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
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Use bioconductor-clusterprofiler to plan and run an ORA or GSEA analysis of [gene list or ranked genes] for [species and annotation source] in an R environment, returning enrichment plots and interpretation.
Required inputs
[gene list or ranked genes]
[species and annotation source]
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SKILL.md
name: bioconductor-clusterprofilerdescription: 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 sourceswhen_to_use: "Use when: 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.. Not for: 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)."user-invocable: false
clusterProfiler
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
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.
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 — free to start.