GO/KEGG Enrichment Analysis

Automated pipeline for Gene Ontology and KEGG pathway enrichment analysis with result interpretation and visualization.

Features

• GO Enrichment: Biological Process (BP), Molecular Function (MF), Cellular Component (CC)
• KEGG Pathway: Pathway enrichment with organism-specific mapping
• Multiple ID Support: Gene symbols, Entrez IDs, Ensembl IDs, RefSeq
• Statistical Methods: Hypergeometric test, Fisher's exact test, GSEA support
• Visualizations: Bar plots, dot plots, enrichment maps, cnet plots
• Result Interpretation: Automatic biological significance summary

Supported Organisms

Common Name	Scientific Name	KEGG Code	OrgDB Package
Human	Homo sapiens	hsa	org.Hs.eg.db
Mouse	Mus musculus	mmu	org.Mm.eg.db
Rat	Rattus norvegicus	rno	org.Rn.eg.db
Zebrafish	Danio rerio	dre	org.Dr.eg.db
Fly	Drosophila melanogaster	dme	org.Dm.eg.db
Yeast	Saccharomyces cerevisiae	sce	org.Sc.sgd.db

Usage

Basic Usage

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

Parameters

Parameter	Description	Default	Required
--genes	Path to gene list file (one gene per line)	-	Yes
--organism	Organism code (human/mouse/rat/zebrafish/fly/yeast)	human	No
--id-type	Gene ID type (symbol/entrez/ensembl/refseq)	symbol	No
--background	Background gene list file	all genes	No
--pvalue-cutoff	P-value cutoff for significance	0.05	No
--qvalue-cutoff	Adjusted p-value (q-value) cutoff	0.2	No
--analysis	Analysis type (go/kegg/all)	all	No
--output	Output directory	./enrichment_results	No
--format	Output format (csv/tsv/excel/all)	all	No

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

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

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