2020
DOI: 10.1101/2020.04.16.044958
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ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

Abstract: Pathway analyses are key methods to analyse ‘omics experiments. Nevertheless, integrating data from different ‘omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome’s existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically map… Show more

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Cited by 33 publications
(37 citation statements)
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“…Thereby, any result is only a partial representation of the underlying biological changes. In addition these figures ignore any underlying quantitative information [ 29 ].…”
Section: Discussionmentioning
confidence: 99%
“…Thereby, any result is only a partial representation of the underlying biological changes. In addition these figures ignore any underlying quantitative information [ 29 ].…”
Section: Discussionmentioning
confidence: 99%
“…RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.) Enrichment analysis was applied to the total gene counts using the reactome GSA package (52) in Rstudio. Enrichment analysis was applied to the total gene counts using the CAMERA algorithm (53).…”
Section: Rnaseq Data Analysismentioning
confidence: 99%
“…Well-defined marker genes for each cluster were used to identify potential cell populations, such as T cells (CD3E, CD4, CD8A), B cells (CD19, CD20, SDC1), macrophages (CD14), and NK cells (NCAM1, FCRIII, Granzyme B). For gene sets representing specific cellular functions or pathways, we performed functional enrichment analysis with the online tool Reactome GSA (https://www.biorxiv.org/content/early/2020/04/18/2020.04.16.044958) [48].…”
Section: Single-cell Rna Sequencing and Analysismentioning
confidence: 99%