Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis

Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis

Abstract In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous stu...

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Bibliographic Details
Main Authors: Juan Wang, Nana Zhang, Shasha Yuan, Junliang Shang, Lingyun Dai, Feng Li, Jinxing Liu
Format: Article
Language:English
Published: BMC 2022-12-01
Series:BMC Genomics
Subjects:
Dictionary learning
Low-rank representation
scRNA-seq data analysis
Subspace clustering
Cell type identification
Online Access:https://doi.org/10.1186/s12864-022-09027-0

Internet

https://doi.org/10.1186/s12864-022-09027-0

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