Initial commit
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Zhongwei Li
309
skills/bioservices/scripts/pathway_analysis.py
Executable file
309
skills/bioservices/scripts/pathway_analysis.py
Executable file
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#!/usr/bin/env python3
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"""
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KEGG Pathway Network Analysis
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This script analyzes all pathways for an organism and extracts:
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- Pathway sizes (number of genes)
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- Protein-protein interactions
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- Interaction type distributions
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- Network data in various formats (CSV, SIF)
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Usage:
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python pathway_analysis.py ORGANISM OUTPUT_DIR [--limit N]
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Examples:
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python pathway_analysis.py hsa ./human_pathways
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python pathway_analysis.py mmu ./mouse_pathways --limit 50
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Organism codes:
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hsa = Homo sapiens (human)
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mmu = Mus musculus (mouse)
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dme = Drosophila melanogaster
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sce = Saccharomyces cerevisiae (yeast)
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eco = Escherichia coli
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"""
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import sys
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import os
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import argparse
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import csv
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from collections import Counter
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from bioservices import KEGG
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def get_all_pathways(kegg, organism):
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"""Get all pathway IDs for organism."""
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print(f"\nRetrieving pathways for {organism}...")
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kegg.organism = organism
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pathway_ids = kegg.pathwayIds
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print(f"✓ Found {len(pathway_ids)} pathways")
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return pathway_ids
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def analyze_pathway(kegg, pathway_id):
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"""Analyze single pathway for size and interactions."""
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try:
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# Parse KGML pathway
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kgml = kegg.parse_kgml_pathway(pathway_id)
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entries = kgml.get('entries', [])
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relations = kgml.get('relations', [])
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# Count relation types
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relation_types = Counter()
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for rel in relations:
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rel_type = rel.get('name', 'unknown')
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relation_types[rel_type] += 1
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# Get pathway name
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try:
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entry = kegg.get(pathway_id)
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pathway_name = "Unknown"
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for line in entry.split("\n"):
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if line.startswith("NAME"):
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pathway_name = line.replace("NAME", "").strip()
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break
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except:
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pathway_name = "Unknown"
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result = {
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'pathway_id': pathway_id,
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'pathway_name': pathway_name,
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'num_entries': len(entries),
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'num_relations': len(relations),
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'relation_types': dict(relation_types),
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'entries': entries,
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'relations': relations
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}
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return result
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except Exception as e:
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print(f" ✗ Error analyzing {pathway_id}: {e}")
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return None
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def analyze_all_pathways(kegg, pathway_ids, limit=None):
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"""Analyze all pathways."""
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if limit:
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pathway_ids = pathway_ids[:limit]
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print(f"\n⚠ Limiting analysis to first {limit} pathways")
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print(f"\nAnalyzing {len(pathway_ids)} pathways...")
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results = []
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for i, pathway_id in enumerate(pathway_ids, 1):
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print(f" [{i}/{len(pathway_ids)}] {pathway_id}", end="\r")
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result = analyze_pathway(kegg, pathway_id)
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if result:
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results.append(result)
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print(f"\n✓ Successfully analyzed {len(results)}/{len(pathway_ids)} pathways")
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return results
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def save_pathway_summary(results, output_file):
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"""Save pathway summary to CSV."""
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print(f"\nSaving pathway summary to {output_file}...")
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with open(output_file, 'w', newline='') as f:
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writer = csv.writer(f)
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# Header
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writer.writerow([
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'Pathway_ID',
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'Pathway_Name',
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'Num_Genes',
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'Num_Interactions',
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'Activation',
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'Inhibition',
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'Phosphorylation',
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'Binding',
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'Other'
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])
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# Data
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for result in results:
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rel_types = result['relation_types']
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writer.writerow([
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result['pathway_id'],
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result['pathway_name'],
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result['num_entries'],
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result['num_relations'],
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rel_types.get('activation', 0),
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rel_types.get('inhibition', 0),
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rel_types.get('phosphorylation', 0),
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rel_types.get('binding/association', 0),
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sum(v for k, v in rel_types.items()
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if k not in ['activation', 'inhibition', 'phosphorylation', 'binding/association'])
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])
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print(f"✓ Summary saved")
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def save_interactions_sif(results, output_file):
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"""Save all interactions in SIF format."""
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print(f"\nSaving interactions to {output_file}...")
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with open(output_file, 'w') as f:
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for result in results:
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pathway_id = result['pathway_id']
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for rel in result['relations']:
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entry1 = rel.get('entry1', '')
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entry2 = rel.get('entry2', '')
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interaction_type = rel.get('name', 'interaction')
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# Write SIF format: source\tinteraction\ttarget
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f.write(f"{entry1}\t{interaction_type}\t{entry2}\n")
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print(f"✓ Interactions saved")
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def save_detailed_pathway_info(results, output_dir):
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"""Save detailed information for each pathway."""
