enrichment

Run GSEA, ORA, and decoupler enrichment on gene expression data.

29|3|Updated Jun 11, 2026
One-click install
npx skills add https://github.com/inflexa-ai/inflexa --skill enrichment-inflexa-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: enrichment
Source: https://github.com/inflexa-ai/inflexa/tree/main/skills/enrichment
Command: npx skills add https://github.com/inflexa-ai/inflexa --skill enrichment-inflexa-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gseapy, rpy2, decoupler, pandas, numpy, and includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of interpreting high-throughput omics data by providing a standardized, reproducible framework for functional enrichment and pathway activity inference.

Core Features & Use Cases

  • Comprehensive Analysis: Supports GSEA, ORA, ssGSEA, GSVA, and decoupler-based activity inference.
  • Methodological Rigor: Enforces best practices like proper background selection for ORA and signed ranking for GSEA to ensure statistically valid results.
  • Use Case: A researcher has a list of differentially expressed genes and needs to identify the biological pathways involved; this skill guides them to select the appropriate database (e.g., MSigDB Hallmark) and method (e.g., ORA or GSEA) while avoiding common pitfalls like network-dependent KEGG calls.

Quick Start

Use the enrichment skill to perform a preranked GSEA on the provided DESeq2 results using the MSigDB hallmark gene sets.

Frequently Asked Questions about enrichment

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

FAQPage Schema
How do I perform gene set enrichment analysis on differentially expressed genes?

Gene set enrichment analysis is performed using GSEA or ORA to identify biological pathways from differentially expressed genes. The skill guides database selection, like MSigDB Hallmark, and applies Benjamini-Hochberg FDR correction.

Can I infer pathway activity from single-cell profiles using decoupler?

Pathway activity inference from single-cell profiles is supported using decoupler methods. The skill executes pathway activity inference alongside ssGSEA and GSVA to interpret single-cell gene expression data.

What's the best way to avoid common pitfalls like network-dependent KEGG calls in functional annotation?

To avoid network-dependent KEGG calls during functional annotation, the skill enforces offline reference database resolution and methodological rigor, ensuring proper background selection for ORA and signed ranking for GSEA.

Does this enrichment skill support GSVA and ssGSEA for pathway analysis?

The enrichment skill supports GSVA and ssGSEA for pathway analysis, alongside GSEA, ORA, and decoupler-based activity inference. These methods provide comprehensive functional enrichment for high-throughput omics data.

When do I need offline reference database resolution for GSEA?

Offline reference database resolution for GSEA is needed to ensure reproducible functional enrichment without network dependencies. The skill satisfies this requirement by resolving reference databases locally.

Why does ORA require proper background selection in functional enrichment?

ORA requires proper background selection in functional enrichment to ensure statistically valid results. The skill enforces this methodological rigor alongside gene set size filtering and Benjamini-Hochberg FDR correction.