argument('-ot', '--output_type',
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101 type = str,
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102 required = True,
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103 help = 'output type')
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104
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105 parser.add_argument('-ota', '--output_type_analysis',
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106 type = str,
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107 required = False,
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108 help = 'output type analysis')
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109
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110 parser.add_argument('-idop', '--output_path',
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111 type = str,
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112 default='flux_simulation',
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113 help = 'output path for maps')
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114
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115 ARGS = parser.parse_args(args)
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116 return ARGS
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117
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118 ########################### warning ###########################################
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119 def warning(s :str) -> None:
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120 """
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121 Log a warning message to an output log file and print it to the console.
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122
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123 Args:
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124 s (str): The warning message to be logged and printed.
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125
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126 Returns:
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127 None
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128 """
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129 with open(ARGS.out_log, 'a') as log:
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130 log.write(s + "\n\n")
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131 print(s)
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132
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133
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134 def write_to_file(dataset: pd.DataFrame, name: str, keep_index:bool=False)->None:
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456
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135 """
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136 Write a DataFrame to a TSV file under ARGS.output_path with a given base name.
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137
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138 Args:
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139 dataset: The DataFrame to write.
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140 name: Base file name (without extension).
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141 keep_index: Whether to keep the DataFrame index in the file.
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142
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143 Returns:
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144 None
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145 """
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410
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146 dataset.index.name = 'Reactions'
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147 dataset.to_csv(ARGS.output_path + "/" + name + ".csv", sep = '\t', index = keep_index)
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148
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149 ############################ dataset input ####################################
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150 def read_dataset(data :str, name :str) -> pd.DataFrame:
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151 """
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152 Read a dataset from a CSV file and return it as a pandas DataFrame.
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153
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154 Args:
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155 data (str): Path to the CSV file containing the dataset.
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156 name (str): Name of the dataset, used in error messages.
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157
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158 Returns:
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159 pandas.DataFrame: DataFrame containing the dataset.
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160
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161 Raises:
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162 pd.errors.EmptyDataError: If the CSV file is empty.
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163 sys.exit: If the CSV file has the wrong format, the execution is aborted.
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164 """
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165 try:
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166 dataset = pd.read_csv(data, sep = '\t', header = 0, index_col=0, engine='python')
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167 except pd.errors.EmptyDataError:
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168 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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169 if len(dataset.columns) < 2:
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170 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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171 return dataset
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172
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173
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174
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175 def OPTGP_sampler(model:cobra.Model, model_name:str, n_samples:int=1000, thinning:int=100, n_batches:int=1, seed:int=0)-> None:
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176 """
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177 Samples from the OPTGP (Optimal Global Perturbation) algorithm and saves the results to CSV files.
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178
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179 Args:
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180 model (cobra.Model): The COBRA model to sample from.
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181 model_name (str): The name of the model, used in naming output files.
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182 n_samples (int, optional): Number of samples per batch. Default is 1000.
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183 thinning (int, optional): Thinning parameter for the sampler. Default is 100.
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184 n_batches (int, optional): Number of batches to run. Default is 1.
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185 seed (int, optional): Random seed for reproducibility. Default is 0.
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186
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187 Returns:
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188 None
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189 """
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190
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191 for i in range(0, n_batches):
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192 optgp = OptGPSampler(model, thinning, seed)
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193 samples = optgp.sample(n_samples)
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194 samples.to_csv(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_OPTGP.csv', index=False)
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195 seed+=1
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196 samplesTotal = pd.DataFrame()
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197 for i in range(0, n_batches):
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198 samples_batch = pd.read_csv(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_OPTGP.csv')
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199 samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True)
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200
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201 write_to_file(samplesTotal.T, model_name, True)
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202
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203 for i in range(0, n_batches):
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204 os.remove(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_OPTGP.csv')
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205
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206
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207 def CBS_sampler(model:cobra.Model, model_name:str, n_samples:int=1000, n_batches:int=1, seed:int=0)-> None:
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208 """
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209 Samples using the CBS (Constraint-based Sampling) algorithm and saves the results to CSV files.
