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

Abstract 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 ph...

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Main Authors: Maria Mircea, Mazène Hochane, Xueying Fan, Susana M. Chuva de Sousa Lopes, Diego Garlaschelli, Stefan Semrau
Format: Article
Language:English
Published: BMC 2022-01-01
Series:Genome Biology
Subjects:
Online Access:https://doi.org/10.1186/s13059-021-02590-x
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author Maria Mircea
Mazène Hochane
Xueying Fan
Susana M. Chuva de Sousa Lopes
Diego Garlaschelli
Stefan Semrau
author_facet Maria Mircea
Mazène Hochane
Xueying Fan
Susana M. Chuva de Sousa Lopes
Diego Garlaschelli
Stefan Semrau
author_sort Maria Mircea
collection DOAJ
description Abstract 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.
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spelling doaj.art-f12df2ac4b9545a197354df046e711b12022-12-21T21:20:14ZengBMCGenome Biology1474-760X2022-01-0123112410.1186/s13059-021-02590-xPhiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulationsMaria Mircea0Mazène Hochane1Xueying Fan2Susana M. Chuva de Sousa Lopes3Diego Garlaschelli4Stefan Semrau5Leiden Institute of Physics, Leiden UniversityLeiden Academic Center for Drug Research, Leiden UniversityDepartment of Anatomy and Embryology, Leiden University Medical CenterDepartment of Anatomy and Embryology, Leiden University Medical CenterLeiden Institute of Physics, Leiden UniversityLeiden Institute of Physics, Leiden UniversityAbstract 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.https://doi.org/10.1186/s13059-021-02590-xClusterabilityscRNA-seqRandom matrix theory
spellingShingle Maria Mircea
Mazène Hochane
Xueying Fan
Susana M. Chuva de Sousa Lopes
Diego Garlaschelli
Stefan Semrau
Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
Genome Biology
Clusterability
scRNA-seq
Random matrix theory
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 Clusterability
scRNA-seq
Random matrix theory
url https://doi.org/10.1186/s13059-021-02590-x
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