multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data
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multiGSEA: a GSEA‑based pathway enrichment analysis for multi‑omics data Sebastian Canzler* and Jörg Hackermüller *Correspondence: [email protected] Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Abstract Background: Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application. Results: Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. Conclusions: With multiGSEA we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. multiGSEA is publicly available under the GPL-3 license at https ://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packa ges/multiGSEA. Keywords: Pathway enrichment, GSEA, Multi-omics, Bioconductor, Software, R
Background When measuring molecular responses to a certain treatment or gaining insights into clinical phenotypes, gene set or pathway enrichment techniques are tools of first choice to infer mechanistic biological information from high-dimensional molecular omics data. Through different statistical techniques, such as over-representation analysis (ORA) or gene set enrichment analysis (GSEA), these methods are capable of identifying specific sets of genes or molecular response/signaling pathways that are triggered upon a certain treatment or disease. These sets might represent specific molecular functions, as defined by Gene Ontology (GO) [1], biological processes or experimentally derived gene sets which are publicly
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