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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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461
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18 import numpy as np
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410
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19 import cobra
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20 import utils.CBS_backend as CBS_backend
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21 from joblib import Parallel, delayed, cpu_count
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22 from cobra.sampling import OptGPSampler
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23 import sys
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419
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24 import utils.model_utils as model_utils
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410
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25
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26
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27 ################################# process args ###############################
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461
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28 def process_args(args: List[str] = None) -> argparse.Namespace:
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410
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29 """
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30 Processes command-line arguments.
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461
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31
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410
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32 Args:
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33 args (list): List of command-line arguments.
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461
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34
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410
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35 Returns:
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36 Namespace: An object containing parsed arguments.
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37 """
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461
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38 parser = argparse.ArgumentParser(usage='%(prog)s [options]',
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39 description='process some value\'s')
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40
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41 parser.add_argument("-mo", "--model_upload", type=str,
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42 help="path to input file with custom rules, if provided")
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410
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43
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461
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44 parser.add_argument("-mab", "--model_and_bounds", type=str,
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45 choices=['True', 'False'],
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46 required=True,
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47 help="upload mode: True for model+bounds, False for complete models")
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48
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469
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49 parser.add_argument("-ens", "--sampling_enabled", type=str,
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470
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50 choices=['true', 'false'],
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469
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51 required=True,
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470
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52 help="enable sampling: 'true' for sampling, 'false' for no sampling")
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469
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53
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461
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54 parser.add_argument('-ol', '--out_log',
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55 help="Output log")
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410
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56
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57 parser.add_argument('-td', '--tool_dir',
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461
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58 type=str,
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59 required=True,
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60 help='your tool directory')
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410
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61
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62 parser.add_argument('-in', '--input',
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461
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63 required=True,
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64 type=str,
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65 help='input bounds files or complete model files')
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410
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66
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419
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67 parser.add_argument('-ni', '--name',
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461
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68 required=True,
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410
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69 type=str,
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461
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70 help='cell names')
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410
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71
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72 parser.add_argument('-a', '--algorithm',
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461
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73 type=str,
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74 choices=['OPTGP', 'CBS'],
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75 required=True,
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76 help='choose sampling algorithm')
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410
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77
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461
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78 parser.add_argument('-th', '--thinning',
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79 type=int,
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80 default=100,
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473
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81 required=True,
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461
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82 help='choose thinning')
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410
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83
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461
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84 parser.add_argument('-ns', '--n_samples',
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85 type=int,
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86 required=True,
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87 help='choose how many samples (set to 0 for optimization only)')
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410
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88
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461
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89 parser.add_argument('-sd', '--seed',
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90 type=int,
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91 required=True,
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92 help='seed for random number generation')
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410
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93
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461
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94 parser.add_argument('-nb', '--n_batches',
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95 type=int,
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96 required=True,
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97 help='choose how many batches')
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410
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98
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430
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99 parser.add_argument('-opt', '--perc_opt',
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461
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100 type=float,
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430
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101 default=0.9,
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474
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102 required=False,
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461
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103 help='choose the fraction of optimality for FVA (0-1)')
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430
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104
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461
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105 parser.add_argument('-ot', '--output_type',
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106 type=str,
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107 required=True,
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108 help='output type for sampling results')
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410
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109
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461
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110 parser.add_argument('-ota', '--output_type_analysis',
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111 type=str,
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112 required=False,
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113 help='output type analysis (optimization methods)')
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410
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114
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461
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115 parser.add_argument('-idop', '--output_path',
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116 type=str,
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410
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117 default='flux_simulation',
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461
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118 help='output path for maps')
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410
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119
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120 ARGS = parser.parse_args(args)
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121 return ARGS
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122 ########################### warning ###########################################
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123 def warning(s :str) -> None:
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124 """
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125 Log a warning message to an output log file and print it to the console.
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126
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127 Args:
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128 s (str): The warning message to be logged and printed.
