Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations

The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (ϕ...

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Autores principales: Mircea, Maria, Hochane, Mazène, Fan, Xueying, Chuva de Sousa Lopes, Susana M., Garlaschelli, Diego, Semrau, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751334/
https://www.ncbi.nlm.nih.gov/pubmed/35012604
http://dx.doi.org/10.1186/s13059-021-02590-x
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author Mircea, Maria
Hochane, Mazène
Fan, Xueying
Chuva de Sousa Lopes, Susana M.
Garlaschelli, Diego
Semrau, Stefan
author_facet Mircea, Maria
Hochane, Mazène
Fan, Xueying
Chuva de Sousa Lopes, Susana M.
Garlaschelli, Diego
Semrau, Stefan
author_sort Mircea, Maria
collection PubMed
description The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (ϕ(clust)), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non-random substructure, testably leading to the discovery of previously overlooked phenotypes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02590-x.
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spelling pubmed-87513342022-01-12 Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations Mircea, Maria Hochane, Mazène Fan, Xueying Chuva de Sousa Lopes, Susana M. Garlaschelli, Diego Semrau, Stefan Genome Biol Method The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (ϕ(clust)), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non-random substructure, testably leading to the discovery of previously overlooked phenotypes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02590-x. BioMed Central 2022-01-10 /pmc/articles/PMC8751334/ /pubmed/35012604 http://dx.doi.org/10.1186/s13059-021-02590-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Method
Mircea, Maria
Hochane, Mazène
Fan, Xueying
Chuva de Sousa Lopes, Susana M.
Garlaschelli, Diego
Semrau, Stefan
Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
title Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
title_full Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
title_fullStr Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
title_full_unstemmed Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
title_short Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
title_sort phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751334/
https://www.ncbi.nlm.nih.gov/pubmed/35012604
http://dx.doi.org/10.1186/s13059-021-02590-x
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