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491
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1 """
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2 Scripts to generate a tabular file of a metabolic model (built-in or custom).
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3
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4 This script loads a COBRA model (built-in or custom), optionally applies
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5 medium and gene nomenclature settings, derives reaction-related metadata
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6 (GPR rules, formulas, bounds, objective coefficients, medium membership,
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7 and compartments for ENGRO2), and writes a tabular summary.
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8 """
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9
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10 import os
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11 import csv
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12 import cobra
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13 import argparse
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14 import pandas as pd
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15 import utils.general_utils as utils
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16 from typing import Optional, Tuple, List
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17 import utils.model_utils as modelUtils
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18 import logging
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19 from pathlib import Path
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20
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21
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22 ARGS : argparse.Namespace
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23 def process_args(args: List[str] = None) -> argparse.Namespace:
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24 """
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25 Parse command-line arguments for metabolic_model_setting.
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26 """
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27
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28 parser = argparse.ArgumentParser(
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29 usage="%(prog)s [options]",
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30 description="Generate custom data from a given model"
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31 )
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32
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33 parser.add_argument("--out_log", type=str, required=True,
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34 help="Output log file")
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35
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36 parser.add_argument("--model", type=str,
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37 help="Built-in model identifier (e.g., ENGRO2, Recon, HMRcore)")
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38 parser.add_argument("--input", type=str,
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39 help="Custom model file (JSON or XML)")
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40 parser.add_argument("--name", type=str, required=True,
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41 help="Model name (default or custom)")
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42
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43 parser.add_argument("--medium_selector", type=str, required=True,
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44 help="Medium selection option")
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45
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46 parser.add_argument("--gene_format", type=str, default="Default",
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47 help="Gene nomenclature format: Default (original), ENSNG, HGNC_SYMBOL, HGNC_ID, ENTREZ")
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48
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49 parser.add_argument("--out_tabular", type=str,
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50 help="Output file for the merged dataset (CSV or XLSX)")
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51
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52 parser.add_argument("--tool_dir", type=str, default=os.path.dirname(__file__),
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53 help="Tool directory (passed from Galaxy as $__tool_directory__)")
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54
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55
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56 return parser.parse_args(args)
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57
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58 ################################- INPUT DATA LOADING -################################
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59 def load_custom_model(file_path :utils.FilePath, ext :Optional[utils.FileFormat] = None) -> cobra.Model:
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60 """
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61 Loads a custom model from a file, either in JSON, XML, MAT, or YML format.
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62
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63 Args:
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64 file_path : The path to the file containing the custom model.
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65 ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour.
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66
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67 Raises:
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68 DataErr : if the file is in an invalid format or cannot be opened for whatever reason.
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69
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70 Returns:
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71 cobra.Model : the model, if successfully opened.
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72 """
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73 ext = ext if ext else file_path.ext
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74 try:
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75 if ext is utils.FileFormat.XML:
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76 return cobra.io.read_sbml_model(file_path.show())
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77
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78 if ext is utils.FileFormat.JSON:
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79 return cobra.io.load_json_model(file_path.show())
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80
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81 if ext is utils.FileFormat.MAT:
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82 return cobra.io.load_matlab_model(file_path.show())
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83
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84 if ext is utils.FileFormat.YML:
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85 return cobra.io.load_yaml_model(file_path.show())
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86
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87 except Exception as e: raise utils.DataErr(file_path, e.__str__())
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88 raise utils.DataErr(
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89 file_path,
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90 f"Unrecognized format '{file_path.ext}'. Only JSON, XML, MAT, YML are supported."
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91 )
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92
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93
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94 ###############################- FILE SAVING -################################
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95 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
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96 """
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97 Saves any dictionary-shaped data in a .csv file created at the given file_path as FilePath.
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98
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99 Args:
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100 data : the data to be written to the file.
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101 file_path : the path to the .csv file.
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102 fieldNames : the names of the fields (columns) in the .csv file.
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103
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104 Returns:
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105 None
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106 """
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107 with open(file_path.show(), 'w', newline='') as csvfile:
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108 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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109 writer.writeheader()
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110
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111 for key, value in data.items():
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112 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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113
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114 def save_as_csv(data :dict, file_path :str, fieldNames :Tuple[str, str]) -> None:
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115 """
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116 Saves any dictionary-shaped data in a .csv file created at the given file_path as string.
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117
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118 Args:
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119 data : the data to be written to the file.
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120 file_path : the path to the .csv file.
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121 fieldNames : the names of the fields (columns) in the .csv file.
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122
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123 Returns:
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124 None
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125 """
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126 with open(file_path, 'w', newline='') as csvfile:
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127 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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128 writer.writeheader()
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129
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130 for key, value in data.items():
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131 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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132
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133 def save_as_tabular_df(df: pd.DataFrame, path: str) -> None:
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134 """
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135 Save a pandas DataFrame as a tab-separated file, creating directories as needed.
