# Bioconductor Clusterprofiler

> This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources

- Skill: `biomate-ai/bioconductor-clusterprofiler` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-clusterprofiler`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-clusterprofiler/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/bioconductor-clusterprofiler

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# clusterProfiler

## Dependencies & Environment

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

- **Version:** 4.20.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Imports:** aisdk, AnnotationDbi, dplyr, enrichit, enrichplot, ggplot2, GO.db, GOSemSim, gson, httr, igraph, jsonlite, magrittr, plyr, qvalue, rlang, tidyr, yulab.utils
- **Install:** `BiocManager::install("clusterProfiler")`

## When to Use
- Performing Over-Representation Analysis or Gene Set Enrichment Analysis.
- Comparing biological themes among gene clusters.
- Visualizing functional profiles of genomic coordinates (supported by ChIPseeker), genes, and gene clusters.
- Querying Gene Ontology annotations online via AnnotationHub or KEGG Pathway and Module data.

## When NOT to Use
- For purely interactive web-based enrichment without an R environment (use web portals like DAVID directly).
- When analyzing species not supported by online databases (unless providing customized user annotations).

## Data Requirements
- Genomic coordinates, gene lists, or gene clusters.
- Annotations from supported ontologies/pathways (e.g., Disease Ontology, DisGeNET, Gene Ontology, KEGG, Reactome, Molecular Signatures Database) or customized user ontologies.

## Key Parameters
- No specific parameters are detailed in the provided vignette text.

## Best Practices
- Utilize the package's built-in visualization functions such as `barplot`, `cnetplot`, `dotplot`, `emapplot`, `gseaplot`, `goplot`, and `upsetplot` to interpret enrichment results.
- When querying Gene Ontology, use AnnotationHub to support many species with online annotation queries.
- Provide a reproducible example when posting bugs to the GitHub issue tracker.

## Common Pitfalls
- Failing to find answers to common problems because the user did not visit the clusterProfiler homepage documentation first.
- Posting questions to the Bioconductor support site without tagging the post with `clusterProfiler`, leading to delayed responses.
- Attempting to analyze unsupported species without supplying a customized ontology or user annotation.

## Alternatives
- `DOSE`: Specifically focused on Disease Ontology and Network of Cancer Gene enrichment.
- `ReactomePA`: Specifically tailored for Reactome Pathway analysis.
- `goseq`: Alternative for GO enrichment that explicitly corrects for RNA-seq transcript length bias.

## Citations
- G Yu, LG Wang, Y Han, QY He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology 2012, 16(5):284-287. doi: 10.1089/omi.2011.0118.

## References
- Homepage: https://bioconductor.org/packages/clusterProfiler
- Vignette: https://yulab-smu.github.io/clusterProfiler-book/

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## Run this on BioMate

This skill is the **knowledge layer** — when, why, and how to use `clusterprofiler`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=clusterprofiler)** — free to start.

▶ **[Open `clusterprofiler` on BioMate →](https://www.biomate.ai?ref=kb&pkg=clusterprofiler)**