Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1186/s12859-020-03910-x
Lizenz creative commons licence
Titel (primär) multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data
Autor Canzler, S. ORCID logo ; Hackermüller, J. ORCID logo
Quelle BMC Bioinformatics
Erscheinungsjahr 2020
Department MOLSYB
Band/Volume 21
Seite von art. 561
Sprache englisch
Keywords Pathway enrichment; GSEA; Multi-omics; Bioconductor; Software; R
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 layersbecame prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application.
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 differentorganisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs.
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://githu /multi GSEA and at bioconductor: https ://bioco nduct GSEA.
dauerhafte UFZ-Verlinkung
Canzler, S., Hackermüller, J. (2020):
multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data
BMC Bioinformatics 21 , art. 561 10.1186/s12859-020-03910-x