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pathway-enrichment

Analyze gene lists and ranked tables for enriched biological pathways.

74|5|Updated Dec 10, 2025
Search Tags:#pathway enrichment#reactome#gsea#kegg#gene ontology#gene set analysis
One-click install
npx skills add https://github.com/dralkh/seerai --skill pathway-enrichment
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pathway-enrichment
Source: https://github.com/dralkh/seerai/tree/main/skills/pathway-enrichment
Command: npx skills add https://github.com/dralkh/seerai --skill pathway-enrichment

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, gseapy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you turn a gene list or ranked gene table into biologically meaningful pathway, GO term, and gene-set enrichment results, so you can move from raw hits to interpretable biology.

Core Features & Use Cases

  • Over-representation analysis: Test thresholded hit lists for enriched GO, KEGG, Reactome, WikiPathways, and MSigDB signatures.
  • Ranked enrichment analysis: Run preranked GSEA when you have a full ordered gene score table instead of a cutoff-based list.
  • Interpretation and reporting: Handle gene-ID mapping, background selection, multiple-testing correction, redundancy reduction, and publication-ready summaries.
  • Use case: A researcher with differential expression output from RNA-seq can identify which immune, signaling, or metabolic pathways are most affected and present the results in a clean table and dotplot.

Quick Start

Ask for an enrichment analysis on your gene list or ranked genes and specify the organism, preferred gene-set libraries, and whether you want ORA or GSEA results.

Frequently Asked Questions about pathway-enrichment

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

FAQPage Schema
How do I perform pathway enrichment analysis on a gene list?▼

Pathway enrichment analysis identifies biologically meaningful gene sets from your gene list or ranked table using over-representation analysis or preranked GSEA. This Skill supports GO, KEGG, Reactome, and MSigDB libraries.

When should I use GSEA instead of over-representation analysis?▼

Use preranked GSEA when you have a full ordered gene score table without a cutoff, and use over-representation analysis when you have a thresholded hit list. This Skill handles both methods to identify enriched pathways.

Can I analyze differential expression output from RNA-seq or Scanpy?▼

Yes, you can analyze differential expression output from RNA-seq, Scanpy, CRISPR screens, and proteomics hits. The Skill interprets marker genes and ranked tables to find affected immune, signaling, or metabolic pathways.

Does this gene set analysis tool handle multiple-testing correction and gene-ID mapping?▼

Yes, gene set analysis requires correct gene-ID mapping, organism selection, and background universe control. This Skill manages multiple-testing correction, redundancy reduction, and generates publication-ready summaries.

What is the best way to visualize enriched biological pathways?▼

To visualize enriched biological pathways, this Skill uses the gseapy library to generate clean result tables and dotplots. It simplifies interpretation workflows for your gene set analysis output.

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