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210
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211 Args:
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212 model (cobra.Model): The COBRA model to sample from.
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213 model_name (str): The name of the model, used in naming output files.
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214 n_samples (int, optional): Number of samples per batch. Default is 1000.
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215 n_batches (int, optional): Number of batches to run. Default is 1.
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216 seed (int, optional): Random seed for reproducibility. Default is 0.
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217
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218 Returns:
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219 None
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220 """
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221
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222 df_FVA = cobra.flux_analysis.flux_variability_analysis(model,fraction_of_optimum=0).round(6)
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223
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224 df_coefficients = CBS_backend.randomObjectiveFunction(model, n_samples*n_batches, df_FVA, seed=seed)
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225
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226 for i in range(0, n_batches):
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227 samples = pd.DataFrame(columns =[reaction.id for reaction in model.reactions], index = range(n_samples))
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228 try:
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229 CBS_backend.randomObjectiveFunctionSampling(model, n_samples, df_coefficients.iloc[:,i*n_samples:(i+1)*n_samples], samples)
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230 except Exception as e:
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231 utils.logWarning(
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232 "Warning: GLPK solver has failed for " + model_name + ". Trying with COBRA interface. Error:" + str(e),
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233 ARGS.out_log)
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234 CBS_backend.randomObjectiveFunctionSampling_cobrapy(model, n_samples, df_coefficients.iloc[:,i*n_samples:(i+1)*n_samples],
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235 samples)
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236 utils.logWarning(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv', ARGS.out_log)
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237 samples.to_csv(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv', index=False)
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238
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239 samplesTotal = pd.DataFrame()
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240 for i in range(0, n_batches):
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241 samples_batch = pd.read_csv(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv')
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242 samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True)
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243
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244 write_to_file(samplesTotal.T, model_name, True)
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245
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246 for i in range(0, n_batches):
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247 os.remove(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv')
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248
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249
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419
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250
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251 def model_sampler_with_bounds(model_input_original: cobra.Model, bounds_path: str, cell_name: str) -> List[pd.DataFrame]:
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410
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252 """
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419
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253 MODE 1: Prepares the model with bounds from separate bounds file and performs sampling.
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410
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254
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255 Args:
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256 model_input_original (cobra.Model): The original COBRA model.
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257 bounds_path (str): Path to the CSV file containing the bounds dataset.
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258 cell_name (str): Name of the cell, used to generate filenames for output.
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259
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260 Returns:
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261 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
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262 """
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263
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264 model_input = model_input_original.copy()
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265 bounds_df = read_dataset(bounds_path, "bounds dataset")
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419
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266
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267 # Apply bounds to model
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410
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268 for rxn_index, row in bounds_df.iterrows():
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419
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269 try:
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270 model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound
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271 model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound
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272 except KeyError:
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273 warning(f"Warning: Reaction {rxn_index} not found in model. Skipping.")
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410
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274
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419
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275 return perform_sampling_and_analysis(model_input, cell_name)
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276
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277
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278 def perform_sampling_and_analysis(model_input: cobra.Model, cell_name: str) -> List[pd.DataFrame]:
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279 """
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280 Common function to perform sampling and analysis on a prepared model.
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281
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282 Args:
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283 model_input (cobra.Model): The prepared COBRA model with bounds applied.
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284 cell_name (str): Name of the cell, used to generate filenames for output.
