Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
Maria Mircea, Mazène Hochane, Xueying Fan, Susana M. Chuva de Sousa Lopes, Diego Garlaschelli, Stefan Semrau
Abstract
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.
Topics & Concepts
BiologyPhenotypeComputational biologyMeasure (data warehouse)Cluster analysisTranscriptomeEvolutionary biologyCluster (spacecraft)GeneticsGeneData miningArtificial intelligenceComputer scienceGene expressionProgramming languageSingle-cell and spatial transcriptomicsCell Image Analysis TechniquesAdvanced Biosensing Techniques and Applications