| name | bioconductor-gseabase |
| description | This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). |
| when_to_use | Use when: 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 mapIdenti. Not for: 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 m |
| user-invocable | false |
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
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