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

Mircea, M.; Hochane, M.; Fan, X.Y.; Lopes, S.M.C.D.; Garlaschelli, D.; Semrau, S. (2022)

Article / Letter to editor

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 (phi(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.

• Clusterability
• scRNA-seq
• Random matrix theory

• All authors: Mircea, M.; Hochane, M.; Fan, X.Y.; Lopes, S.M.C.D.; Garlaschelli, D.; Semrau, S.
• Date: 2022-01-10
• Journal: Genome Biology
• Volume: 23
• Issue: 1
• DOI: doi:10.1186/s13059-021-02590-x
• Link: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02590-x