A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract 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.

Authors 6

1. Maria Mircea Aachen

   Leiden University

   Affiliation as printed

   Leiden Institute of Physics, Leiden University

   Leiden Institute of Physics, Leiden University, Leiden, The Netherlands

2. Mazène Hochane Aachen Leiden Academic Centre for Drug Research

   Leiden University

   Affiliation as printed

   Leiden Academic Center for Drug Research, Leiden University

   Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands

3. Xueying Fan Aachen Department of Anatomy and Embryology

   Leiden University Medical Center

   Affiliation as printed

   Department of Anatomy and Embryology, Leiden University Medical Center

   Department of Anatomy and Embryology, Leiden University Medical Center, Leiden, The Netherlands

4. Susana M. Chuva de Sousa Lopes Aachen Department of Anatomy and Embryology

   Leiden University Medical Center

   Affiliation as printed

   Department of Anatomy and Embryology, Leiden University Medical Center

   Department of Anatomy and Embryology, Leiden University Medical Center, Leiden, The Netherlands

5. Diego Garlaschelli Aachen

   Leiden University · IMT School for Advanced Studies Lucca

   Affiliation as printed

   Leiden Institute of Physics, Leiden University

   Networks Unit, IMT School for Advanced Studies

   Leiden Institute of Physics, Leiden University, Leiden, The Netherlands

   Networks Unit, IMT School for Advanced Studies, Lucca, Italy

6. Stefan Semrau corresponding Aachen

   Leiden University

   Affiliation as printed

   Leiden Institute of Physics, Leiden University

   Leiden Institute of Physics, Leiden University, Leiden, The Netherlands