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129
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130 Returns:
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131 None
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132 """
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133 with open(ARGS.out_log, 'a') as log:
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134 log.write(s + "\n\n")
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135 print(s)
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136
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137
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138 def write_to_file(dataset: pd.DataFrame, name: str, keep_index:bool=False)->None:
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456
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139 """
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140 Write a DataFrame to a TSV file under ARGS.output_path with a given base name.
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141
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142 Args:
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143 dataset: The DataFrame to write.
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144 name: Base file name (without extension).
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145 keep_index: Whether to keep the DataFrame index in the file.
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146
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147 Returns:
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148 None
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149 """
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410
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150 dataset.index.name = 'Reactions'
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151 dataset.to_csv(ARGS.output_path + "/" + name + ".csv", sep = '\t', index = keep_index)
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152
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153 ############################ dataset input ####################################
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154 def read_dataset(data :str, name :str) -> pd.DataFrame:
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155 """
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156 Read a dataset from a CSV file and return it as a pandas DataFrame.
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157
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158 Args:
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159 data (str): Path to the CSV file containing the dataset.
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160 name (str): Name of the dataset, used in error messages.
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161
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162 Returns:
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163 pandas.DataFrame: DataFrame containing the dataset.
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164
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165 Raises:
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166 pd.errors.EmptyDataError: If the CSV file is empty.
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167 sys.exit: If the CSV file has the wrong format, the execution is aborted.
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168 """
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169 try:
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170 dataset = pd.read_csv(data, sep = '\t', header = 0, index_col=0, engine='python')
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171 except pd.errors.EmptyDataError:
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172 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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173 if len(dataset.columns) < 2:
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174 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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175 return dataset
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176
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177
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178
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461
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179 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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410
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180 """
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181 Samples from the OPTGP (Optimal Global Perturbation) algorithm and saves the results to CSV files.
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461
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182
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410
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183 Args:
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184 model (cobra.Model): The COBRA model to sample from.
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185 model_name (str): The name of the model, used in naming output files.
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186 n_samples (int, optional): Number of samples per batch. Default is 1000.
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187 thinning (int, optional): Thinning parameter for the sampler. Default is 100.
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188 n_batches (int, optional): Number of batches to run. Default is 1.
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189 seed (int, optional): Random seed for reproducibility. Default is 0.
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461
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190
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410
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191 Returns:
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192 None
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193 """
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461
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194 import numpy as np
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195
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196 # Get reaction IDs for consistent column ordering
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197 reaction_ids = [rxn.id for rxn in model.reactions]
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198
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199 # Sample and save each batch as numpy file
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200 for i in range(n_batches):
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410
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201 optgp = OptGPSampler(model, thinning, seed)
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202 samples = optgp.sample(n_samples)
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461
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203
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204 # Save as numpy array (more memory efficient)
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205 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
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206 np.save(batch_filename, samples.values)
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207
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208 seed += 1
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209
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210 # Merge all batches into a single DataFrame
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211 all_samples = []
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212
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213 for i in range(n_batches):
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214 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
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215 batch_data = np.load(batch_filename)
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216 all_samples.append(batch_data)
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217
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218 # Concatenate all batches
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219 samplesTotal_array = np.vstack(all_samples)
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220
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221 # Convert back to DataFrame with proper column names
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222 samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids)
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223
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224 # Save the final merged result as CSV
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410
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225 write_to_file(samplesTotal.T, model_name, True)
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461
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226
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227 # Clean up temporary numpy files
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228 for i in range(n_batches):
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229 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
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230 if os.path.exists(batch_filename):
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231 os.remove(batch_filename)
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410
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232
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233
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461
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234 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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410
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235 """
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236 Samples using the CBS (Constraint-based Sampling) algorithm and saves the results to CSV files.
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461
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237
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410
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238 Args:
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239 model (cobra.Model): The COBRA model to sample from.
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240 model_name (str): The name of the model, used in naming output files.
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241 n_samples (int, optional): Number of samples per batch. Default is 1000.
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242 n_batches (int, optional): Number of batches to run. Default is 1.
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243 seed (int, optional): Random seed for reproducibility. Default is 0.
