Cargando…

mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data

BACKGROUND: 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 ever-growing size of data sets generated, there is a lack of tools available t...

Descripción completa

Detalles Bibliográficos
Autores principales: Kaspi, Antony, Ziemann, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325150/
https://www.ncbi.nlm.nih.gov/pubmed/32600408
http://dx.doi.org/10.1186/s12864-020-06856-9
_version_ 1783552097737768960
author Kaspi, Antony
Ziemann, Mark
author_facet Kaspi, Antony
Ziemann, Mark
author_sort Kaspi, Antony
collection PubMed
description BACKGROUND: 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 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. RESULTS: To 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. CONCLUSION: mitch 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).
format Online
Article
Text
id pubmed-7325150
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-73251502020-06-30 mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data Kaspi, Antony Ziemann, Mark BMC Genomics Software BACKGROUND: 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 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. RESULTS: To 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. CONCLUSION: mitch 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). BioMed Central 2020-06-29 /pmc/articles/PMC7325150/ /pubmed/32600408 http://dx.doi.org/10.1186/s12864-020-06856-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Kaspi, Antony
Ziemann, Mark
mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data
title mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data
title_full mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data
title_fullStr mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data
title_full_unstemmed mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data
title_short mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data
title_sort mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325150/
https://www.ncbi.nlm.nih.gov/pubmed/32600408
http://dx.doi.org/10.1186/s12864-020-06856-9
work_keys_str_mv AT kaspiantony mitchmulticontrastpathwayenrichmentformultiomicsandsinglecellprofilingdata
AT ziemannmark mitchmulticontrastpathwayenrichmentformultiomicsandsinglecellprofilingdata