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comparison COBRAxy/utils/ras_to_bounds.py @ 57:0b4be1dbdbc4 draft

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author luca_milaz
date Sun, 13 Oct 2024 06:52:58 +0000
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56:9688ad27287b 57:0b4be1dbdbc4
1 import argparse
2 import utils.general_utils as utils
3 from typing import Optional, List
4 import os
5 import numpy as np
6 import pandas as pd
7 import cobra
8 import sys
9 import csv
10 from joblib import Parallel, delayed, cpu_count
11
12 ################################# process args ###############################
13 def process_args(args :List[str]) -> argparse.Namespace:
14 """
15 Processes command-line arguments.
16
17 Args:
18 args (list): List of command-line arguments.
19
20 Returns:
21 Namespace: An object containing parsed arguments.
22 """
23 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
24 description = 'process some value\'s')
25
26 parser.add_argument(
27 '-ms', '--model_selector',
28 type = utils.Model, default = utils.Model.ENGRO2, choices = [utils.Model.ENGRO2, utils.Model.Custom],
29 help = 'chose which type of model you want use')
30
31 parser.add_argument("-mo", "--model", type = str,
32 help = "path to input file with custom rules, if provided")
33
34 parser.add_argument("-mn", "--model_name", type = str, help = "custom mode name")
35
36 parser.add_argument(
37 '-mes', '--medium_selector',
38 default = "allOpen",
39 help = 'chose which type of medium you want use')
40
41 parser.add_argument("-meo", "--medium", type = str,
42 help = "path to input file with custom medium, if provided")
43
44 parser.add_argument('-ol', '--out_log',
45 help = "Output log")
46
47 parser.add_argument('-td', '--tool_dir',
48 type = str,
49 required = True,
50 help = 'your tool directory')
51
52 parser.add_argument('-ir', '--input_ras',
53 type=str,
54 required = False,
55 help = 'input ras')
56
57 parser.add_argument('-rs', '--ras_selector',
58 required = True,
59 type=utils.Bool("using_RAS"),
60 help = 'ras selector')
61
62 parser.add_argument('-c', '--classes',
63 type = str,
64 required = False,
65 help = 'input classes')
66
67 parser.add_argument('-cc', '--cell_class',
68 type = str,
69 help = 'output of cell class')
70
71 ARGS = parser.parse_args()
72 return ARGS
73
74 ########################### warning ###########################################
75 def warning(s :str) -> None:
76 """
77 Log a warning message to an output log file and print it to the console.
78
79 Args:
80 s (str): The warning message to be logged and printed.
81
82 Returns:
83 None
84 """
85 with open(ARGS.out_log, 'a') as log:
86 log.write(s + "\n\n")
87 print(s)
88
89 ############################ dataset input ####################################
90 def read_dataset(data :str, name :str) -> pd.DataFrame:
91 """
92 Read a dataset from a CSV file and return it as a pandas DataFrame.
93
94 Args:
95 data (str): Path to the CSV file containing the dataset.
96 name (str): Name of the dataset, used in error messages.
97
98 Returns:
99 pandas.DataFrame: DataFrame containing the dataset.
100
101 Raises:
102 pd.errors.EmptyDataError: If the CSV file is empty.
103 sys.exit: If the CSV file has the wrong format, the execution is aborted.
104 """
105 try:
106 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
107 except pd.errors.EmptyDataError:
108 sys.exit('Execution aborted: wrong format of ' + name + '\n')
109 if len(dataset.columns) < 2:
110 sys.exit('Execution aborted: wrong format of ' + name + '\n')
111 return dataset
112
113
114 def apply_ras_bounds(model, ras_row, rxns_ids):
115 """
116 Adjust the bounds of reactions in the model based on RAS values.
117
118 Args:
119 model (cobra.Model): The metabolic model to be modified.
120 ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
121 rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
122
123 Returns:
124 None
125 """
126 for reaction in rxns_ids:
127 if reaction in ras_row.index:
128 scaling_factor = ras_row[reaction]
129 lower_bound=model.reactions.get_by_id(reaction).lower_bound
130 upper_bound=model.reactions.get_by_id(reaction).upper_bound
131 valMax=float((upper_bound)*scaling_factor)
132 valMin=float((lower_bound)*scaling_factor)
133 if upper_bound!=0 and lower_bound==0:
134 model.reactions.get_by_id(reaction).upper_bound=valMax
135 if upper_bound==0 and lower_bound!=0:
136 model.reactions.get_by_id(reaction).lower_bound=valMin
137 if upper_bound!=0 and lower_bound!=0:
138 model.reactions.get_by_id(reaction).lower_bound=valMin
139 model.reactions.get_by_id(reaction).upper_bound=valMax
140 pass
141
142 def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder):
143 """
144 Process a single RAS cell, apply bounds, and save the bounds to a CSV file.
145
146 Args:
147 cellName (str): The name of the RAS cell (used for naming the output file).