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461
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244
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410
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245 Returns:
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246 None
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247 """
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461
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248 import numpy as np
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249
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250 # Get reaction IDs for consistent column ordering
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251 reaction_ids = [reaction.id for reaction in model.reactions]
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252
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253 # Perform FVA analysis once for all batches
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254 df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0).round(6)
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255
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256 # Generate random objective functions for all samples across all batches
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257 df_coefficients = CBS_backend.randomObjectiveFunction(model, n_samples * n_batches, df_FVA, seed=seed)
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410
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258
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461
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259 # Sample and save each batch as numpy file
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260 for i in range(n_batches):
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261 samples = pd.DataFrame(columns=reaction_ids, index=range(n_samples))
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262
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410
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263 try:
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461
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264 CBS_backend.randomObjectiveFunctionSampling(
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265 model,
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266 n_samples,
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267 df_coefficients.iloc[:, i * n_samples:(i + 1) * n_samples],
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268 samples
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269 )
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410
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270 except Exception as e:
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271 utils.logWarning(
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461
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272 f"Warning: GLPK solver has failed for {model_name}. Trying with COBRA interface. Error: {str(e)}",
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273 ARGS.out_log
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274 )
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275 CBS_backend.randomObjectiveFunctionSampling_cobrapy(
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276 model,
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277 n_samples,
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278 df_coefficients.iloc[:, i * n_samples:(i + 1) * n_samples],
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279 samples
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280 )
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281
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282 # Save as numpy array (more memory efficient)
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283 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
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284 utils.logWarning(batch_filename, ARGS.out_log)
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285 np.save(batch_filename, samples.values)
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286
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287 # Merge all batches into a single DataFrame
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288 all_samples = []
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289
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290 for i in range(n_batches):
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291 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
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292 batch_data = np.load(batch_filename)
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293 all_samples.append(batch_data)
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294
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295 # Concatenate all batches
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296 samplesTotal_array = np.vstack(all_samples)
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297
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298 # Convert back to DataFrame with proper column names
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299 samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids)
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300
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301 # Save the final merged result as CSV
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410
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302 write_to_file(samplesTotal.T, model_name, True)
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461
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303
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304 # Clean up temporary numpy files
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305 for i in range(n_batches):
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306 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
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307 if os.path.exists(batch_filename):
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308 os.remove(batch_filename)
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410
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309
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310
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419
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311
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312 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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313 """
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419
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314 MODE 1: Prepares the model with bounds from separate bounds file and performs sampling.
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410
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315
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316 Args:
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317 model_input_original (cobra.Model): The original COBRA model.
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318 bounds_path (str): Path to the CSV file containing the bounds dataset.
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319 cell_name (str): Name of the cell, used to generate filenames for output.
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320
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321 Returns:
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322 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
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323 """
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324
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325 model_input = model_input_original.copy()
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326 bounds_df = read_dataset(bounds_path, "bounds dataset")
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419
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327
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328 # Apply bounds to model
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410
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329 for rxn_index, row in bounds_df.iterrows():
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419
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330 try:
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331 model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound
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332 model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound
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333 except KeyError:
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334 warning(f"Warning: Reaction {rxn_index} not found in model. Skipping.")
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410
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335
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419
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336 return perform_sampling_and_analysis(model_input, cell_name)
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337
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338
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339 def perform_sampling_and_analysis(model_input: cobra.Model, cell_name: str) -> List[pd.DataFrame]:
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340 """
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341 Common function to perform sampling and analysis on a prepared model.
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342
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343 Args:
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344 model_input (cobra.Model): The prepared COBRA model with bounds applied.
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345 cell_name (str): Name of the cell, used to generate filenames for output.
