Bioconductor Enrichplot

Q: Why use Bioconductor Enrichplot on TypingMind?

Because you install it once and use it with any model. Bioconductor Enrichplot is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

Q: How do I install Bioconductor Enrichplot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/enrichment/enrichplot. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Q: Which AI models can use Bioconductor Enrichplot?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

Q: How many AI models can I use with Bioconductor Enrichplot?

As many as you like. As long as a model supports skills, you can use Bioconductor Enrichplot with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Q: Is the Bioconductor Enrichplot AI skill free?

It is published on GitHub by bioMate-AI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

Q: What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

Q: How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Organization

bioMate-AI

bioconductor-enrichplot

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

Overview

Publisher	bioMate-AI
Repository	`biomate-bioconductor-kb`
Skill name	`bioconductor-enrichplot`
Stars	804
Forks	66
Bundled files	Instructions only
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by bioMate-AI on GitHub. Read the source before you install it.

Installation

Install the Bioconductor Enrichplot AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/enrichment/enrichplot
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/enrichment/enrichplot

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/bioMate-AI/biomate-bioconductor-kb.git /tmp/biomate-bioconductor-kb
mkdir -p .claude/skills
cp -r /tmp/biomate-bioconductor-kb/skills/enrichment/enrichplot .claude/skills/bioconductor-enrichplot

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioconductor Enrichplot in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bioconductor Enrichplot on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bioconductor Enrichplot is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Key Parameters

showCategory (5): Number of enriched terms/categories to display in the plot.
color ("p.adjust"): Variable used to color nodes or bars (e.g., "pvalue", "p.adjust", "qvalue").
layout ("kk"): Layout algorithm for network plots like cnetplot or emapplot.
foldChange (NULL): Numeric vector of fold changes to color gene nodes in cnetplot or heatplot.
geneSetID: Numeric index or character ID of the gene set to plot in gseaplot2.

Best Practices

Calculate Similarities: Calculate pairwise term similarities using pairwise_termsim() before running emapplot() or treeplot().
Identify Hub Genes: Use cnetplot to identify hub genes that contribute to multiple enriched pathways.
Avoid Overcrowding: Adjust the showCategory parameter to avoid overcrowded plots when dealing with many enriched terms.
Customize with ggplot2: Leverage ggplot2 functions (e.g., theme(), labs()) to customize the output plots since all enrichplot figures are standard ggplot objects.

Common Pitfalls

Missing Similarity Matrix: Running emapplot or treeplot throws an error about missing term similarity; fix this by running x <- pairwise_termsim(x) on your enrichment object first.
Overlapping Labels: Node labels in network plots overlap and are unreadable; fix this by setting cex_label_category or using ggrepel options if supported.
Mismatched Gene IDs: Gene names in cnetplot are displayed as cryptic Entrez IDs; fix this by using setReadable() from clusterProfiler to translate IDs to gene symbols before plotting.

Alternatives

clusterProfiler: Contains basic built-in plotting functions (barplots, dotplots) but lacks advanced network visualizations.
pathfindR: Offers active-subnetwork-oriented enrichment and visualization.
enrichViewNet: Specifically designed for visualizing enrichment networks using interactive layouts.

Citations

Yu, G. (2020). yulab-smu.top/biomedical-knowledge-mining-book/.

References

Homepage: https://bioconductor.org/packages/enrichplot
Vignette: https://bioconductor.org/packages/release/bioc/vignettes/enrichplot/inst/doc/enrichplot.html

Run this on BioMate

This skill is the knowledge layer — when, why, and how to use enrichplot. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open enrichplot on BioMate →

Frequently asked questions

What does the Bioconductor Enrichplot AI skill do?

Why use Bioconductor Enrichplot on TypingMind?

Because you install it once and use it with any model. Bioconductor Enrichplot is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bioconductor Enrichplot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/enrichment/enrichplot. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bioconductor Enrichplot?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bioconductor Enrichplot?

As many as you like. As long as a model supports skills, you can use Bioconductor Enrichplot with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bioconductor Enrichplot AI skill free?

It is published on GitHub by bioMate-AI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

enrichplot

Dependencies & Environment

When to Use

When NOT to Use

Data Requirements

Key Parameters

Best Practices

Common Pitfalls

Alternatives

Citations

References

Run this on BioMate

Frequently asked questions

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