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285
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286 Returns:
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287 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
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288 """
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410
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289
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290 if ARGS.algorithm == 'OPTGP':
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291 OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)
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292 elif ARGS.algorithm == 'CBS':
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419
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293 CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed)
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410
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294
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295 df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types)
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296
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297 if("fluxes" not in ARGS.output_types):
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419
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298 os.remove(ARGS.output_path + "/" + cell_name + '.csv')
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410
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299
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419
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300 returnList = [df_mean, df_median, df_quantiles]
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410
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301
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302 df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, cell_name, ARGS.output_type_analysis)
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303
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304 if("pFBA" in ARGS.output_type_analysis):
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305 returnList.append(df_pFBA)
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306 if("FVA" in ARGS.output_type_analysis):
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307 returnList.append(df_FVA)
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308 if("sensitivity" in ARGS.output_type_analysis):
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309 returnList.append(df_sensitivity)
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310
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311 return returnList
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312
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313 def fluxes_statistics(model_name: str, output_types:List)-> List[pd.DataFrame]:
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314 """
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315 Computes statistics (mean, median, quantiles) for the fluxes.
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316
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317 Args:
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318 model_name (str): Name of the model, used in filename for input.
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319 output_types (List[str]): Types of statistics to compute (mean, median, quantiles).
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320
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321 Returns:
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322 List[pd.DataFrame]: List of DataFrames containing mean, median, and quantiles statistics.
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323 """
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324
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325 df_mean = pd.DataFrame()
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326 df_median= pd.DataFrame()
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327 df_quantiles= pd.DataFrame()
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328
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329 df_samples = pd.read_csv(ARGS.output_path + "/" + model_name + '.csv', sep = '\t', index_col = 0).T
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330 df_samples = df_samples.round(8)
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331
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332 for output_type in output_types:
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333 if(output_type == "mean"):
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334 df_mean = df_samples.mean()
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335 df_mean = df_mean.to_frame().T
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336 df_mean = df_mean.reset_index(drop=True)
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337 df_mean.index = [model_name]
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338 elif(output_type == "median"):
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339 df_median = df_samples.median()
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340 df_median = df_median.to_frame().T
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341 df_median = df_median.reset_index(drop=True)
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342 df_median.index = [model_name]
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343 elif(output_type == "quantiles"):
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344 newRow = []
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345 cols = []
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346 for rxn in df_samples.columns:
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347 quantiles = df_samples[rxn].quantile([0.25, 0.50, 0.75])
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348 newRow.append(quantiles[0.25])
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349 cols.append(rxn + "_q1")
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350 newRow.append(quantiles[0.5])
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351 cols.append(rxn + "_q2")
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352 newRow.append(quantiles[0.75])
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353 cols.append(rxn + "_q3")
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354 df_quantiles = pd.DataFrame(columns=cols)
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355 df_quantiles.loc[0] = newRow
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356 df_quantiles = df_quantiles.reset_index(drop=True)
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357 df_quantiles.index = [model_name]
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358
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359 return df_mean, df_median, df_quantiles
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360
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361 def fluxes_analysis(model:cobra.Model, model_name:str, output_types:List)-> List[pd.DataFrame]:
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362 """
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363 Performs flux analysis including pFBA, FVA, and sensitivity analysis.
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364
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365 Args:
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366 model (cobra.Model): The COBRA model to analyze.
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367 model_name (str): Name of the model, used in filenames for output.
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368 output_types (List[str]): Types of analysis to perform (pFBA, FVA, sensitivity).
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369
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370 Returns:
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371 List[pd.DataFrame]: List of DataFrames containing pFBA, FVA, and sensitivity analysis results.
