Initial commit
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Zhongwei Li
235
skills/gget/scripts/enrichment_pipeline.py
Executable file
235
skills/gget/scripts/enrichment_pipeline.py
Executable file
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#!/usr/bin/env python3
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"""
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Enrichment Analysis Pipeline
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Perform comprehensive enrichment analysis on a gene list
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"""
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import argparse
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import sys
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from pathlib import Path
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import gget
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import pandas as pd
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def read_gene_list(file_path):
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"""Read gene list from file (one gene per line or CSV)."""
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file_path = Path(file_path)
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if file_path.suffix == ".csv":
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df = pd.read_csv(file_path)
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# Assume first column contains gene names
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genes = df.iloc[:, 0].tolist()
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else:
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# Plain text file
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with open(file_path, "r") as f:
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genes = [line.strip() for line in f if line.strip()]
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return genes
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def enrichment_pipeline(
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gene_list,
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species="human",
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background=None,
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output_prefix="enrichment",
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plot=True,
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):
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"""
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Perform comprehensive enrichment analysis.
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Args:
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gene_list: List of gene symbols
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species: Species for analysis
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background: Background gene list (optional)
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output_prefix: Prefix for output files
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plot: Whether to generate plots
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"""
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print("Enrichment Analysis Pipeline")
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print("=" * 60)
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print(f"Analyzing {len(gene_list)} genes")
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print(f"Species: {species}\n")
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# Database categories to analyze
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databases = {
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"pathway": "KEGG Pathways",
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"ontology": "Gene Ontology (Biological Process)",
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"transcription": "Transcription Factors (ChEA)",
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"diseases_drugs": "Disease Associations (GWAS)",
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"celltypes": "Cell Type Markers (PanglaoDB)",
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}
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results = {}
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for db_key, db_name in databases.items():
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print(f"\nAnalyzing: {db_name}")
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print("-" * 60)
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try:
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enrichment = gget.enrichr(
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gene_list,
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database=db_key,
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species=species,
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background_list=background,
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plot=plot,
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)
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if enrichment is not None and len(enrichment) > 0:
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# Save results
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output_file = f"{output_prefix}_{db_key}.csv"
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enrichment.to_csv(output_file, index=False)
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print(f"Results saved to: {output_file}")
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# Show top 5 results
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print(f"\nTop 5 enriched terms:")
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for i, row in enrichment.head(5).iterrows():
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term = row.get("name", row.get("term", "Unknown"))
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p_val = row.get(
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"adjusted_p_value",
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row.get("p_value", row.get("Adjusted P-value", 1)),
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)
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print(f" {i+1}. {term}")
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print(f" P-value: {p_val:.2e}")
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results[db_key] = enrichment
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else:
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print("No significant results found")
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except Exception as e:
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print(f"Error: {e}")
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# Generate summary report
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print("\n" + "=" * 60)
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print("Generating summary report...")
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summary = []
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for db_key, db_name in databases.items():
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if db_key in results and len(results[db_key]) > 0:
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summary.append(
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{
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"Database": db_name,
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"Total Terms": len(results[db_key]),
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"Top Term": results[db_key].iloc[0].get(
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"name", results[db_key].iloc[0].get("term", "N/A")
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),
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}
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)
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if summary:
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summary_df = pd.DataFrame(summary)
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summary_file = f"{output_prefix}_summary.csv"
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summary_df.to_csv(summary_file, index=False)
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print(f"\nSummary saved to: {summary_file}")
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print("\n" + summary_df.to_string(index=False))
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else:
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print("\nNo enrichment results to summarize")
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# Get expression data for genes
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print("\n" + "=" * 60)
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print("Getting expression data for input genes...")
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try:
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# Get tissue expression for first few genes
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expr_data = []
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for gene in gene_list[:5]: # Limit to first 5
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print(f" Getting expression for {gene}...")
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try:
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tissue_expr = gget.archs4(gene, which="tissue")
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top_tissue = tissue_expr.nlargest(1, "median").iloc[0]
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expr_data.append(
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{
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"Gene": gene,
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"Top Tissue": top_tissue["tissue"],
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"Median Expression": top_tissue["median"],
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}
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)
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except Exception as e:
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print(f" Warning: {e}")
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if expr_data:
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expr_df = pd.DataFrame(expr_data)
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expr_file = f"{output_prefix}_expression.csv"
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expr_df.to_csv(expr_file, index=False)
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print(f"\nExpression data saved to: {expr_file}")
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except Exception as e:
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print(f"Error getting expression data: {e}")
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print("\n" + "=" * 60)
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print("Enrichment analysis complete!")
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print(f"\nOutput files (prefix: {output_prefix}):")
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for db_key in databases.keys():
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if db_key in results:
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print(f" - {output_prefix}_{db_key}.csv")
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print(f" - {output_prefix}_summary.csv")
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print(f" - {output_prefix}_expression.csv")
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return True
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def main():
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parser = argparse.ArgumentParser(
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description="Perform comprehensive enrichment analysis using gget"
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)
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parser.add_argument(
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"genes",
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help="Gene list file (one gene per line or CSV with genes in first column)",
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)
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parser.add_argument(
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"-s",
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"--species",
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default="human",
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help="Species (human, mouse, fly, yeast, worm, fish)",
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)
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parser.add_argument(
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"-b", "--background", help="Background gene list file (optional)"
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)
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parser.add_argument(
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"-o", "--output", default="enrichment", help="Output prefix (default: enrichment)"
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)
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parser.add_argument(
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"--no-plot", action="store_true", help="Disable plotting"
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)
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args = parser.parse_args()
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# Read gene list
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if not Path(args.genes).exists():
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print(f"Error: File not found: {args.genes}")
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sys.exit(1)
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try:
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gene_list = read_gene_list(args.genes)
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print(f"Read {len(gene_list)} genes from {args.genes}")
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# Read background if provided
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background = None
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if args.background:
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if Path(args.background).exists():
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background = read_gene_list(args.background)
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print(f"Read {len(background)} background genes from {args.background}")
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else:
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print(f"Warning: Background file not found: {args.background}")
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success = enrichment_pipeline(
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gene_list,
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species=args.species,
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background=background,
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output_prefix=args.output,
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plot=not args.no_plot,
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)
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sys.exit(0 if success else 1)
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except KeyboardInterrupt:
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print("\n\nAnalysis interrupted by user")
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sys.exit(1)
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except Exception as e:
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print(f"\n\nError: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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if __name__ == "__main__":
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main()
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