# Bioconductor Gseabase

> This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).

- Skill: `biomate-ai/bioconductor-gseabase` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-gseabase`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-gseabase/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-gseabase

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# GSEABase

## Dependencies & Environment

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

- **Version:** 1.74.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Depends:** BiocGenerics, Biobase, annotate, graph
- **Imports:** AnnotationDbi, XML
- **System requirements:** URL
- **Install:** `BiocManager::install("GSEABase")`

## When to Use
- **Gene Set Management**: Storing and manipulating gene sets and collections using robust S4 classes like `GeneSet()` and `GeneSetCollection()`.
- **Importing Standard Formats**: Reading gene sets encoded in XML following the schema and conventions of the Broad Institute using `getBroadSets()`.
- **Identifier Mapping**: Programmatically mapping gene identifiers within gene sets (e.g., Symbol to Entrez) using `mapIdentifiers()` and Bioconductor annotation packages.
- **Dynamic Collection Building**: Building custom gene set collections dynamically from GO or KEGG databases using `GOCollection()`.

## When NOT to Use
- For performing the actual statistical enrichment test (e.g., GSEA, ORA); use the fgsea or limma packages instead because GSEABase is purely for data structure management.
- For fast, tidyverse-compatible data frame manipulation of gene sets; use the msigdbr package instead because GSEABase relies heavily on strict S4 object classes.

## Data Requirements
- **Input formats**: Broad `.xml` files, `ExpressionSet` objects, or standard R vectors of gene identifiers.
- **Identifiers**: Gene IDs (Entrez, Symbol, Annotation) must be consistent across a single `GeneSet`.
- **Annotations**: Requires corresponding organism-specific annotation packages (e.g., `hgu95av2.db`, `org.Hs.eg.db`) if identifier mapping or GO extraction is needed.

## Key Parameters
- **setName**: A character string specifying the name of the gene set.
- **geneIdType** (`NullIdentifier()`): Specifies the gene identifier type (e.g., `EntrezIdentifier()`, `SymbolIdentifier()`, `AnnotationIdentifier()`) to ensure safe mapping.
- **collectionType** (`NullCollection()`): Defines the source/metadata of the gene set (e.g., `GOCollection()`, `BroadCollection()`).
- **urls**: The file path, URL, or connection to the `.xml` file when importing Broad sets.
- **evidenceCode**: Used within `GOCollection()` to filter pathways satisfying specific GO evidence constraints (e.g., `"IMP"`, `"IDA"`).
- **phenotype**: A character string describing the phenotype under consideration when creating a `GeneColorSet()`.

## Best Practices
- Always explicitly define the `geneIdType` (e.g., `EntrezIdentifier()`) when creating a `GeneSet()` to prevent downstream mapping errors.
- Leverage `mapIdentifiers()` in conjunction with an annotation package to harmonize gene IDs in the `GeneSetCollection()` before intersecting with your expression data.
- Use `details()` to inspect the metadata, creation date, and source of a `GeneSet` for careful curation.
- Validate gene sets against your expression matrix by directly subsetting the `ExpressionSet` using the `GeneSet` object.

## Common Pitfalls
- **Empty intersections during subsetting**: Caused by the gene set using Symbols while the expression data uses Annotation IDs. *Fix*: Use `mapIdentifiers()` to convert the `GeneSet` to match the data's ID type before subsetting.
- **Memory bloat with GO collections**: Caused by creating sets for every single GO term indiscriminately. *Fix*: Filter GO terms by providing arguments such as `evidenceCode` to `GOCollection()` to restrict the pathways.
- **Loss of directionality in sets**: Standard gene sets do not capture whether a gene is upregulated or downregulated. *Fix*: Use `GeneColorSet()` to indicate how features of genes and phenotypes are associated.

## Alternatives
- **msigdbr**: Provides MSigDB gene sets directly as tidy data frames rather than S4 objects, ideal for tidyverse workflows.
- **fgsea**: Uses simple named lists of character vectors for gene sets, bypassing the need for complex S4 classes entirely.

## Citations
- Morgan M, Falcon S, Gentleman R (2023). "GSEABase: Gene set enrichment data structures and methods." Bioconductor.

## References
- Homepage: https://bioconductor.org/packages/GSEABase
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GSEABase

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## Run this on BioMate

This skill is the **knowledge layer** — when, why, and how to use `gseabase`. 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=gseabase)** — free to start.

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