Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Genome Biol. 2024 Aug 16;25(1):223. doi: 10.1186/s13059-024-03345-0.
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.
单细胞 RNA 测序（scRNA-seq）数据的可用性和规模迅速增加，需要可扩展的综合分析方法。尽管已经开发了许多用于数据集成的方法，但很少有方法专注于在综合分析中理解不同细胞群体中生物条件的异质影响。我们提出的可扩展方法 scParser 对生物条件的异质影响进行建模，揭示了基因表达对表型贡献的关键机制。值得注意的是，扩展后的 scParser 确定了细胞亚群中导致疾病发病机制的生物学过程。与最先进的方法相比，scParser 在细胞聚类方面具有优异的性能，并且具有广泛而多样的适用性。
