Data & ReproScientific VisualizationFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-pathway-enrichment-visualization

Maintainer FreedomIntelligence · Last updated April 1, 2026

Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.

OpenClawNanoClawAnalysisWritingbio-pathway-enrichment-visualization🧬 bioinformatics (gptomics bio-* suite)bioinformatics — pathway & network analysisvisualize

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-pathway-enrichment-visualization

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

R: dotplot(), cnetplot(), emapplot(), gseaplot2() (enrichplot).
Create publication-quality plots from my enrichment analysis" → Generate dotplots, gene-concept networks, enrichment maps, GSEA running score plots, and ridgeplots from clusterProfiler results. R: dotplot(), cnetplot(), emapplot(), gseaplot2() (enrichplot).
dotplot(ego, showCategory = 15, font.size = 10, title = 'GO Enrichment') + scale_color_gradient(low = 'red', high = 'blue').

Source Doc

Excerpt From SKILL.md

Scope

This skill covers enrichplot package functions designed for clusterProfiler results:

dotplot(), barplot() - Summary views
cnetplot(), emapplot(), treeplot() - Network/hierarchical views
gseaplot2(), ridgeplot() - GSEA-specific
goplot(), heatplot(), upsetplot() - Specialized views

For custom ggplot2 enrichment dotplots (manual implementation), see data-visualization/specialized-omics-plots.

Setup

Goal: Load required packages for visualizing enrichment analysis results.

Approach: Import clusterProfiler, enrichplot, and ggplot2 which provide the plotting functions for enrichment objects.

library(clusterProfiler)
library(enrichplot)
library(ggplot2)

## Dot Plot

**Goal:** Summarize enrichment results showing gene ratio, count, and significance in a single figure.

**Approach:** Use enrichplot dotplot which maps gene ratio to x-axis, term to y-axis, dot size to count, and color to p-value.

Most common visualization - shows gene ratio, count, and significance.

```r
dotplot(ego, showCategory = 20)