A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations

The ability to discover new cell populations by unsupervised clustering of single-cell transcriptomics data 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 SIGMA, a clusterability measure derived from random matrix theory, that can be used to identify cell clusters with non-random sub-structure, testably leading to the discovery of previously overlooked phenotypes.