mirror of
https://github.com/K-Dense-AI/claude-scientific-skills.git
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Initial commit
This commit is contained in:
Timothy Kassis
110
scientific-packages/arboreto/scripts/basic_grn_inference.py
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110
scientific-packages/arboreto/scripts/basic_grn_inference.py
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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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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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Usage:
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python basic_grn_inference.py <expression_file> [options]
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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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"""
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import argparse
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import pandas as pd
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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='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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)
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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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'--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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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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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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205
scientific-packages/arboreto/scripts/compare_algorithms.py
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205
scientific-packages/arboreto/scripts/compare_algorithms.py
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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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157
scientific-packages/arboreto/scripts/distributed_inference.py
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157
scientific-packages/arboreto/scripts/distributed_inference.py
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@@ -0,0 +1,157 @@
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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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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}")
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print(f" Threads per worker: {args.threads}")
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print(f" Memory limit per worker: {args.memory_limit}")
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print(f" Dashboard: http://localhost:{args.dashboard_port}")
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cluster = LocalCluster(
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n_workers=args.workers,
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threads_per_worker=args.threads,
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memory_limit=args.memory_limit,
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diagnostics_port=args.dashboard_port
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)
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client = Client(cluster)
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print(f"\nDask cluster ready!")
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print(f" Dashboard available at: {client.dashboard_link}")
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# Run GRNBoost2
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print("\nRunning GRNBoost2 inference with distributed computation...")
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print(" (Monitor progress via the Dask dashboard)")
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try:
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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=args.seed,
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client_or_address=client
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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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finally:
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# Clean up Dask resources
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print("\nClosing Dask cluster...")
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client.close()
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cluster.close()
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if __name__ == '__main__':
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main()
|
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