mirror of
https://github.com/K-Dense-AI/claude-scientific-skills.git
synced 2026-03-27 07:09:27 +08:00
Improve the arboreto skill
This commit is contained in:
Timothy Kassis
@@ -1,18 +1,18 @@
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#!/usr/bin/env python3
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"""
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Basic GRN inference script using arboreto GRNBoost2.
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Basic GRN inference example using Arboreto.
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This script demonstrates the standard workflow for gene regulatory network inference:
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1. Load expression data
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2. Optionally load transcription factor names
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3. Run GRNBoost2 inference
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4. Save results
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This script demonstrates the standard workflow for inferring gene regulatory
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networks from expression data using GRNBoost2.
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Usage:
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python basic_grn_inference.py <expression_file> [options]
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python basic_grn_inference.py <expression_file> <output_file> [--tf-file TF_FILE] [--seed SEED]
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Example:
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python basic_grn_inference.py expression_data.tsv -t tf_names.txt -o network.tsv
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Arguments:
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expression_file: Path to expression matrix (TSV format, genes as columns)
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output_file: Path for output network (TSV format)
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--tf-file: Optional path to transcription factors file (one per line)
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--seed: Random seed for reproducibility (default: 777)
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"""
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import argparse
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@@ -21,90 +21,77 @@ from arboreto.algo import grnboost2
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from arboreto.utils import load_tf_names
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def main():
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def run_grn_inference(expression_file, output_file, tf_file=None, seed=777):
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"""
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Run GRN inference using GRNBoost2.
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Args:
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expression_file: Path to expression matrix TSV file
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output_file: Path for output network file
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tf_file: Optional path to TF names file
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seed: Random seed for reproducibility
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"""
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print(f"Loading expression data from {expression_file}...")
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expression_data = pd.read_csv(expression_file, sep='\t')
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print(f"Expression matrix shape: {expression_data.shape}")
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print(f"Number of genes: {expression_data.shape[1]}")
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print(f"Number of observations: {expression_data.shape[0]}")
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# Load TF names if provided
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tf_names = 'all'
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if tf_file:
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print(f"Loading transcription factors from {tf_file}...")
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tf_names = load_tf_names(tf_file)
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print(f"Number of TFs: {len(tf_names)}")
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# Run GRN inference
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print(f"Running GRNBoost2 with seed={seed}...")
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network = grnboost2(
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expression_data=expression_data,
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tf_names=tf_names,
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seed=seed,
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verbose=True
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)
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# Save results
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print(f"Saving network to {output_file}...")
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network.to_csv(output_file, sep='\t', index=False, header=False)
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print(f"Done! Network contains {len(network)} regulatory links.")
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print(f"\nTop 10 regulatory links:")
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print(network.head(10).to_string(index=False))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description='Infer gene regulatory network using GRNBoost2'
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)
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parser.add_argument(
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'expression_file',
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help='Path to expression data file (TSV/CSV format)'
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help='Path to expression matrix (TSV format, genes as columns)'
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)
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parser.add_argument(
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'-t', '--tf-file',
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help='Path to file containing transcription factor names (one per line)',
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'output_file',
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help='Path for output network (TSV format)'
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)
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parser.add_argument(
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'--tf-file',
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help='Path to transcription factors file (one per line)',
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default=None
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)
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parser.add_argument(
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'-o', '--output',
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help='Output file path for network results',
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default='network_output.tsv'
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)
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parser.add_argument(
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'-s', '--seed',
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'--seed',
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help='Random seed for reproducibility (default: 777)',
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type=int,
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help='Random seed for reproducibility',
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default=42
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)
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parser.add_argument(
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'--sep',
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help='Separator for input file (default: tab)',
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default='\t'
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)
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parser.add_argument(
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'--transpose',
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action='store_true',
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help='Transpose the expression matrix (use if genes are rows)'
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default=777
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)
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args = parser.parse_args()
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# Load expression data
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print(f"Loading expression data from {args.expression_file}...")
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expression_data = pd.read_csv(args.expression_file, sep=args.sep, index_col=0)
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# Transpose if needed
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if args.transpose:
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print("Transposing expression matrix...")