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346
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347 Returns:
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348 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
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349 """
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410
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350
|
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473
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351 if ARGS.sampling_enabled == "true":
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469
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352
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353 if ARGS.algorithm == 'OPTGP':
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354 OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)
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355 elif ARGS.algorithm == 'CBS':
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356 CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed)
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410
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357
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469
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358 df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types)
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|
410
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359
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469
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360 if("fluxes" not in ARGS.output_types):
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361 os.remove(ARGS.output_path + "/" + cell_name + '.csv')
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362
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363 returnList = [df_mean, df_median, df_quantiles]
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410
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364
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365 df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, cell_name, ARGS.output_type_analysis)
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366
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367 if("pFBA" in ARGS.output_type_analysis):
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368 returnList.append(df_pFBA)
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369 if("FVA" in ARGS.output_type_analysis):
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370 returnList.append(df_FVA)
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371 if("sensitivity" in ARGS.output_type_analysis):
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372 returnList.append(df_sensitivity)
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373
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374 return returnList
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375
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376 def fluxes_statistics(model_name: str, output_types:List)-> List[pd.DataFrame]:
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|
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377 """
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|
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378 Computes statistics (mean, median, quantiles) for the fluxes.
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379
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380 Args:
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381 model_name (str): Name of the model, used in filename for input.
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382 output_types (List[str]): Types of statistics to compute (mean, median, quantiles).
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383
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384 Returns:
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385 List[pd.DataFrame]: List of DataFrames containing mean, median, and quantiles statistics.
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386 """
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387
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388 df_mean = pd.DataFrame()
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389 df_median= pd.DataFrame()
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390 df_quantiles= pd.DataFrame()
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391
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392 df_samples = pd.read_csv(ARGS.output_path + "/" + model_name + '.csv', sep = '\t', index_col = 0).T
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393 df_samples = df_samples.round(8)
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394
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395 for output_type in output_types:
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396 if(output_type == "mean"):
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397 df_mean = df_samples.mean()
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398 df_mean = df_mean.to_frame().T
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399 df_mean = df_mean.reset_index(drop=True)
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400 df_mean.index = [model_name]
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401 elif(output_type == "median"):
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402 df_median = df_samples.median()
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403 df_median = df_median.to_frame().T
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404 df_median = df_median.reset_index(drop=True)
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405 df_median.index = [model_name]
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406 elif(output_type == "quantiles"):
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407 newRow = []
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408 cols = []
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409 for rxn in df_samples.columns:
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410 quantiles = df_samples[rxn].quantile([0.25, 0.50, 0.75])
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411 newRow.append(quantiles[0.25])
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412 cols.append(rxn + "_q1")
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413 newRow.append(quantiles[0.5])
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414 cols.append(rxn + "_q2")
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415 newRow.append(quantiles[0.75])
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416 cols.append(rxn + "_q3")
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417 df_quantiles = pd.DataFrame(columns=cols)
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418 df_quantiles.loc[0] = newRow
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419 df_quantiles = df_quantiles.reset_index(drop=True)
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420 df_quantiles.index = [model_name]
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421
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422 return df_mean, df_median, df_quantiles
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423
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424 def fluxes_analysis(model:cobra.Model, model_name:str, output_types:List)-> List[pd.DataFrame]:
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425 """
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426 Performs flux analysis including pFBA, FVA, and sensitivity analysis.
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427
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428 Args:
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429 model (cobra.Model): The COBRA model to analyze.
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430 model_name (str): Name of the model, used in filenames for output.
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431 output_types (List[str]): Types of analysis to perform (pFBA, FVA, sensitivity).
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432
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433 Returns:
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434 List[pd.DataFrame]: List of DataFrames containing pFBA, FVA, and sensitivity analysis results.
