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...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
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BMC
2022-01-01
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Series: | Genome Biology |
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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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format | Article |
id | doaj.art-f12df2ac4b9545a197354df046e711b1 |
institution | Directory Open Access Journal |
issn | 1474-760X |
language | English |
last_indexed | 2024-12-18T04:57:11Z |
publishDate | 2022-01-01 |
publisher | BMC |
record_format | Article |
series | Genome Biology |
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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