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expression_data = expression_data.T
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print(f"Expression data shape: {expression_data.shape}")
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print(f" Observations (rows): {expression_data.shape[0]}")
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print(f" Genes (columns): {expression_data.shape[1]}")
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# Load TF names if provided
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tf_names = None
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if args.tf_file:
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print(f"Loading transcription factor names from {args.tf_file}...")
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tf_names = load_tf_names(args.tf_file)
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print(f" Found {len(tf_names)} transcription factors")
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else:
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print("No TF file provided. Using all genes as potential regulators.")
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# Run GRNBoost2
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print("\nRunning GRNBoost2 inference...")
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print(" (This may take a while depending on dataset size)")
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network = grnboost2(
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expression_data=expression_data,
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tf_names=tf_names,
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run_grn_inference(
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expression_file=args.expression_file,
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output_file=args.output_file,
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tf_file=args.tf_file,
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seed=args.seed
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)
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print(f"\nInference complete!")
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print(f" Total regulatory links inferred: {len(network)}")
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print(f" Unique TFs: {network['TF'].nunique()}")
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print(f" Unique targets: {network['target'].nunique()}")
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# Save results
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print(f"\nSaving results to {args.output}...")
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network.to_csv(args.output, sep='\t', index=False)
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# Display top 10 predictions
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print("\nTop 10 predicted regulatory relationships:")
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print(network.head(10).to_string(index=False))
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print("\nDone!")
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if __name__ == '__main__':
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main()
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@@ -1,205 +0,0 @@
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#!/usr/bin/env python3
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"""
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Compare GRNBoost2 and GENIE3 algorithms on the same dataset.
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This script runs both algorithms on the same expression data and compares:
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- Runtime
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- Number of predicted links
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- Top predicted relationships
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- Overlap between predictions
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Usage:
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python compare_algorithms.py <expression_file> [options]
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Example:
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python compare_algorithms.py expression_data.tsv -t tf_names.txt
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"""
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import argparse
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import time
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import pandas as pd
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from arboreto.algo import grnboost2, genie3
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from arboreto.utils import load_tf_names
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def compare_networks(network1, network2, name1, name2, top_n=100):
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"""Compare two inferred networks."""
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print(f"\n{'='*60}")
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print("Network Comparison")
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print(f"{'='*60}")
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# Basic statistics
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print(f"\n{name1} Statistics:")
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print(f" Total links: {len(network1)}")
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print(f" Unique TFs: {network1['TF'].nunique()}")
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print(f" Unique targets: {network1['target'].nunique()}")
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print(f" Importance range: [{network1['importance'].min():.3f}, {network1['importance'].max():.3f}]")
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print(f"\n{name2} Statistics:")
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print(f" Total links: {len(network2)}")
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print(f" Unique TFs: {network2['TF'].nunique()}")
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print(f" Unique targets: {network2['target'].nunique()}")
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print(f" Importance range: [{network2['importance'].min():.3f}, {network2['importance'].max():.3f}]")
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# Compare top predictions
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print(f"\nTop {top_n} Predictions Overlap:")
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# Create edge sets for top N predictions
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top_edges1 = set(
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zip(network1.head(top_n)['TF'], network1.head(top_n)['target'])
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)
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top_edges2 = set(
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zip(network2.head(top_n)['TF'], network2.head(top_n)['target'])
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)
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# Calculate overlap
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overlap = top_edges1 & top_edges2
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only_net1 = top_edges1 - top_edges2
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only_net2 = top_edges2 - top_edges1
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overlap_pct = (len(overlap) / top_n) * 100
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print(f" Shared edges: {len(overlap)} ({overlap_pct:.1f}%)")
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print(f" Only in {name1}: {len(only_net1)}")
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print(f" Only in {name2}: {len(only_net2)}")
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# Show some example overlapping edges
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if overlap:
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print(f"\nExample overlapping predictions:")
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for i, (tf, target) in enumerate(list(overlap)[:5], 1):
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print(f" {i}. {tf} -> {target}")
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def main():
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parser = argparse.ArgumentParser(
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description='Compare GRNBoost2 and GENIE3 algorithms'
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)
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parser.add_argument(
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'expression_file',
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help='Path to expression data file (TSV/CSV format)'
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)
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parser.add_argument(
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'-t', '--tf-file',
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help='Path to file containing transcription factor names (one per line)',
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default=None
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)
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parser.add_argument(
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'--grnboost2-output',
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help='Output file path for GRNBoost2 results',
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default='grnboost2_network.tsv'
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)
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parser.add_argument(
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'--genie3-output',
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help='Output file path for GENIE3 results',
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default='genie3_network.tsv'
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)
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parser.add_argument(
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'-s', '--seed',
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type=int,
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help='Random seed for reproducibility',
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default=42
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)
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parser.add_argument(
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'--sep',
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help='Separator for input file (default: tab)',
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default='\t'
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)
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parser.add_argument(
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'--transpose',
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action='store_true',
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help='Transpose the expression matrix (use if genes are rows)'
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)
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parser.add_argument(
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'--top-n',
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type=int,
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help='Number of top predictions to compare (default: 100)',
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default=100
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)
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args = parser.parse_args()
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# Load expression data
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print(f"Loading expression data from {args.expression_file}...")
