annotate gem_flux_variability_analysis.py @ 0:dfeabe31d865 draft default tip

planemo upload for repository https://github.com/AlmaasLab/elixir-galaxy-tools-systemsbiology commit 3f7bec1264a86e1488ee1315dbac0f44675f5171

1 import argparse
2 
3 import cobra
4 import pandas as pd
5 
6 
7 def __main__():
8     parser = argparse.ArgumentParser(
9         prog="FluxVariabilityAnalysis",
10         description="This program performs flux variability "
11         "analysis on a GEM",
12         epilog="Adding an epilog, but doubt it's needed.",
13     )
14     parser.add_argument(
15         "-m", "--cb_model_location",
16         dest="cb_model_location",
17         action="store",
18         type=str,
19         default=None,
20         required=True,
21         help="The model to use."
22     )
23     parser.add_argument(
24         "-output",
25         "--output",
26         dest="out_file",
27         action="store",
28         type=str,
29         default=None,
30         required=True,
31         help="The output file."
32     )
33     parser.add_argument(
34         "-f",
35         "--fraction",
36         dest="fraction_of_optimum",
37         action="store",
38         type=float,
39         default=None,
40         required=True,
41         help="The fraction of optimum the FVA solutions should come within."
42     )
43     parser.add_argument(
44         "-u",
45         "--uptake_constraints_file",
46         dest="uptake_constraints_file",
47         action="store",
48         type=str,
49         default=None,
50         required=False,
51         help="File containing new uptake constraits."
52     )
53 
54     args = parser.parse_args()
55 
56     # Validate constraints file first if provided
57     constraints_df = None
58     if args.uptake_constraints_file is not None\
59             and args.uptake_constraints_file != "None":
60         try:
61             constraints_df = pd.read_csv(
62                 args.uptake_constraints_file,
63                 sep=";",
64                 header=0,
65                 index_col=False
66             )
67 
68             required_columns = ['reaction_id', 'lower_bound', 'upper_bound']
69             missing_columns = [col for col in required_columns if
70                                col not in constraints_df.columns]
71 
72             if missing_columns:
73                 raise ValueError(
74                     f"Constraints file is missing required columns: "
75                     f"{', '.join(missing_columns)}. "
76                     f"Required columns are: {', '.join(required_columns)}"
77                 )
78         except FileNotFoundError:
79             raise FileNotFoundError(
80                 f"Constraints file not found: {args.uptake_constraints_file}"
81             )
82         except pd.errors.EmptyDataError:
83             raise ValueError("Constraints file is empty")
84         except Exception as e:
85             raise ValueError(f"Error processing constraints file: {str(e)}")
86 
87     # Load model
88     cb_model = cobra.io.read_sbml_model(args.cb_model_location)
89 
90     # Apply constraints if they were loaded successfully
91     if constraints_df is not None:
92         for index, row in constraints_df.iterrows():
93             cb_model.reactions.get_by_id(
94                 row["reaction_id"]).lower_bound = float(row["lower_bound"])
95             cb_model.reactions.get_by_id(
96                 row["reaction_id"]).upper_bound = float(row["upper_bound"])
97 
98     fraction_of_optimum = args.fraction_of_optimum
99 
00     # perform fva
01     fva_result = cobra.flux_analysis.flux_variability_analysis(
02         cb_model,
03         fraction_of_optimum=fraction_of_optimum
04     )
05 
06     # add reaction names and ids to the dataframe
07     fva_result["reaction_id"] = fva_result.index
08     fva_result["reaction_name"] = fva_result["reaction_id"].apply(
09         lambda x: cb_model.reactions.get_by_id(x).name
10     )
11 
12     # reorder the columns
13     fva_result = fva_result[[
14         "reaction_id", "reaction_name", "minimum", "maximum"
15     ]]
16 
17     fva_result.to_csv(args.out_file, sep=";", index=False, header=True)
18 
19 
20 if __name__ == "__main__":
21     __main__()