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gseapy-gene-enrichment

Perform GSEA and ORA on gene lists with Enrichr pathway lookups.

298|27|Updated Feb 18, 2026
Search Tags:#bioinformatics#pathway analysis#rna-seq#gsea#enrichment analysis#gene sets
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill gseapy-gene-enrichment
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gseapy-gene-enrichment
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/genomics-bioinformatics/gseapy-gene-enrichment
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill gseapy-gene-enrichment

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps researchers interpret large lists of genes from experiments like RNA-seq by identifying the biological pathways or functions that are significantly over-represented or enriched.

Core Features & Use Cases

  • Over-Representation Analysis (ORA): Quickly test gene lists against over 200 databases (GO, KEGG, MSigDB) via the Enrichr API.
  • Gene Set Enrichment Analysis (GSEA): Perform preranked GSEA on ranked gene lists (e.g., from differential expression analysis) to find enriched pathways.
  • Visualization: Generate publication-quality dot plots and GSEA running-score plots.
  • Use Case: After identifying differentially expressed genes in a cancer study, use this Skill to find out which known biological pathways (like 'cell cycle' or 'apoptosis') are most affected.

Quick Start

Run over-representation analysis on the provided gene list against the GO Biological Process database.

Frequently Asked Questions about gseapy-gene-enrichment

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

FAQPage Schema
What is the best way to find enriched biological pathways from a list of differentially expressed genes?▼

To find enriched biological pathways from differentially expressed genes, use over-representation analysis (ORA) against databases like GO, KEGG, and MSigDB. This Skill queries the Enrichr API to identify significantly over-represented pathways in your experimental gene list.

Can I generate publication-quality plots for GSEA running scores?▼

Yes, you can generate publication-quality visualization plots for GSEA running scores and pathway enrichment. This Skill produces dot plots and running-score plots to help you visually interpret pathway-level results from your transcriptomics or proteomics experiments.

Does this enrichment analysis tool support both GSEA prerank and ORA methods?▼

Yes, this enrichment analysis tool supports both GSEA prerank analysis on ranked gene lists and over-representation analysis (ORA) on unranked gene sets. It processes transcriptomics or proteomics data to produce pathway-level interpretation tables and plots.

Why use GSEA prerank instead of over-representation analysis for pathway interpretation?▼

Use GSEA prerank instead of over-representation analysis when you have a continuous ranked gene list from differential expression analysis, rather than a binary list. GSEA prerank evaluates the ranking distribution to detect pathway-level shifts that ORA might miss.

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