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bio-workflows-expression-to-pathways

Perform GO, KEGG, and Reactome enrichment and GSEA on differential expression results in R.

Updated Aug 23, 2026
Search Tags:#go#expression#enrichment#reactome#bioconductor#gsea#kegg
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-workflows-expression-to-pathways-stellaromics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-workflows-expression-to-pathways
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-workflows-expression-to-pathways
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-workflows-expression-to-pathways-stellaromics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Researchers need to translate differential expression results into biological meaning by identifying enriched pathways and visualizing the results across GO, KEGG, and Reactome.

Core Features & Use Cases

  • GO, KEGG, and Reactome enrichment using clusterProfiler and related Bioconductor packages.
  • GSEA on ranked gene lists and visualization of enrichment results.
  • End-to-end workflow: input DE genes or ranked lists, producing enrichment tables and figures.

Quick Start

Provide a DE gene list or ranked genes and this workflow will perform GO/KEGG/Reactome enrichment, run GSEA, and generate enrichment plots.

Frequently Asked Questions about bio-workflows-expression-to-pathways

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run GO, KEGG, and Reactome enrichment on differential expression results?▼

To run GO, KEGG, and Reactome enrichment on differential expression results, provide a DE gene list or ranked genes to the workflow. It processes the inputs in R using Bioconductor packages like clusterProfiler and ReactomePA to generate enrichment tables and plots.

Can I use Bioconductor packages like clusterProfiler for pathway enrichment visualization?▼

Yes, this workflow uses Bioconductor packages including clusterProfiler and ReactomePA for pathway enrichment visualization. It accepts gene identifiers and outputs consolidated results packages containing enrichment tables and figures.

What is the best way to translate differential expression gene lists into biological pathway insights?▼

The best way to translate differential expression gene lists into biological pathway insights is through enrichment analysis. This workflow applies GO, KEGG, Reactome, and GSEA to DE gene sets, outputting functional insights and visualization plots.

Do I need ranked gene sets or just a DE gene list for GSEA enrichment analysis?▼

For GSEA enrichment analysis, you need a ranked gene set rather than a simple DE gene list. This workflow accepts both formats, applying standard enrichment to DE lists and GSEA specifically to ranked gene sets.

What gene identifier format is expected for GO and KEGG enrichment analysis?▼

GO and KEGG enrichment analysis expects standard gene identifiers within your DE gene list or ranked gene sets. The workflow processes these inputs in R to derive functional insights and output enrichment tables and plots.

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