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
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.
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.
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