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136
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137 Args:
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138 df: The DataFrame to write.
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139 path: Destination file path (will be written as TSV).
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140
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141 Raises:
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142 DataErr: If writing the output fails for any reason.
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143
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144 Returns:
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145 None
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146 """
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147 try:
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148 os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
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149 df.to_csv(path, sep="\t", index=False)
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150 except Exception as e:
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151 raise utils.DataErr(path, f"failed writing tabular output: {e}")
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152
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153 def is_placeholder(gid) -> bool:
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154 """Return True if the gene id looks like a placeholder (e.g., 0/NA/NAN/empty)."""
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155 if gid is None:
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156 return True
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157 s = str(gid).strip().lower()
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158 return s in {"0", "", "na", "nan"} # lowercase for simple matching
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159
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160 def sample_valid_gene_ids(genes, limit=10):
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161 """Yield up to `limit` valid gene IDs, skipping placeholders (e.g., the first 0 in RECON)."""
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162 out = []
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163 for g in genes:
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164 gid = getattr(g, "id", getattr(g, "gene_id", g))
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165 if not is_placeholder(gid):
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166 out.append(str(gid))
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167 if len(out) >= limit:
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168 break
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169 return out
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170
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171
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172 ###############################- ENTRY POINT -################################
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173 def main(args:List[str] = None) -> None:
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174 """
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175 Initialize and generate custom data based on the frontend input arguments.
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176
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177 Returns:
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178 None
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179 """
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180 # Parse args from frontend (Galaxy XML)
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181 global ARGS
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182 ARGS = process_args(args)
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183
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184
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185 if ARGS.input:
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186 # Load a custom model from file
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187 model = load_custom_model(
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188 utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
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189 else:
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190 # Load a built-in model
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191
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192 try:
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193 model_enum = utils.Model[ARGS.model] # e.g., Model['ENGRO2']
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194 except KeyError:
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195 raise utils.ArgsErr("model", "one of Recon/ENGRO2/HMRcore/Custom_model", ARGS.model)
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196
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197 # Load built-in model (Model.getCOBRAmodel uses tool_dir to locate local models)
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198 try:
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199 model = model_enum.getCOBRAmodel(toolDir=ARGS.tool_dir)
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200 except Exception as e:
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201 # Wrap/normalize load errors as DataErr for consistency
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202 raise utils.DataErr(ARGS.model, f"failed loading built-in model: {e}")
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203
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204 # Determine final model name: explicit --name overrides, otherwise use the model id
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205
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206 if ARGS.name == "ENGRO2" and ARGS.medium_selector != "Default":
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207 df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
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208 ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
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209 medium = df_mediums[[ARGS.medium_selector]]
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210 medium = medium[ARGS.medium_selector].to_dict()
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211
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212 # Reset all medium reactions lower bound to zero
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213 for rxn_id, _ in model.medium.items():
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214 model.reactions.get_by_id(rxn_id).lower_bound = float(0.0)
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215
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216 # Apply selected medium uptake bounds (negative for uptake)
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217 for reaction, value in medium.items():
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218 if value is not None:
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219 model.reactions.get_by_id(reaction).lower_bound = -float(value)
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220
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221 # Initialize translation_issues dictionary
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222 translation_issues = {}
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223
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224 if (ARGS.name == "Recon" or ARGS.name == "ENGRO2") and ARGS.gene_format != "Default":
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225 logging.basicConfig(level=logging.INFO)
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226 logger = logging.getLogger(__name__)
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227
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228 model, translation_issues = modelUtils.translate_model_genes(
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229 model=model,
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230 mapping_df= pd.read_csv(ARGS.tool_dir + "/local/mappings/genes_human.csv", dtype={'entrez_id': str}),
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231 target_nomenclature=ARGS.gene_format,
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232 source_nomenclature='HGNC_symbol',
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233 logger=logger
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234 )
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235
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236 if ARGS.name == "Custom_model" and ARGS.gene_format != "Default":
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237 logging.basicConfig(level=logging.INFO)
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238 logger = logging.getLogger(__name__)
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239
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240 tmp_check = []
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241 for g in model.genes[1:5]: # check first 3 genes only
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242 tmp_check.append(modelUtils.gene_type(g.id, "Custom_model"))
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243
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244 if len(set(tmp_check)) > 1:
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245 raise utils.DataErr("Custom_model", "The custom model contains genes with mixed or unrecognized nomenclature. Please ensure all genes use the same recognized nomenclature before applying gene_format conversion.")
