Cargando…

vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis

Gene-set analysis (GSA) dominates the functional interpretation of omics data and downstream hypothesis generation. Despite its ability to summarise thousands of measurements into semantically interpretable components, GSA often results in hundreds of significantly enriched gene-sets. However, summa...

Descripción completa

Detalles Bibliográficos
Autores principales: Mohamed, Ahmed, Bhuva, Dharmesh D, Lee, Sam, Liu, Ning, Tan, Chin Wee, Davis, Melissa J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320187/
https://www.ncbi.nlm.nih.gov/pubmed/37158226
http://dx.doi.org/10.1093/nar/gkad337
_version_ 1785068398884945920
author Mohamed, Ahmed
Bhuva, Dharmesh D
Lee, Sam
Liu, Ning
Tan, Chin Wee
Davis, Melissa J
author_facet Mohamed, Ahmed
Bhuva, Dharmesh D
Lee, Sam
Liu, Ning
Tan, Chin Wee
Davis, Melissa J
author_sort Mohamed, Ahmed
collection PubMed
description Gene-set analysis (GSA) dominates the functional interpretation of omics data and downstream hypothesis generation. Despite its ability to summarise thousands of measurements into semantically interpretable components, GSA often results in hundreds of significantly enriched gene-sets. However, summarisation and effective visualisation of GSA results to facilitate hypothesis generation is still lacking. While some webservers provide gene-set visualization tools, there is still a need for tools that can effectively summarize and guide exploration of GSA results. To enable versatility, webservers accept gene lists as input, however, none provide end-to-end solutions for emerging data types such as single-cell and spatial omics. Here, we present vissE.Cloud, a webserver for end-to-end gene-set analysis, offering gene-set summarisation and highly interactive visualisation. vissE.Cloud uses algorithms from our earlier R package vissE to summarise GSA results by identifying biological themes. We maintain versatility by allowing analysis of gene lists, as well as, analysis of raw single-cell and spatial omics data, including CosMx and Xenium data, making vissE.Cloud the first webserver to provide end-to-end gene-set analysis of sub-cellular localised spatial data. Structuring the results hierarchically allows swift interactive investigations of results at the gene, gene-set, and clusters level. vissE.Cloud is freely available at https://www.vissE.Cloud.
format Online
Article
Text
id pubmed-10320187
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-103201872023-07-06 vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis Mohamed, Ahmed Bhuva, Dharmesh D Lee, Sam Liu, Ning Tan, Chin Wee Davis, Melissa J Nucleic Acids Res Web Server issue Gene-set analysis (GSA) dominates the functional interpretation of omics data and downstream hypothesis generation. Despite its ability to summarise thousands of measurements into semantically interpretable components, GSA often results in hundreds of significantly enriched gene-sets. However, summarisation and effective visualisation of GSA results to facilitate hypothesis generation is still lacking. While some webservers provide gene-set visualization tools, there is still a need for tools that can effectively summarize and guide exploration of GSA results. To enable versatility, webservers accept gene lists as input, however, none provide end-to-end solutions for emerging data types such as single-cell and spatial omics. Here, we present vissE.Cloud, a webserver for end-to-end gene-set analysis, offering gene-set summarisation and highly interactive visualisation. vissE.Cloud uses algorithms from our earlier R package vissE to summarise GSA results by identifying biological themes. We maintain versatility by allowing analysis of gene lists, as well as, analysis of raw single-cell and spatial omics data, including CosMx and Xenium data, making vissE.Cloud the first webserver to provide end-to-end gene-set analysis of sub-cellular localised spatial data. Structuring the results hierarchically allows swift interactive investigations of results at the gene, gene-set, and clusters level. vissE.Cloud is freely available at https://www.vissE.Cloud. Oxford University Press 2023-05-09 /pmc/articles/PMC10320187/ /pubmed/37158226 http://dx.doi.org/10.1093/nar/gkad337 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server issue
Mohamed, Ahmed
Bhuva, Dharmesh D
Lee, Sam
Liu, Ning
Tan, Chin Wee
Davis, Melissa J
vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis
title vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis
title_full vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis
title_fullStr vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis
title_full_unstemmed vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis
title_short vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis
title_sort visse.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis
topic Web Server issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320187/
https://www.ncbi.nlm.nih.gov/pubmed/37158226
http://dx.doi.org/10.1093/nar/gkad337
work_keys_str_mv AT mohamedahmed vissecloudawebservertovisualisehigherordermolecularphenotypesfromenrichmentanalysis
AT bhuvadharmeshd vissecloudawebservertovisualisehigherordermolecularphenotypesfromenrichmentanalysis
AT leesam vissecloudawebservertovisualisehigherordermolecularphenotypesfromenrichmentanalysis
AT liuning vissecloudawebservertovisualisehigherordermolecularphenotypesfromenrichmentanalysis
AT tanchinwee vissecloudawebservertovisualisehigherordermolecularphenotypesfromenrichmentanalysis
AT davismelissaj vissecloudawebservertovisualisehigherordermolecularphenotypesfromenrichmentanalysis