refitter_example.py

import logging

#import numpy as np
from argparse import ArgumentParser
import pandas as pd

from signaturesnet import DATA
from signaturesnet.modules.signet_module import SigNet

logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s',
                    level=logging.INFO,
                    datefmt='%Y-%m-%d %H:%M:%S')

def parse_args():
    parser = ArgumentParser()
    parser.add_argument(
        '--experiment_id', action='store', nargs=1, type=str, required=False, default="test_0",
        help=f"Name of this inference's results"
    )

    parser.add_argument(
        '--input_format', action='store', nargs=1, type=str, required=False, default="counts",
        help=f'Format input. Must be one of: counts, bed, vcf'
    )

    parser.add_argument(
        '--input_data', action='store', nargs=1, type=str, required=False, default=[DATA + "/datasets/example_input.csv"],
        help=f'Path to the input data to be analyzed. By default it will use PCAWG dataset'
    )

    parser.add_argument(
        '--reference_genome', action='store', nargs=1, type=str, required=False, default=[None],
        help=f'Name or path to the reference genome. Needed when input_format is bed or vcf.'
    )

    parser.add_argument(
        '--normalization', action='store', nargs=1, type=str, required=False, default=[None],
        help=f'The kind of normalization to be applied to the data. Should be either "None" (default), "exome", "genome" or a path to a file with the oppportunities.'
    )
    
    parser.add_argument(
        '--only_nnls', action='store', nargs=1, type=str, required=False, default=[False],
        help=f'Boolean. Whether to use nnls mode only. Default: "False".'
    )

    parser.add_argument(
        '--cutoff', action='store', nargs=1, type=float, required=False, default=[0.01],
        help=f'Cutoff to be applied to the final weights.'
    )

    parser.add_argument(
        '--output', action='store', nargs=1, type=str, required=False, default=["Output"],
        help=f'Path to folder where all the output files will be saved. Default: "Output".'
    )

    parser.add_argument(
        '--plot_figs', action='store', nargs=1, type=str, required=False, default=[False],
        help=f'Boolean. Whether to compute plots for the output. Default: "False".'
    )
    
    args = parser.parse_args()
    return args

if __name__ == "__main__":
    # Parse command-line arguments
    args = parse_args()
    
    # Read data
    mutations = pd.read_csv(args.input_data[0], header=0, index_col=0)

    # Load & Run signaturesnet
    signaturesnet = SigNet(opportunities_name_or_path=args.normalization[0])
    results = signaturesnet(mutation_dataset=mutations, cutoff=args.cutoff[0], only_NNLS=args.only_nnls[0])

    # Extract results
    w, u, l, c, _ = results.get_output()

    # Store results
    results.save(path=args.output[0])

    # Plot figures
    results.plot_results(compute=args.plot_figs[0], save=True, path=args.output[0]+'/plots')

    print("Done! Check out your results in the folder: ", args.output[0], " :D")