#!/usr/bin/env python3"""Basic GRN inference example using Arboreto. This script demonstrates the standard workflow for inferring gene regulatorynetworks from expression data using GRNBoost2. Usage:    python basic_grn_inference.py <expression_file> <output_file> [--tf-file TF_FILE] [--seed SEED] Arguments:    expression_file: Path to expression matrix (TSV format, genes as columns)    output_file: Path for output network (TSV format)    --tf-file: Optional path to transcription factors file (one per line)    --seed: Random seed for reproducibility (default: 777)""" import argparseimport pandas as pdfrom arboreto.algo import grnboost2from arboreto.utils import load_tf_names  def run_grn_inference(expression_file, output_file, tf_file=None, seed=777):    """    Run GRN inference using GRNBoost2.     Args:        expression_file: Path to expression matrix TSV file        output_file: Path for output network file        tf_file: Optional path to TF names file        seed: Random seed for reproducibility    """    print(f"Loading expression data from {expression_file}...")    expression_data = pd.read_csv(expression_file, sep='\t')     print(f"Expression matrix shape: {expression_data.shape}")    print(f"Number of genes: {expression_data.shape[1]}")    print(f"Number of observations: {expression_data.shape[0]}")     # Load TF names if provided    tf_names = 'all'    if tf_file:        print(f"Loading transcription factors from {tf_file}...")        tf_names = load_tf_names(tf_file)        print(f"Number of TFs: {len(tf_names)}")     # Run GRN inference    print(f"Running GRNBoost2 with seed={seed}...")    network = grnboost2(        expression_data=expression_data,        tf_names=tf_names,        seed=seed,        verbose=True    )     # Save results    print(f"Saving network to {output_file}...")    network.to_csv(output_file, sep='\t', index=False, header=False)     print(f"Done! Network contains {len(network)} regulatory links.")    print(f"\nTop 10 regulatory links:")    print(network.head(10).to_string(index=False))  if __name__ == '__main__':    parser = argparse.ArgumentParser(        description='Infer gene regulatory network using GRNBoost2'    )    parser.add_argument(        'expression_file',        help='Path to expression matrix (TSV format, genes as columns)'    )    parser.add_argument(        'output_file',        help='Path for output network (TSV format)'    )    parser.add_argument(        '--tf-file',        help='Path to transcription factors file (one per line)',        default=None    )    parser.add_argument(        '--seed',        help='Random seed for reproducibility (default: 777)',        type=int,        default=777    )     args = parser.parse_args()     run_grn_inference(        expression_file=args.expression_file,        output_file=args.output_file,        tf_file=args.tf_file,        seed=args.seed    )