GO/KEGG Enrichment Analysis

Usage

Basic Usage

# Run enrichment analysis with gene list
python scripts/main.py --genes gene_list.txt --organism human --output results/

Parameters

Advanced Usage

# GO enrichment only with specific ontology
python scripts/main.py \
    --genes deg_upregulated.txt \
    --organism mouse \
    --analysis go \
    --go-ontologies BP,MF \
    --pvalue-cutoff 0.01 \
    --output go_results/

# KEGG enrichment with custom background
python scripts/main.py \
    --genes treatment_genes.txt \
    --background all_expressed_genes.txt \
    --organism human \
    --analysis kegg \
    --qvalue-cutoff 0.05 \
    --output kegg_results/

Input Format

Gene List File

TP53
BRCA1
EGFR
MYC
KRAS
PTEN

With Expression Values (for GSEA)

gene,log2FoldChange
TP53,2.5
BRCA1,-1.8
EGFR,3.2

Output Files

output/
├── go_enrichment/
│   ├── GO_BP_results.csv       # Biological Process results
│   ├── GO_MF_results.csv       # Molecular Function results
│   ├── GO_CC_results.csv       # Cellular Component results
│   ├── GO_BP_barplot.pdf       # Visualization
│   ├── GO_MF_dotplot.pdf
│   └── GO_summary.txt          # Interpretation summary
├── kegg_enrichment/
│   ├── KEGG_results.csv        # Pathway results
│   ├── KEGG_barplot.pdf
│   ├── KEGG_dotplot.pdf
│   └── KEGG_pathview/          # Pathway diagrams
└── combined_report.html        # Interactive report

Result Interpretation

The tool automatically generates biological interpretation including:

1. Top Enriched Terms: Significant GO terms/pathways ranked by enrichment ratio
2. Functional Themes: Clustered biological themes from enriched terms
3. Key Genes: Core genes driving enrichment in significant terms
4. Network Relationships: Gene-term relationship visualization
5. Clinical Relevance: Disease associations (for human genes)

Technical Difficulty: HIGH

⚠️ AI自主验收状态: 需人工检查

This skill requires:

• R/Bioconductor environment with clusterProfiler
• Multiple annotation databases (org.*.eg.db)
• KEGG REST API access
• Complex visualization dependencies

Dependencies

Required R Packages

install.packages(c("BiocManager", "ggplot2", "dplyr", "readr"))
BiocManager::install(c(
    "clusterProfiler", 
    "org.Hs.eg.db", "org.Mm.eg.db", "org.Rn.eg.db",
    "enrichplot", "pathview", "DOSE"
))

Python Dependencies

pip install pandas numpy matplotlib seaborn rpy2

Example Workflow

1. Prepare Input: Create gene list from DEG analysis
2. Run Analysis: Execute main.py with appropriate parameters
3. Review Results: Check generated CSV files and visualizations
4. Interpret: Read auto-generated summary for biological insights

References

See references/ for:

• clusterProfiler documentation
• KEGG API guide
• Statistical methods explanation
• Visualization examples

Limitations

• Requires internet connection for KEGG database queries
• Large gene lists (>5000) may require increased memory
• Some pathways may not be available for all organisms
• KEGG API has rate limits (max 3 requests/second)

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support