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

Autor: Zhao K; Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China., So HC; School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China. hcso@cuhk.edu.hk.; KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Hong Kong SAR, China. hcso@cuhk.edu.hk.; Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China. hcso@cuhk.edu.hk.; Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China. hcso@cuhk.edu.hk.; Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China. hcso@cuhk.edu.hk.; Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong SAR, China. hcso@cuhk.edu.hk., Lin Z; Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China. zhixianglin@cuhk.edu.hk.
Jazyk: angličtina
Zdroj: Genome biology [Genome Biol] 2024 Aug 16; Vol. 25 (1), pp. 223. Date of Electronic Publication: 2024 Aug 16.
DOI: 10.1186/s13059-024-03345-0
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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