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pathway-enrichment

Analyze gene lists and ranked tables for enriched pathways and gene sets.

Updated Jul 1, 2026
Search Tags:#bioinformatics#pathway analysis#enrichment#gsea#gene sets#ora
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill pathway-enrichment-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pathway-enrichment
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/pathway-enrichment
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill pathway-enrichment-jasrajtulsi

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 and gene-set results, so you can understand what processes are over-represented or enriched.

Core Features & Use Cases

  • Over-representation analysis for thresholded hit lists using Enrichr-style or offline enrichment workflows.
  • Preranked GSEA for full ranked gene tables, including lead-gene interpretation and FDR filtering.
  • Pathway interpretation support across GO, KEGG, Reactome, WikiPathways, Hallmark, and other MSigDB collections.
  • Practical analysis helpers such as gene-ID mapping, organism handling, background selection, plotting, and redundancy reduction.
  • Use Case: A researcher uploads differential expression results and asks for the top enriched biological processes, the key leading-edge genes, and a clear summary of the most important findings.

Quick Start

Ask the pathway-enrichment skill to analyze my gene list or ranked results, run the appropriate enrichment method, and summarize the significant pathways with the key genes involved.

Frequently Asked Questions about pathway-enrichment

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

FAQPage Schema
How do I run pathway enrichment analysis on a differential expression gene list?▼

Pathway enrichment analysis identifies over-represented biological processes by submitting thresholded gene lists to over-representation analysis or full ranked tables to preranked GSEA, summarizing significant pathways and leading-edge genes.

What is the difference between over-representation analysis and preranked GSEA for gene sets?▼

Over-representation analysis evaluates thresholded hit lists for enriched terms, while preranked GSEA analyzes full ranked gene tables to identify enriched gene sets with FDR filtering and leading-edge gene interpretation.

Can I use KEGG, Reactome, and MSigDB Hallmark gene sets for enrichment analysis?▼

Yes, pathway enrichment supports GO, KEGG, Reactome, WikiPathways, Hallmark, Enrichr, and MSigDB collections to identify enriched biological processes across differential expression, screen hits, and cluster marker workflows.

Do I need to map gene identifiers and select a background for over-representation analysis?▼

Correct gene identifiers, organism matching, and proper background selection are required for over-representation analysis to ensure accurate enrichment results and adjusted p-value control.

How do I interpret leading-edge genes and redundancy in GSEA pathway results?▼

GSEA pathway interpretation focuses on leading-edge genes driving enrichment signal, FDR-filtered significant gene sets, and redundancy reduction to clarify the most important biological findings.

Why does pathway enrichment fail when gene identifiers or organism matching is incorrect?▼

Pathway enrichment requires correct gene identifiers and organism matching to map genes to reference databases; mismatches cause failed enrichment, invalid overlap calculations, and unreliable adjusted p-values.

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