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topexpressinggenes

Identify top expressed genes per cluster and perform pathway enrichment analysis on scRNA-seq data.

22|4|Updated May 18, 2021
Search Tags:#visualization#enrichment#pathway-analysis#clusters#scrna-seq#tcr-bcr#top-expressing-genes
One-click install
npx skills add https://github.com/pwwang/immunopipe --skill topexpressinggenes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: topexpressinggenes
Source: https://github.com/pwwang/immunopipe/tree/main/skills/topexpressinggenes
Command: npx skills add https://github.com/pwwang/immunopipe --skill topexpressinggenes

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identifies and visualizes the top expressing genes per cluster in T/B cells, followed by pathway enrichment analysis to interpret cluster programs.

Core Features & Use Cases

  • Top gene profiling: identifies the top expressed genes per cluster.
  • Enrichment analysis: performs pathway enrichment using selected databases.
  • Visualizations: provides plots and summaries to aid quick cluster characterization.

Quick Start

Provide the SeuratClustering output to TopExpressingGenes to generate the top expressed genes per cluster and enrichment visuals.

Frequently Asked Questions about topexpressinggenes

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

FAQPage Schema
How do I identify top expressing genes per cluster in scRNA-seq data?▼

To identify top expressing genes per cluster in scRNA-seq data, apply this process to SeuratClustering outputs, which profiles top genes and performs pathway enrichment to interpret cluster functions.

What is the best way to perform pathway enrichment on T and B cell clusters?▼

Pathway enrichment for T and B cell clusters is performed by identifying top expressed genes per cluster and applying selected enrichment databases to interpret the biological programs driving those clusters.

Can I customize gene counts and enrichment databases for cluster characterization?▼

Yes, cluster characterization accepts configurable inputs for gene count, enrichment databases, and plotting options, allowing customized top gene profiling and visualization of pathway analysis results.

Does this workflow require SeuratClustering outputs to function?▼

Yes, this workflow uses upstream SeuratClustering outputs as its direct input, applying them to characterize clusters and visualize top expressed genes alongside their pathway enrichment summaries.

When do I need pathway enrichment analysis after scRNA-seq clustering?▼

Pathway enrichment analysis after scRNA-seq clustering is needed when you want to interpret the biological functions of identified clusters by moving beyond gene lists to understand underlying cellular programs.

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