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

Maria Mircea1, Mazène Hochane2, Xueying Fan3

  • 1Leiden Institute of Physics, Leiden University, Leiden, The Netherlands.

Genome Biology
|January 11, 2022
PubMed
Summary

We developed phiclust, a new method to identify meaningful cell subpopulations within single-cell data. This tool helps discover previously overlooked cell phenotypes by analyzing cluster substructure.

Keywords:
ClusterabilityRandom matrix theoryscRNA-seq

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Area of Science:

  • Single-cell genomics
  • Computational biology
  • Systems biology

Background:

  • Unsupervised clustering of single-cell transcriptomes enables novel cell phenotype discovery.
  • A lack of principled methods hinders the identification of meaningful subpopulations within cell clusters.

Purpose of the Study:

  • To introduce phiclust (ϕclust), a novel clusterability measure.
  • To provide a principled approach for resolving meaningful subpopulations in single-cell data.

Main Methods:

  • Utilizing random matrix theory to derive the phiclust (ϕclust) measure.
  • Applying phiclust (ϕclust) to identify non-random substructure within cell clusters.

Main Results:

  • Phiclust (ϕclust) effectively identifies cell clusters with significant substructure.
  • The method facilitates the discovery of previously overlooked cell phenotypes.

Conclusions:

  • Phiclust (ϕclust) offers a robust tool for enhancing single-cell data analysis.
  • This approach advances the discovery of cellular heterogeneity and novel phenotypes.