Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1186/s12859-020-03910-x |
Licence  |
|
| Title (Primary) | multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data |
| Author | Canzler, S.
; Hackermüller, J.
|
| Source Titel | BMC Bioinformatics |
| Year | 2020 |
| Department | MOLSYB |
| Volume | 21 |
| Page From | art. 561 |
| Language | englisch |
| Data and Software links | https://doi.org/10.5281/zenodo.13359264 https://doi.org/10.5281/zenodo.13359285 https://doi.org/10.5281/zenodo.13359290 |
| 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. 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 differentorganisms 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://githu b.com/yigbt /multi GSEA and at bioconductor: https ://bioco nduct or.org/packages/multi GSEA. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23929 |
| 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 |
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