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bio-workflows-expression-to-pathways
Maintainer FreedomIntelligence · Last updated April 1, 2026
End-to-end workflow from differential expression to GO/KEGG/Reactome enrichment and pathway visualization.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflows-expression-to-pathways
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Convert differential expression results into biological insights through functional enrichment analysis.
- res <- read.csv('deseq2_results.csv', row.names = 1).
Source Doc
Excerpt From SKILL.md
From DESeq2 Results
library(DESeq2)
library(clusterProfiler)
library(org.Hs.eg.db)
## Significant genes for ORA
sig_genes <- rownames(subset(res, padj < 0.05 & abs(log2FoldChange) > 1))
## All genes for background
all_genes <- rownames(res)
Use cases
- Use bio-workflows-expression-to-pathways to prepare publication-quality figures.
- Apply bio-workflows-expression-to-pathways when results need clear visual communication.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- pathway-analysis/go-enrichment - GO enrichment details
- pathway-analysis/kegg-pathways - KEGG analysis
- pathway-analysis/reactome-pathways - Reactome analysis
- pathway-analysis/gsea - GSEA methods
- pathway-analysis/enrichment-visualization - Visualization options
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