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372 """
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373
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374 df_pFBA = pd.DataFrame()
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375 df_FVA= pd.DataFrame()
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376 df_sensitivity= pd.DataFrame()
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377
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378 for output_type in output_types:
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379 if(output_type == "pFBA"):
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380 model.objective = "Biomass"
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381 solution = cobra.flux_analysis.pfba(model)
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382 fluxes = solution.fluxes
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419
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383 df_pFBA.loc[0,[rxn.id for rxn in model.reactions]] = fluxes.tolist()
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410
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384 df_pFBA = df_pFBA.reset_index(drop=True)
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385 df_pFBA.index = [model_name]
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386 df_pFBA = df_pFBA.astype(float).round(6)
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387 elif(output_type == "FVA"):
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430
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388 fva = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=ARGS.perc_opt, processes=1).round(8)
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410
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389 columns = []
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390 for rxn in fva.index.to_list():
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391 columns.append(rxn + "_min")
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392 columns.append(rxn + "_max")
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393 df_FVA= pd.DataFrame(columns = columns)
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394 for index_rxn, row in fva.iterrows():
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395 df_FVA.loc[0, index_rxn+ "_min"] = fva.loc[index_rxn, "minimum"]
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396 df_FVA.loc[0, index_rxn+ "_max"] = fva.loc[index_rxn, "maximum"]
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397 df_FVA = df_FVA.reset_index(drop=True)
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398 df_FVA.index = [model_name]
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399 df_FVA = df_FVA.astype(float).round(6)
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400 elif(output_type == "sensitivity"):
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401 model.objective = "Biomass"
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402 solution_original = model.optimize().objective_value
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403 reactions = model.reactions
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404 single = cobra.flux_analysis.single_reaction_deletion(model)
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405 newRow = []
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406 df_sensitivity = pd.DataFrame(columns = [rxn.id for rxn in reactions], index = [model_name])
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407 for rxn in reactions:
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408 newRow.append(single.knockout[rxn.id].growth.values[0]/solution_original)
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409 df_sensitivity.loc[model_name] = newRow
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410 df_sensitivity = df_sensitivity.astype(float).round(6)
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411 return df_pFBA, df_FVA, df_sensitivity
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412
|
|
|
413 ############################# main ###########################################
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414 def main(args :List[str] = None) -> None:
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415 """
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456
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416 Initialize and run sampling/analysis based on the frontend input arguments.
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410
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417
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418 Returns:
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419 None
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420 """
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421
|
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419
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422 num_processors = max(1, cpu_count() - 1)
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410
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423
|
|
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424 global ARGS
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425 ARGS = process_args(args)
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426
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427 if not os.path.exists(ARGS.output_path):
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428 os.makedirs(ARGS.output_path)
|
|
419
|
429
|
|
|
430 # --- Normalize inputs (the tool may pass comma-separated --input and either --name or --names) ---
|
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421
|
431 ARGS.input_files = ARGS.input.split(",") if ARGS.input else []
|
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419
|
432 ARGS.file_names = ARGS.name.split(",")
|
|
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433 # output types (required) -> list
|
|
421
|
434 ARGS.output_types = ARGS.output_type.split(",") if ARGS.output_type else []
|
|
419
|
435 # optional analysis output types -> list or empty
|
|
421
|
436 ARGS.output_type_analysis = ARGS.output_type_analysis.split(",") if ARGS.output_type_analysis else []
|
|
419
|
437
|
|
421
|
438 print("=== INPUT FILES ===")
|
|
422
|
439 print(f"{ARGS.input_files}")
|
|
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440 print(f"{ARGS.file_names}")
|
|
|
441 print(f"{ARGS.output_type}")
|
|
|
442 print(f"{ARGS.output_types}")
|
|
|
443 print(f"{ARGS.output_type_analysis}")
|
|
410
|
444
|
|
419
|
445 if ARGS.model_and_bounds == "True":
|
|
|
446 # MODE 1: Model + bounds (separate files)
|
|
|
447 print("=== MODE 1: Model + Bounds (separate files) ===")
|
|
|
448
|
|
|
449 # Load base model
|
|
|
450 if not ARGS.model_upload:
|
|
|
451 sys.exit("Error: model_upload is required for Mode 1")
|
|
410
|
452
|
|
419
|
453 base_model = model_utils.build_cobra_model_from_csv(ARGS.model_upload)
|
|
410
|
454
|
|
419
|
455 validation = model_utils.validate_model(base_model)
|
|
|
456
|
|
456
|
457 print("\n=== MODEL VALIDATION ===")
|
|
419
|
458 for key, value in validation.items():
|
|
|
459 print(f"{key}: {value}")
|
|
|
460
|
|
456
|
461 # Set solver verbosity to 1 to see warning and error messages only.
