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bioRxiv · 10.1101/2021.05.11.443685

A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations

Mircea, M.·Hochane, M.·Fan, X.·Chuva de Sousa Lopes, S. M.·Garlaschelli, D.·Semrau, S.
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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.

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Mircea, M., Hochane, M., Fan, X., Chuva de Sousa Lopes, S. M., Garlaschelli, D., Semrau, S.. 2021-05-13. A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations. https://doi.org/10.1101/2021.05.11.443685

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