148 ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
149 model (cobra.Model): The metabolic model to be modified.
150 rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
151 output_folder (str): Folder path where the output CSV file will be saved.
152
153 Returns:
154 None
155 """
156 model_new = model.copy()
157 apply_ras_bounds(model_new, ras_row, rxns_ids)
158 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
159 bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
160 pass
161
162 def generate_bounds(model: cobra.Model, medium: dict, ras=None, output_folder='output/') -> pd.DataFrame:
163 """
164 Generate reaction bounds for a metabolic model based on medium conditions and optional RAS adjustments.
165
166 Args:
167 model (cobra.Model): The metabolic model for which bounds will be generated.
168 medium (dict): A dictionary where keys are reaction IDs and values are the medium conditions.
169 ras (pd.DataFrame, optional): RAS pandas dataframe. Defaults to None.
170 output_folder (str, optional): Folder path where output CSV files will be saved. Defaults to 'output/'.
171
172 Returns:
173 pd.DataFrame: DataFrame containing the bounds of reactions in the model.
174 """
175 rxns_ids = [rxn.id for rxn in model.reactions]
176
177 # Set medium conditions
178 for reaction, value in medium.items():
179 if value is not None:
180 model.reactions.get_by_id(reaction).lower_bound = -float(value)
181
182 # Perform Flux Variability Analysis (FVA)
183 df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
184
185 # Set FVA bounds
186 for reaction in rxns_ids:
187 rxn = model.reactions.get_by_id(reaction)
188 rxn.lower_bound = float(df_FVA.loc[reaction, "minimum"])
189 rxn.upper_bound = float(df_FVA.loc[reaction, "maximum"])
190
191 if ras is not None:
192 Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
193 else:
194 model_new = model.copy()
195 apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids)
196 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
197 bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
198 pass
199
200
201
202 ############################# main ###########################################
203 def main() -> None:
204 """
205 Initializes everything and sets the program in motion based on the fronted input arguments.
206
207 Returns:
208 None
209 """
210 if not os.path.exists('ras_to_bounds'):
211 os.makedirs('ras_to_bounds')
212
213
214 global ARGS
215 ARGS = process_args(sys.argv)
216
217 ARGS.output_folder = 'ras_to_bounds/'
218
219 if(ARGS.ras_selector == True):
220 ras_file_list = ARGS.input_ras.split(",")
221 if(len(ras_file_list)>1):
222 ras_class_names = [cls.strip() for cls in ARGS.classes.split(',')]
223 else:
224 ras_class_names = ["placeHolder"]
225 ras_list = []
226 class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
227 for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
228 ras = read_dataset(ras_matrix, "ras dataset")
229 ras.replace("None", None, inplace=True)
230 ras.set_index("Reactions", drop=True, inplace=True)
231 ras = ras.T
232 ras = ras.astype(float)
233 ras_list.append(ras)
234 for patient_id in ras.index:
235 class_assignments = pd.concat([class_assignments, pd.DataFrame({"Patient_ID": ras.index, "Class": ras_class_name})])
236
237
238 # Concatenate all ras DataFrames into a single DataFrame
239 ras_combined = pd.concat(ras_list, axis=1)
240 # Normalize the RAS values by max RAS
241 ras_combined = ras_combined.div(ras_combined.max(axis=0))
242 ras_combined = ras_combined.fillna(0)
243
244
245
246 model_type :utils.Model = ARGS.model_selector
247 if model_type is utils.Model.Custom:
248 model = model_type.getCOBRAmodel(customPath = utils.FilePath.fromStrPath(ARGS.model), customExtension = utils.FilePath.fromStrPath(ARGS.model_name).ext)
249 else:
250 model = model_type.getCOBRAmodel(toolDir=ARGS.tool_dir)
251
252 if(ARGS.medium_selector == "Custom"):
253 medium = read_dataset(ARGS.medium, "medium dataset")
254 medium.set_index(medium.columns[0], inplace=True)
255 medium = medium.astype(float)
256 medium = medium[medium.columns[0]].to_dict()
257 else:
258 df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
259 ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
260 medium = df_mediums[[ARGS.medium_selector]]
261 medium = medium[ARGS.medium_selector].to_dict()
262
263 if(ARGS.ras_selector == True):
264 generate_bounds(model, medium, ras = ras_combined, output_folder=ARGS.output_folder)
265 if(len(ras_list)>1):
266 class_assignments.to_csv(ARGS.cell_class, sep = '\t', index = False)
267 else:
268 generate_bounds(model, medium, output_folder=ARGS.output_folder)
269
270 pass
271
272 ##############################################################################
273 if __name__ == "__main__":
274 main()