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246 else:
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247 source_nomenclature = tmp_check[0]
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248
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249 if source_nomenclature != ARGS.gene_format:
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250 model, translation_issues = modelUtils.translate_model_genes(
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251 model=model,
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252 mapping_df= pd.read_csv(ARGS.tool_dir + "/local/mappings/genes_human.csv", dtype={'entrez_id': str}),
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253 target_nomenclature=ARGS.gene_format,
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254 source_nomenclature=source_nomenclature,
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255 logger=logger
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256 )
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257
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258
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259
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260
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261 if ARGS.name == "Custom_model" and ARGS.gene_format != "Default":
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262 logger = logging.getLogger(__name__)
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263
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264 # Take a small, clean sample of gene IDs (skipping placeholders like 0)
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265 ids_sample = sample_valid_gene_ids(model.genes, limit=10)
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266 if not ids_sample:
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267 raise utils.DataErr(
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268 "Custom_model",
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269 "No valid gene IDs found (many may be placeholders like 0)."
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270 )
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271
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272 # Detect source nomenclature on the sample
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273 types = []
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274 for gid in ids_sample:
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275 try:
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276 t = modelUtils.gene_type(gid, "Custom_model")
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277 except Exception as e:
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278 # Keep it simple: skip problematic IDs
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279 logger.debug(f"gene_type failed for {gid}: {e}")
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280 t = None
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281 if t:
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282 types.append(t)
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283
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284 if not types:
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285 raise utils.DataErr(
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286 "Custom_model",
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287 "Could not detect a known gene nomenclature from the sample."
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288 )
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289
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290 unique_types = set(types)
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291 if len(unique_types) > 1:
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292 raise utils.DataErr(
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293 "Custom_model",
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294 "Mixed or inconsistent gene nomenclatures detected. "
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295 "Please unify them before converting."
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296 )
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297
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298 source_nomenclature = types[0]
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299
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300 # Convert only if needed
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301 if source_nomenclature != ARGS.gene_format:
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302 model, translation_issues = modelUtils.translate_model_genes(
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303 model=model,
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304 mapping_df= pd.read_csv(ARGS.tool_dir + "/local/mappings/genes_human.csv", dtype={'entrez_id': str}),
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305 target_nomenclature=ARGS.gene_format,
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306 source_nomenclature=source_nomenclature,
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307 logger=logger
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308 )
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309
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310 # generate data
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311 rules = modelUtils.generate_rules(model, asParsed = False)
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312 reactions = modelUtils.generate_reactions(model, asParsed = False)
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313 bounds = modelUtils.generate_bounds(model)
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314 medium = modelUtils.get_medium(model)
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315 objective_function = modelUtils.extract_objective_coefficients(model)
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316
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317 if ARGS.name == "ENGRO2":
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318 compartments = modelUtils.generate_compartments(model)
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319
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320 df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "GPR"])
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321 df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Formula"])
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322
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323 # Create DataFrame for translation issues
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324 df_translation_issues = pd.DataFrame([
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325 {"ReactionID": rxn_id, "TranslationIssues": issues}
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326 for rxn_id, issues in translation_issues.items()
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327 ])
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328
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329 df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
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330 df_medium = medium.rename(columns = {"reaction": "ReactionID"})
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331 df_medium["InMedium"] = True
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332
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333 merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
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334 merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
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335 merged = merged.merge(objective_function, on = "ReactionID", how = "outer")
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336 if ARGS.name == "ENGRO2":
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337 merged = merged.merge(compartments, on = "ReactionID", how = "outer")
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338 merged = merged.merge(df_medium, on = "ReactionID", how = "left")
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339
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340 # Add translation issues column
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341 if not df_translation_issues.empty:
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342 merged = merged.merge(df_translation_issues, on = "ReactionID", how = "left")
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343 merged["TranslationIssues"] = merged["TranslationIssues"].fillna("")
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344 else:
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345 # Add empty TranslationIssues column if no issues found
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346 #merged["TranslationIssues"] = ""
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347 pass
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348
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349 merged["InMedium"] = merged["InMedium"].fillna(False)
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350
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351 merged = merged.sort_values(by = "InMedium", ascending = False)
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352
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353 if not ARGS.out_tabular:
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354 raise utils.ArgsErr("out_tabular", "output path (--out_tabular) is required when output_format == tabular", ARGS.out_tabular)
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355 save_as_tabular_df(merged, ARGS.out_tabular)
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356 expected = ARGS.out_tabular
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357
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358 # verify output exists and non-empty
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359 if not expected or not os.path.exists(expected) or os.path.getsize(expected) == 0:
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360 raise utils.DataErr(expected, "Output not created or empty")
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361
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362 print("Metabolic_model_setting: completed successfully")
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363
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364 if __name__ == '__main__':
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365
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366 main()
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