scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis

KIZ OpenIR

	scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis
	Zhao, K; So, HC; Lin, ZX
	2024
发表期刊	GENOME BIOL
ISSN	1474-760X
卷号	25 期号:1
摘要	The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.
收录类别	SCI
语种	英语
文献类型	期刊论文
条目标识符	http://ir.kiz.ac.cn/handle/152453/14704
专题	昆明动物研究所
推荐引用方式 GB/T 7714	Zhao, K,So, HC,Lin, ZX. scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis[J]. GENOME BIOL,2024,25(1).
APA	Zhao, K,So, HC,&Lin, ZX.(2024).scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis.GENOME BIOL,25(1).
MLA	Zhao, K,et al."scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis".GENOME BIOL 25.1(2024).

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QT2025041012.pdf（5497KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	请求全文