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enrichment-ora

Identify enriched pathways in gene lists using Fisher's exact test across KEGG, Reactome, MSigDB Hallmark, WikiPathways, and GO gene sets.

3|Updated Apr 3, 2026
Search Tags:#omics#enrichment#gene-list#pathway#ora#gseapy
One-click install
npx skills add https://github.com/chenyhvvvv/STAT-agent --skill enrichment-ora
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: enrichment-ora
Source: https://github.com/chenyhvvvv/STAT-agent/tree/main/stat_agent/skills/enrichment-ora-ssgsea
Command: npx skills add https://github.com/chenyhvvvv/STAT-agent --skill enrichment-ora

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ORA helps researchers determine whether their gene lists show significant enrichment in known biological pathways or gene sets, enabling faster interpretation of omics data.

Core Features & Use Cases

  • Over-Representation Analysis: Fisher's exact test to identify enriched pathways from a user-provided gene list.
  • Flexible gene sets: Supports KEGG, Reactome, MSigDB Hallmark, WikiPathways, and GO databases via gseapy.
  • Real-world use case: Given a list of DE genes from an experiment, obtain enriched pathways to interpret biological themes.

Quick Start

Run an ORA on your gene list to identify significantly enriched pathways and view results, then inspect adata.uns['ora_results'] for results.

Frequently Asked Questions about enrichment-ora

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

FAQPage Schema
How do I find enriched pathways from a gene list using ORA?▼

Over-Representation Analysis (ORA) identifies enriched pathways by applying Fisher's exact test to a gene list. It compares your input against KEGG, Reactome, MSigDB Hallmark, WikiPathways, and GO gene sets to highlight significantly over-represented biological themes.

What gene set databases are supported for enrichment analysis?▼

Enrichment analysis supports KEGG, Reactome, MSigDB Hallmark, WikiPathways, and GO gene sets. These databases are accessed via gseapy to calculate over-representation across known biological pathways.

Can I use mouse gene names for pathway enrichment analysis?▼

Pathway enrichment analysis supports both human and mouse gene names. You need to provide your gene list, specify the species, and select a target library to generate accurate enrichment results.

How do I interpret Fisher's exact test results for enriched pathways?▼

Fisher's exact test results for enriched pathways are stored in adata.uns['ora_results']. This output contains significantly over-represented gene sets, allowing you to quickly interpret the biological themes active in your differential expression data.

Do I need a background gene list for over-representation analysis?▼

Over-representation analysis requires a gene list input, species specification, and library selection. The Fisher's exact test evaluates whether your provided genes are over-represented in specific pathways compared to the background.

What is the best way to analyze differentially expressed genes for biological themes?▼

The best way to analyze differentially expressed genes for biological themes is over-representation analysis. It applies Fisher's exact test across KEGG, Reactome, and GO to identify significantly enriched pathways from your experimental gene list.

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