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435 """
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436
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437 df_pFBA = pd.DataFrame()
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438 df_FVA= pd.DataFrame()
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439 df_sensitivity= pd.DataFrame()
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440
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441 for output_type in output_types:
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442 if(output_type == "pFBA"):
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443 model.objective = "Biomass"
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444 solution = cobra.flux_analysis.pfba(model)
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445 fluxes = solution.fluxes
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419
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446 df_pFBA.loc[0,[rxn.id for rxn in model.reactions]] = fluxes.tolist()
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410
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447 df_pFBA = df_pFBA.reset_index(drop=True)
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448 df_pFBA.index = [model_name]
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449 df_pFBA = df_pFBA.astype(float).round(6)
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450 elif(output_type == "FVA"):
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430
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451 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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452 columns = []
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453 for rxn in fva.index.to_list():
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454 columns.append(rxn + "_min")
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455 columns.append(rxn + "_max")
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456 df_FVA= pd.DataFrame(columns = columns)
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457 for index_rxn, row in fva.iterrows():
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458 df_FVA.loc[0, index_rxn+ "_min"] = fva.loc[index_rxn, "minimum"]
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459 df_FVA.loc[0, index_rxn+ "_max"] = fva.loc[index_rxn, "maximum"]
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460 df_FVA = df_FVA.reset_index(drop=True)
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461 df_FVA.index = [model_name]
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462 df_FVA = df_FVA.astype(float).round(6)
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463 elif(output_type == "sensitivity"):
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464 model.objective = "Biomass"
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465 solution_original = model.optimize().objective_value
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466 reactions = model.reactions
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467 single = cobra.flux_analysis.single_reaction_deletion(model)
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468 newRow = []
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469 df_sensitivity = pd.DataFrame(columns = [rxn.id for rxn in reactions], index = [model_name])
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470 for rxn in reactions:
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471 newRow.append(single.knockout[rxn.id].growth.values[0]/solution_original)
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472 df_sensitivity.loc[model_name] = newRow
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473 df_sensitivity = df_sensitivity.astype(float).round(6)
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474 return df_pFBA, df_FVA, df_sensitivity
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475
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476 ############################# main ###########################################
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461
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477 def main(args: List[str] = None) -> None:
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410
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478 """
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456
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479 Initialize and run sampling/analysis based on the frontend input arguments.
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410
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480
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481 Returns:
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482 None
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483 """
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484
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419
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485 num_processors = max(1, cpu_count() - 1)
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410
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486
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487 global ARGS
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488 ARGS = process_args(args)
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489
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490 if not os.path.exists(ARGS.output_path):
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491 os.makedirs(ARGS.output_path)
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419
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492
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493 # --- Normalize inputs (the tool may pass comma-separated --input and either --name or --names) ---
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421
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494 ARGS.input_files = ARGS.input.split(",") if ARGS.input else []
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419
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495 ARGS.file_names = ARGS.name.split(",")
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496 # output types (required) -> list
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421
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497 ARGS.output_types = ARGS.output_type.split(",") if ARGS.output_type else []
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419
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498 # optional analysis output types -> list or empty
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421
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499 ARGS.output_type_analysis = ARGS.output_type_analysis.split(",") if ARGS.output_type_analysis else []
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419
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500
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461
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501 # Determine if sampling should be performed
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473
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502 if ARGS.sampling_enabled == "true":
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469
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503 perform_sampling = True
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475
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504 else:
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505 perform_sampling = False
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461
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506
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421
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507 print("=== INPUT FILES ===")
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422
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508 print(f"{ARGS.input_files}")
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509 print(f"{ARGS.file_names}")
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510 print(f"{ARGS.output_type}")
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511 print(f"{ARGS.output_types}")
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512 print(f"{ARGS.output_type_analysis}")
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461
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513 print(f"Sampling enabled: {perform_sampling} (n_samples: {ARGS.n_samples})")
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410
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514
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419
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515 if ARGS.model_and_bounds == "True":
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516 # MODE 1: Model + bounds (separate files)
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517 print("=== MODE 1: Model + Bounds (separate files) ===")
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518
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519 # Load base model
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520 if not ARGS.model_upload:
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521 sys.exit("Error: model_upload is required for Mode 1")
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410
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522
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419
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523 base_model = model_utils.build_cobra_model_from_csv(ARGS.model_upload)
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410
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524
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419
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525 validation = model_utils.validate_model(base_model)
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526
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456
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527 print("\n=== MODEL VALIDATION ===")
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419
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528 for key, value in validation.items():
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529 print(f"{key}: {value}")
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530
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456
|
531 # Set solver verbosity to 1 to see warning and error messages only.
