Abstract
BackgroundInference of biological pathway activity via gene set enrichment analysis is frequently used in the interpretation of clinical and other omics data. With the proliferation of new omics profiling approaches and ever-growing size of data sets generated, there is a lack of tools available to perform and visualise gene set enrichments in analyses involving multiple contrasts.ResultsTo address this, we developed mitch, an R package for multi-contrast gene set enrichment analysis. It uses a rank-MANOVA statistical approach to identify sets of genes that exhibit joint enrichment across multiple contrasts. Its unique visualisation features enable the exploration of enrichments in up to 20 contrasts. We demonstrate the utility of mitch with case studies spanning multi-contrast RNA expression profiling, integrative multi-omics, tool benchmarking and single-cell RNA sequencing. Using simulated data we show that mitch has similar accuracy to state of the art tools for single-contrast enrichment analysis, and superior accuracy in identifying multi-contrast enrichments.Conclusionmitch is a versatile tool for rapidly and accurately identifying and visualising gene set enrichments in multi-contrast omics data. Mitch is available from Bioconductor (https://bioconductor.org/packages/mitch).
Highlights
Inference of biological pathway activity via gene set enrichment analysis is frequently used in the interpretation of clinical and other omics data
We demonstrate the utility of mitch in a variety of use cases including enrichment analysis of multi-omics and single cell transcriptomics
We applied mitch to RNA-seq data initially described by Felisbino et al [49], consisting of two contrasts; (i) low glucose (LG) versus high glucose (HG); and (ii) HG versus HG with valproic acid (HGVPA)
Summary
Inference of biological pathway activity via gene set enrichment analysis is frequently used in the interpretation of clinical and other omics data. With the proliferation of new omics profiling approaches and evergrowing size of data sets generated, there is a lack of tools available to perform and visualise gene set enrichments in analyses involving multiple contrasts. Functional enrichment analysis describes the various ways that summarised omics data can be used to infer differential expression (DE) of molecular pathways, or more broadly sets of genes that are functionally linked [1]. Most commonly used pathway enrichment analysis methods fall into three categories; over-representation analysis (ORA), functional class sorting (FCS) and pathway topology (PT) methods [1, 4, 5]. Functional class scoring is different because it uses all detected genes in the calculation of pathway
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