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expression_data = pd.read_csv(args.expression_file, sep=args.sep, index_col=0)
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# Transpose if needed
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if args.transpose:
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print("Transposing expression matrix...")
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expression_data = expression_data.T
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print(f"Expression data shape: {expression_data.shape}")
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print(f" Observations (rows): {expression_data.shape[0]}")
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print(f" Genes (columns): {expression_data.shape[1]}")
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# Load TF names if provided
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tf_names = None
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if args.tf_file:
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print(f"Loading transcription factor names from {args.tf_file}...")
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tf_names = load_tf_names(args.tf_file)
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print(f" Found {len(tf_names)} transcription factors")
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else:
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print("No TF file provided. Using all genes as potential regulators.")
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# Run GRNBoost2
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print("\n" + "="*60)
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print("Running GRNBoost2...")
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print("="*60)
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start_time = time.time()
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grnboost2_network = grnboost2(
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expression_data=expression_data,
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tf_names=tf_names,
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seed=args.seed
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)
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grnboost2_time = time.time() - start_time
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print(f"GRNBoost2 completed in {grnboost2_time:.2f} seconds")
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# Save GRNBoost2 results
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grnboost2_network.to_csv(args.grnboost2_output, sep='\t', index=False)
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print(f"Results saved to {args.grnboost2_output}")
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# Run GENIE3
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print("\n" + "="*60)
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print("Running GENIE3...")
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print("="*60)
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start_time = time.time()
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genie3_network = genie3(
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expression_data=expression_data,
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tf_names=tf_names,
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seed=args.seed
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)
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genie3_time = time.time() - start_time
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print(f"GENIE3 completed in {genie3_time:.2f} seconds")
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# Save GENIE3 results
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genie3_network.to_csv(args.genie3_output, sep='\t', index=False)
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print(f"Results saved to {args.genie3_output}")
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# Compare runtimes
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print("\n" + "="*60)
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print("Runtime Comparison")
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print("="*60)
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print(f"GRNBoost2: {grnboost2_time:.2f} seconds")
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print(f"GENIE3: {genie3_time:.2f} seconds")
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speedup = genie3_time / grnboost2_time
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print(f"Speedup: {speedup:.2f}x (GRNBoost2 is {speedup:.2f}x faster)")
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# Compare networks
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compare_networks(
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grnboost2_network,
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genie3_network,
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"GRNBoost2",
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"GENIE3",
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top_n=args.top_n
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)
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print("\n" + "="*60)
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print("Comparison complete!")
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print("="*60)
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if __name__ == '__main__':
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main()
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@@ -1,157 +0,0 @@
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#!/usr/bin/env python3
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"""
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Distributed GRN inference script using arboreto with custom Dask configuration.
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This script demonstrates how to use arboreto with a custom Dask LocalCluster
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for better control over computational resources.
