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skills/arboreto/scripts/basic_grn_inference.py

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+#!/usr/bin/env python3
+"""
+Basic GRN inference example using Arboreto.
+
+This script demonstrates the standard workflow for inferring gene regulatory
+networks 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 argparse
+import pandas as pd
+from arboreto.algo import grnboost2
+from 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
+    )