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print(f"\nSaving detailed pathway files to {output_dir}/pathways/...")
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pathway_dir = os.path.join(output_dir, "pathways")
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os.makedirs(pathway_dir, exist_ok=True)
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for result in results:
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pathway_id = result['pathway_id'].replace(":", "_")
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filename = os.path.join(pathway_dir, f"{pathway_id}_interactions.csv")
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with open(filename, 'w', newline='') as f:
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writer = csv.writer(f)
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writer.writerow(['Source', 'Target', 'Interaction_Type', 'Link_Type'])
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for rel in result['relations']:
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writer.writerow([
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rel.get('entry1', ''),
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rel.get('entry2', ''),
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rel.get('name', 'unknown'),
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rel.get('link', 'unknown')
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])
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print(f"✓ Detailed files saved for {len(results)} pathways")
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def print_statistics(results):
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"""Print analysis statistics."""
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print(f"\n{'='*70}")
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print("PATHWAY ANALYSIS STATISTICS")
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print(f"{'='*70}")
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# Total stats
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total_pathways = len(results)
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total_interactions = sum(r['num_relations'] for r in results)
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total_genes = sum(r['num_entries'] for r in results)
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print(f"\nOverall:")
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print(f" Total pathways: {total_pathways}")
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print(f" Total genes/proteins: {total_genes}")
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print(f" Total interactions: {total_interactions}")
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# Largest pathways
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print(f"\nLargest pathways (by gene count):")
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sorted_by_size = sorted(results, key=lambda x: x['num_entries'], reverse=True)
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for i, result in enumerate(sorted_by_size[:10], 1):
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print(f" {i}. {result['pathway_id']}: {result['num_entries']} genes")
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print(f" {result['pathway_name']}")
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# Most connected pathways
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print(f"\nMost connected pathways (by interactions):")
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sorted_by_connections = sorted(results, key=lambda x: x['num_relations'], reverse=True)
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for i, result in enumerate(sorted_by_connections[:10], 1):
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print(f" {i}. {result['pathway_id']}: {result['num_relations']} interactions")
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print(f" {result['pathway_name']}")
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# Interaction type distribution
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print(f"\nInteraction type distribution:")
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all_types = Counter()
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for result in results:
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for rel_type, count in result['relation_types'].items():
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all_types[rel_type] += count
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for rel_type, count in all_types.most_common():
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percentage = (count / total_interactions) * 100 if total_interactions > 0 else 0
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print(f" {rel_type}: {count} ({percentage:.1f}%)")
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def main():
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"""Main analysis workflow."""
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parser = argparse.ArgumentParser(
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description="Analyze KEGG pathways for an organism",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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python pathway_analysis.py hsa ./human_pathways
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python pathway_analysis.py mmu ./mouse_pathways --limit 50
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Organism codes:
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hsa = Homo sapiens (human)
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mmu = Mus musculus (mouse)
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dme = Drosophila melanogaster
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sce = Saccharomyces cerevisiae (yeast)
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eco = Escherichia coli
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"""
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)
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parser.add_argument("organism", help="KEGG organism code (e.g., hsa, mmu)")
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parser.add_argument("output_dir", help="Output directory for results")
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parser.add_argument("--limit", type=int, default=None,
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help="Limit analysis to first N pathways")
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args = parser.parse_args()
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print("=" * 70)
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print("BIOSERVICES: KEGG Pathway Network Analysis")
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print("=" * 70)
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# Create output directory
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os.makedirs(args.output_dir, exist_ok=True)
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# Initialize KEGG
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kegg = KEGG()
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# Get all pathways
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pathway_ids = get_all_pathways(kegg, args.organism)
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if not pathway_ids:
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print(f"\n✗ No pathways found for {args.organism}")
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sys.exit(1)
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# Analyze pathways
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results = analyze_all_pathways(kegg, pathway_ids, args.limit)
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if not results:
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print("\n✗ No pathways successfully analyzed")
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sys.exit(1)
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# Print statistics
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print_statistics(results)
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# Save results
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summary_file = os.path.join(args.output_dir, "pathway_summary.csv")
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save_pathway_summary(results, summary_file)
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sif_file = os.path.join(args.output_dir, "all_interactions.sif")
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save_interactions_sif(results, sif_file)
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save_detailed_pathway_info(results, args.output_dir)
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# Final summary
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print(f"\n{'='*70}")
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print("OUTPUT FILES")
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print(f"{'='*70}")
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print(f" Summary: {summary_file}")
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print(f" Interactions: {sif_file}")
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print(f" Detailed: {args.output_dir}/pathways/")
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print(f"{'='*70}")
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if __name__ == "__main__":
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main()
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