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bio-pathway-enrichment-visualization

Generate publication-quality ggplot2 figures from GO, KEGG, and GSEA enrichment results.

Updated Mar 30, 2026
Search Tags:#go#visualization#enrichment#gsea#kegg#enrichplot#clusterprofiler
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-pathway-enrichment-visualization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-pathway-enrichment-visualization
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-pathway-analysis-enrichment-visualization
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-pathway-enrichment-visualization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Researchers need to transform enrichment analysis results into publication-quality figures without writing custom plotting code, enabling rapid storytelling of pathway insights.

Core Features & Use Cases

  • dotplot(), barplot(), cnetplot(), emapplot(), gseaplot2(), ridgeplot(), and treeplot() can be used across GO, KEGG, and GSEA results to generate a range of visualizations.
  • Real-world scenario: Visualize GO enrichment results with a publication-ready dotplot and a gene-concept network to illustrate significance and gene contributions.
  • Use case: Produce publication-quality figures for a manuscript or presentation with minimal custom plotting.

Quick Start

Load your enrichment results and call the appropriate enrichplot-based plots to generate publication-quality figures.

Frequently Asked Questions about bio-pathway-enrichment-visualization

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

FAQPage Schema
How do I create publication-ready enrichment plots from GO and KEGG results?▼

Generate publication-ready enrichment plots by applying enrichplot functions such as dotplot() or barplot() to GO and KEGG results, producing ggplot2 figures suitable for manuscripts without writing custom code.

What visualizations can I use to show gene contributions in GSEA results?▼

Use cnetplot() to map gene-concept networks or gseaplot2() and ridgeplot() to generate GSEA-specific visuals that illustrate pathway significance and gene contributions from enrichment analysis results.

Do I need R and clusterProfiler to visualize enrichment analysis results?▼

Yes, visualizing enrichment analysis results requires R, clusterProfiler, and enrichplot to execute the plotting functions and generate ggplot2 figures for your publication-ready plots.

Can I generate an emapplot for GO enrichment without writing custom plotting code?▼

Yes, you can generate an emapplot for GO enrichment without custom plotting code by applying the enrichplot emapplot() function directly to your enrichment results to produce publication-quality network visualizations.

What is the best way to visualize pathway enrichment results for a presentation?▼

The best way to visualize pathway enrichment results for a presentation is applying enrichplot functions like treeplot() or barplot() to generate publication-quality figures that rapidly communicate pathway insights.

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