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Usage:
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python distributed_inference.py <expression_file> [options]
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Example:
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python distributed_inference.py expression_data.tsv -t tf_names.txt -w 8 -m 4GB
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"""
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import argparse
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import pandas as pd
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from dask.distributed import Client, LocalCluster
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from arboreto.algo import grnboost2
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from arboreto.utils import load_tf_names
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def main():
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parser = argparse.ArgumentParser(
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description='Distributed GRN inference using GRNBoost2 with custom Dask cluster'
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)
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parser.add_argument(
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'expression_file',
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help='Path to expression data file (TSV/CSV format)'
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)
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parser.add_argument(
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'-t', '--tf-file',
|
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help='Path to file containing transcription factor names (one per line)',
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default=None
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)
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parser.add_argument(
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'-o', '--output',
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help='Output file path for network results',
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default='network_output.tsv'
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)
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parser.add_argument(
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'-s', '--seed',
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type=int,
|
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help='Random seed for reproducibility',
|
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default=42
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)
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parser.add_argument(
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'-w', '--workers',
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type=int,
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help='Number of Dask workers',
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default=4
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)
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parser.add_argument(
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'-m', '--memory-limit',
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help='Memory limit per worker (e.g., "4GB", "2000MB")',
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default='4GB'
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)
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parser.add_argument(
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'--threads',
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type=int,
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help='Threads per worker',
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default=2
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)
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parser.add_argument(
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'--dashboard-port',
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type=int,
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help='Port for Dask dashboard (default: 8787)',
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default=8787
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)
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parser.add_argument(
|
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'--sep',
|
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help='Separator for input file (default: tab)',
|
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default='\t'
|
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)
|
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parser.add_argument(
|
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'--transpose',
|
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action='store_true',
|
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help='Transpose the expression matrix (use if genes are rows)'
|
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)
|
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|
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args = parser.parse_args()
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# Load expression data
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print(f"Loading expression data from {args.expression_file}...")
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expression_data = pd.read_csv(args.expression_file, sep=args.sep, index_col=0)
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# Transpose if needed
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if args.transpose:
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print("Transposing expression matrix...")
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expression_data = expression_data.T
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print(f"Expression data shape: {expression_data.shape}")
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print(f" Observations (rows): {expression_data.shape[0]}")
|
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print(f" Genes (columns): {expression_data.shape[1]}")
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# Load TF names if provided
|
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tf_names = None
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if args.tf_file:
|
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print(f"Loading transcription factor names from {args.tf_file}...")
|
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tf_names = load_tf_names(args.tf_file)
|
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print(f" Found {len(tf_names)} transcription factors")
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else:
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print("No TF file provided. Using all genes as potential regulators.")
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# Set up Dask cluster
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print(f"\nSetting up Dask LocalCluster...")
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print(f" Workers: {args.workers}")
|
||||
print(f" Threads per worker: {args.threads}")
|
||||
print(f" Memory limit per worker: {args.memory_limit}")
|
||||
print(f" Dashboard: http://localhost:{args.dashboard_port}")
|
||||
|
||||
cluster = LocalCluster(
|
||||
n_workers=args.workers,
|
||||
threads_per_worker=args.threads,
|
||||
memory_limit=args.memory_limit,
|
||||
diagnostics_port=args.dashboard_port
|
||||
)
|
||||
client = Client(cluster)
|
||||
|
||||
print(f"\nDask cluster ready!")
|
||||
print(f" Dashboard available at: {client.dashboard_link}")
|
||||
|
||||
# Run GRNBoost2
|
||||
print("\nRunning GRNBoost2 inference with distributed computation...")
|
||||
print(" (Monitor progress via the Dask dashboard)")
|
||||
|
||||
try:
|
||||
network = grnboost2(
|
||||
expression_data=expression_data,
|
||||
tf_names=tf_names,
|
||||
seed=args.seed,
|
||||
client_or_address=client
|
||||
)
|
||||
|
||||
print(f"\nInference complete!")
|
||||
print(f" Total regulatory links inferred: {len(network)}")
|
||||
print(f" Unique TFs: {network['TF'].nunique()}")
|
||||
print(f" Unique targets: {network['target'].nunique()}")
|
||||
|
||||
# Save results
|
||||
print(f"\nSaving results to {args.output}...")
|
||||
network.to_csv(args.output, sep='\t', index=False)
|
||||
|
||||
# Display top 10 predictions
|
||||
print("\nTop 10 predicted regulatory relationships:")
|
||||
print(network.head(10).to_string(index=False))
|
||||
|
||||
print("\nDone!")
|
||||
|
||||
finally:
|
||||
# Clean up Dask resources
|
||||
print("\nClosing Dask cluster...")
|
||||
client.close()
|
||||
cluster.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user