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- Title
Single-cell data clustering based on sparse optimization and low-rank matrix factorization.
- Authors
Yinlei Hu; Bin Li; Falai Chen; Kun Qu
- 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.
- Subjects
MATRIX decomposition; LOW-rank matrices; DATABASES; FACTORIZATION; ALGORITHMS; RNA sequencing; DATA analysis
- Publication
G3: Genes | Genomes | Genetics, 2021, Vol 11, Issue 6, p1
- ISSN
2160-1836
- Publication type
Article
- DOI
10.1093/g3journal/jkab098