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bulkrna-enrichment

Performs pathway enrichment analysis on bulk RNA-seq differential expression results using ORA or GSEA methods.

155|26|Updated Mar 14, 2026
Search Tags:#pathway analysis#GSEA#enrichment#ORA#bulkrna#gene set enrichment
One-click install
npx skills add https://github.com/TianGzlab/OmicsClaw --skill bulkrna-enrichment
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bulkrna-enrichment
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/bulkrna/bulkrna-enrichment
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill bulkrna-enrichment

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the complex process of identifying biological pathways and functions enriched in bulk RNA-seq differential expression results, eliminating manual data wrangling and tool switching.

Core Features & Use Cases

  • Automated Enrichment Analysis: Performs Over-Representation Analysis (ORA) or Gene Set Enrichment Analysis (GSEA) on differential expression data.
  • Flexible Pathway Databases: Supports standard databases (GO, KEGG, Reactome via GSEApy) and custom gene sets.
  • Publication-Ready Outputs: Generates clear reports, plots, and data tables for biological interpretation and reporting.
  • Use Case: After identifying differentially expressed genes between a treatment and control group, use this Skill to quickly determine which biological pathways are significantly affected, providing immediate insights into the molecular mechanisms.

Quick Start

Run pathway enrichment analysis on your bulk RNA differential expression results using the default ORA method.

Frequently Asked Questions about bulkrna-enrichment

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

FAQPage Schema
How do I perform pathway enrichment analysis on bulk RNA-seq differential expression results?▼

Pathway enrichment analysis on bulk RNA-seq data is performed using Over-Representation Analysis (ORA) or Gene Set Enrichment Analysis (GSEA). The Skill automates this process on differential expression results to identify significantly affected biological pathways.

What is the difference between ORA and GSEA for gene set enrichment analysis?▼

Gene set enrichment analysis via ORA tests pre-defined gene lists for over-representation, while GSEA evaluates ranking statistics across the entire differential expression profile. The Skill supports both methods to uncover affected molecular mechanisms.

Can I use custom gene sets for enrichment analysis instead of standard databases?▼

Custom gene sets are supported for pathway enrichment analysis alongside standard databases. The Skill integrates GSEApy for standard databases like GO, KEGG, and Reactome, and includes a built-in hypergeometric fallback for ORA.

Do I need GSEApy installed to run GSEA on my RNA-seq data?▼

GSEApy is utilized for GSEA and standard database queries, but a built-in hypergeometric fallback is provided for ORA. This ensures basic enrichment analysis can proceed even without full GSEApy integration.

What outputs are generated when identifying biological pathways from RNA-seq data?▼

Identifying biological pathways from RNA-seq data generates publication-ready outputs including structured data tables and visualizations. These results provide clear biological interpretation of the differential expression mechanisms.

Why should I use automated enrichment analysis instead of manual data wrangling?▼

Automated enrichment analysis eliminates manual data wrangling and tool switching when processing bulk RNA-seq results. It directly calculates pathway significance and generates structured tables, accelerating biological interpretation.

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