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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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Detalles Bibliográficos
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:
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
Descripción
Sumario: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.