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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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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 |
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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 |
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