kegg-pathway-analysis - Guide KEGG Enrichment for DEG Results
Guides KEGG pathway enrichment for DEG results using ORA or GSEA, direction-aware workflows, organism codes, API fallbacks, comparisons, and answer-first reporting.
Tags
Updated: 2026-10-05Capabilities
What this skill does
- Choose ORA or GSEA
- Split ORA genes by direction
- Select KEGG organism codes
- Validate gene identifier formats
- Define background gene universes
- Handle KEGG API failures
- Use offline gene sets
- Cache pathway data
- Apply multiple-testing correction
- Compare enrichment across conditions
- Report pathway results first
Inputs
- Differential expression results
- Organism code
- Gene identifier type
- Significant gene lists
- Ranked gene list
- Background gene universe
- Testing approach
- Pathway scope
- Significance threshold
Outputs
- ORA or GSEA results
- Direction-specific enrichment results
- Significant pathway IDs
- Significant pathway counts
- Multiple-testing adjusted statistics
- Cross-condition pathway comparisons
- Cached pathway data
Requirements
- R with clusterProfiler or Python with gseapy
- KEGG API access or offline gene sets