bioconductor-gseabase

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

• Homepage: https://bioconductor.org/packages/GSEABase
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GSEABase

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