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419
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532 base_model.solver.configuration.verbosity = 1
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410
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533
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456
|
534 # Process each bounds file with the base model
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419
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535 results = Parallel(n_jobs=num_processors)(
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536 delayed(model_sampler_with_bounds)(base_model, bounds_file, cell_name)
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537 for bounds_file, cell_name in zip(ARGS.input_files, ARGS.file_names)
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538 )
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410
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539
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419
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540 else:
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541 # MODE 2: Multiple complete models
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542 print("=== MODE 2: Multiple complete models ===")
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543
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544 # Process each complete model file
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545 results = Parallel(n_jobs=num_processors)(
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546 delayed(perform_sampling_and_analysis)(model_utils.build_cobra_model_from_csv(model_file), cell_name)
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547 for model_file, cell_name in zip(ARGS.input_files, ARGS.file_names)
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548 )
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|
410
|
549
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461
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550 # Handle sampling outputs (only if sampling was performed)
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551 if perform_sampling:
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552 print("=== PROCESSING SAMPLING RESULTS ===")
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553
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554 all_mean = pd.concat([result[0] for result in results], ignore_index=False)
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555 all_median = pd.concat([result[1] for result in results], ignore_index=False)
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556 all_quantiles = pd.concat([result[2] for result in results], ignore_index=False)
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|
410
|
557
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461
|
558 if "mean" in ARGS.output_types:
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559 all_mean = all_mean.fillna(0.0)
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560 all_mean = all_mean.sort_index()
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561 write_to_file(all_mean.T, "mean", True)
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410
|
562
|
|
461
|
563 if "median" in ARGS.output_types:
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564 all_median = all_median.fillna(0.0)
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565 all_median = all_median.sort_index()
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566 write_to_file(all_median.T, "median", True)
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567
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568 if "quantiles" in ARGS.output_types:
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569 all_quantiles = all_quantiles.fillna(0.0)
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570 all_quantiles = all_quantiles.sort_index()
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571 write_to_file(all_quantiles.T, "quantiles", True)
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572 else:
|
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469
|
573 print("=== SAMPLING SKIPPED (n_samples = 0 or sampling disabled) ===")
|
|
461
|
574
|
|
|
575 # Handle optimization analysis outputs (always available)
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|
|
576 print("=== PROCESSING OPTIMIZATION RESULTS ===")
|
|
410
|
577
|
|
461
|
578 # Determine the starting index for optimization results
|
|
|
579 # If sampling was performed, optimization results start at index 3
|
|
|
580 # If no sampling, optimization results start at index 0
|
|
|
581 index_result = 3 if perform_sampling else 0
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582
|
|
|
583 if "pFBA" in ARGS.output_type_analysis:
|
|
410
|
584 all_pFBA = pd.concat([result[index_result] for result in results], ignore_index=False)
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|
585 all_pFBA = all_pFBA.sort_index()
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|
586 write_to_file(all_pFBA.T, "pFBA", True)
|
|
461
|
587 index_result += 1
|
|
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588
|
|
|
589 if "FVA" in ARGS.output_type_analysis:
|
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|
590 all_FVA = pd.concat([result[index_result] for result in results], ignore_index=False)
|
|
410
|
591 all_FVA = all_FVA.sort_index()
|
|
|
592 write_to_file(all_FVA.T, "FVA", True)
|
|
461
|
593 index_result += 1
|
|
|
594
|
|
|
595 if "sensitivity" in ARGS.output_type_analysis:
|
|
410
|
596 all_sensitivity = pd.concat([result[index_result] for result in results], ignore_index=False)
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|
|
597 all_sensitivity = all_sensitivity.sort_index()
|
|
|
598 write_to_file(all_sensitivity.T, "sensitivity", True)
|
|
|
599
|
|
456
|
600 return
|
|
410
|
601
|
|
|
602 ##############################################################################
|
|
|
603 if __name__ == "__main__":
|
|
|
604 main() |
