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Single-cell data clustering based on sparse optimization and low-rank matrix factorization.

ABSTRACT: Unsupervised clustering is a fundamental step of single-cell RNA-sequencing (scRNA-seq) data analysis. This issue has inspired several clustering methods to classify cells in scRNA-seq data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for scRNA-seq data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single- scRNA-seq data.

SUBMITTER: Hu Y

PROVIDER: S-EPMC8495739 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Single-cell data clustering based on sparse optimization and low-rank matrix factorization.

Hu Yinlei Y Li Bin B Chen Falai F Qu Kun K

G3 (Bethesda, Md.) 20210601 6

Unsupervised clustering is a fundamental step of single-cell RNA-sequencing (scRNA-seq) data analysis. This issue has inspired several clustering methods to classify cells in scRNA-seq data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for scRNA-seq data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed tha ...[more]

PMID: 33787873

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