scfgsea

Rank genes by differential expression to identify enriched pathways in single-cell data.

22|4|Updated May 18, 2021

Search Tags:#single-cell #differential-expression #pathway-analysis #scrna-seq #gsea #fgsea #ranked-genes

Ready to run

npx skills add https://github.com/pwwang/immunopipe --skill scfgsea

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scfgsea
Source: https://github.com/pwwang/immunopipe/tree/main/skills/scfgsea
Command: npx skills add https://github.com/pwwang/immunopipe --skill scfgsea

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quickly identifying enriched biological pathways in single-cell datasets by analyzing ranked genes derived from differential expression, enabling interpretable pathway-level insights.

Core Features & Use Cases

Fast GSEA on single-cell data using the fgsea R package.
Rank genes by differential expression between cell groups and compute enrichment scores, p-values, and NES.
Generate publication-ready visualizations and exportable results for downstream reporting.
Use cases include cluster interpretation, pathway-driven hypotheses in disease vs control studies, and subgroup analyses with custom gene sets.

Quick Start

Run scfgsea on a ranked gene list derived from Seurat clusters to identify enriched pathways.

Frequently Asked Questions about scfgsea

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

FAQPage Schema

How do I perform GSEA on single-cell RNA-seq data?▼

You can perform GSEA on single-cell RNA-seq data by ranking genes using differential expression between cell groups and computing enrichment scores, p-values, and NES with the fgsea R package.

What is the best way to identify enriched pathways in Seurat clusters?▼

The best way to identify enriched pathways in Seurat clusters is to run fgsea-powered GSEA on a ranked gene list derived from cluster differential expression to generate enrichment scores and publication-ready plots.

Can I use custom GMT gene sets for single-cell pathway analysis?▼

Yes, you can use custom GMT gene sets for single-cell pathway analysis. The tool applies configurable gene sets to ranked gene lists to identify enriched pathways across cluster comparisons and subgroup studies.

How do I rank genes for GSEA in disease vs control single-cell studies?▼

To rank genes for GSEA in disease vs control single-cell studies, calculate differential expression between the conditions and use the resulting ranked gene list as input to compute enrichment scores and p-values.

What outputs do I get from fgsea analysis of single-cell data?▼

Outputs from fgsea analysis of single-cell data include enrichment scores, p-values, normalized enrichment scores (NES), and publication-ready visualizations that are exportable for downstream reporting.