# Bioconductor Enrichplot

The 'enrichplot' package implements several visualization methods for interpreting...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Design
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-enrichplot -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-enrichplot -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-enrichplot -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-enrichplot -g -a gemini-cli -y`

## Description

The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualizati

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# enrichplot

## Dependencies & Environment

> Package-intrinsic requirements from the Bioconductor landing page, reproduce in any R environment.

- **Version:** 1.32.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Imports:** aplot, DOSE, dplyr, enrichit, ggfun, ggnewscale, ggplot2, ggrepel, ggtangle, ggtree, GOSemSim, igraph, purrr, RColorBrewer, reshape2, rlang, scatterpie, tidydr, yulab.utils
- **Install:** `BiocManager::install("enrichplot")`

## When to Use
- **Enrichment Visualization**: Visualizing functional enrichment results (Over-Representation Analysis [ORA] or Gene Set Enrichment Analysis [GSEA]) generated by `clusterProfiler`, `DOSE`, `meshes`, or `ReactomePA`.
- **Network Plots**: Creating advanced network plots (e.g., `cnetplot` to show gene-concept networks, `emapplot` for enrichment map plots).
- **GSEA Running Scores**: Generating publication-ready GSEA running score plots (`gseaplot2`) showing the distribution of enrichment scores across ranked gene lists.
- **Functional Clustering**: Visualizing functional similarities and clustering of enriched terms using tree plots (`treeplot`) or heatplots (`heatplot`).

## When NOT to Use
- For performing the actual enrichment calculations, use `clusterProfiler` or `fgsea` instead because `enrichplot` is strictly a visualization package.
- For generic, non-biological network visualizations, use `ggraph` or `igraph` directly to avoid enrichment-specific object constraints.

## Data Requirements
- **Enrichment Result**: An enrichment result object (typically of class `enrichResult` or `gseaResult` from `clusterProfiler` or related packages).
- **Fold Changes**: For gene-concept networks, a named vector of fold changes (numeric) is highly recommended to color gene nodes.

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/enrichment/enrichplot/SKILL.md