|
|
419
|
462 base_model.solver.configuration.verbosity = 1
|
|
410
|
463
|
|
456
|
464 # Process each bounds file with the base model
|
|
419
|
465 results = Parallel(n_jobs=num_processors)(
|
|
|
466 delayed(model_sampler_with_bounds)(base_model, bounds_file, cell_name)
|
|
|
467 for bounds_file, cell_name in zip(ARGS.input_files, ARGS.file_names)
|
|
|
468 )
|
|
410
|
469
|
|
419
|
470 else:
|
|
|
471 # MODE 2: Multiple complete models
|
|
|
472 print("=== MODE 2: Multiple complete models ===")
|
|
|
473
|
|
|
474 # Process each complete model file
|
|
|
475 results = Parallel(n_jobs=num_processors)(
|
|
|
476 delayed(perform_sampling_and_analysis)(model_utils.build_cobra_model_from_csv(model_file), cell_name)
|
|
|
477 for model_file, cell_name in zip(ARGS.input_files, ARGS.file_names)
|
|
|
478 )
|
|
410
|
479
|
|
|
480
|
|
|
481 all_mean = pd.concat([result[0] for result in results], ignore_index=False)
|
|
|
482 all_median = pd.concat([result[1] for result in results], ignore_index=False)
|
|
|
483 all_quantiles = pd.concat([result[2] for result in results], ignore_index=False)
|
|
|
484
|
|
|
485 if("mean" in ARGS.output_types):
|
|
|
486 all_mean = all_mean.fillna(0.0)
|
|
|
487 all_mean = all_mean.sort_index()
|
|
|
488 write_to_file(all_mean.T, "mean", True)
|
|
|
489
|
|
|
490 if("median" in ARGS.output_types):
|
|
|
491 all_median = all_median.fillna(0.0)
|
|
|
492 all_median = all_median.sort_index()
|
|
|
493 write_to_file(all_median.T, "median", True)
|
|
|
494
|
|
|
495 if("quantiles" in ARGS.output_types):
|
|
|
496 all_quantiles = all_quantiles.fillna(0.0)
|
|
|
497 all_quantiles = all_quantiles.sort_index()
|
|
|
498 write_to_file(all_quantiles.T, "quantiles", True)
|
|
|
499
|
|
|
500 index_result = 3
|
|
|
501 if("pFBA" in ARGS.output_type_analysis):
|
|
|
502 all_pFBA = pd.concat([result[index_result] for result in results], ignore_index=False)
|
|
|
503 all_pFBA = all_pFBA.sort_index()
|
|
|
504 write_to_file(all_pFBA.T, "pFBA", True)
|
|
|
505 index_result+=1
|
|
|
506 if("FVA" in ARGS.output_type_analysis):
|
|
|
507 all_FVA= pd.concat([result[index_result] for result in results], ignore_index=False)
|
|
|
508 all_FVA = all_FVA.sort_index()
|
|
|
509 write_to_file(all_FVA.T, "FVA", True)
|
|
|
510 index_result+=1
|
|
|
511 if("sensitivity" in ARGS.output_type_analysis):
|
|
|
512 all_sensitivity = pd.concat([result[index_result] for result in results], ignore_index=False)
|
|
|
513 all_sensitivity = all_sensitivity.sort_index()
|
|
|
514 write_to_file(all_sensitivity.T, "sensitivity", True)
|
|
|
515
|
|
456
|
516 return
|
|
410
|
517
|
|
|
518 ##############################################################################
|
|
|
519 if __name__ == "__main__":
|
|
|
520 main() |
