multiGSEA: GSEA-based pathway enrichment for multi-omics data

multiGSEA is an open-source R/Bioconductor package for identifying biological pathways that are consistently affected across multiple molecular layers. It supports the integrated analysis of transcriptomics, proteomics, and metabolomics data using gene set enrichment analysis (GSEA).

The package performs enrichment analysis separately for each omics layer and subsequently combines the resulting pathway-level p-values into a composite multi-omics significance measure. This retains information from the individual layers while providing an integrated view of biological processes that may not be apparent from a single-omics analysis.

Main features

• Multi-omics pathway enrichment: Analyze transcriptomic, proteomic, and metabolomic data individually or in combination.
• GSEA-based analysis: Apply enrichment analysis to ranked molecular features without requiring an arbitrary significance threshold.
• Integrated significance estimates: Combine pathway-level p-values across omics layers using Stouffer’s method, Fisher’s method, or Edgington’s method.
• Curated pathway resources: Retrieve pathway definitions from up to eight databases—including KEGG, Reactome, and BioCarta—through the Bioconductor graphite package.
• Flexible identifier mapping: Convert common transcript, protein, and metabolite identifiers into formats suitable for pathway analysis. Supported identifiers include Entrez Gene, Ensembl, RefSeq, gene symbols, UniProt, HMDB, KEGG, ChEBI, PubChem, DrugBank, CAS numbers, CompTox identifiers, and metabolite names.
• Multiple organisms: Transcript and protein identifier mapping is available for 11 model organisms, including human, mouse, and rat.
• Custom gene sets: User-defined pathway or gene-set collections can be supplied instead of the built-in pathway resources.

A typical workflow consists of preparing ranked features for each omics layer, retrieving or supplying pathway definitions, calculating enrichment scores separately for every layer, and combining the resulting p-values into an integrated pathway ranking. The required input is deliberately simple: each molecular feature is represented by an identifier, a log2 fold change, and a p-value. Users may also provide their own ranking metric.

Installation

The released version is available from Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("multiGSEA")

The development version and source code are available on GitHub. A documented example covering the complete workflow is provided in the package vignette.

Citation

Canzler, S. and Hackermüller, J. (2020). multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data. BMC Bioinformatics, 21, 561. https://doi.org/10.1186/s12859-020-03910-x