Gong Yuqiao, Xu Jingsi, Wu Maoying, Gao Ruitian, Sun Jianle, Yu Zhangsheng, Zhang Yue
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China; Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Center for Biomedical Data Science, Translational Science Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Cell Rep Methods. 2024 Apr 22;4(4):100742. doi: 10.1016/j.crmeth.2024.100742. Epub 2024 Mar 29.
The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.
阿尔茨海默病（AD）的发病机制涉及不同细胞类型间复杂的基因调控变化。为了帮助解读这种复杂性，我们引入了单细胞贝叶斯双聚类（scBC），这是一种在scRNA和snRNA-seq数据中识别细胞特异性基因网络生物标志物的框架。通过双聚类，scBC能够在单细胞水平上分析功能基因模块中的扰动。将scBC框架应用于AD的snRNA-seq数据，揭示了不同细胞组中基因模块内的扰动，并为AD进展过程中的基因-细胞相关性提供了线索。值得注意的是，我们的方法有助于克服单细胞数据分析中的常见挑战，包括批次效应和缺失事件。纳入先验知识进一步使该框架能够产生更具生物学解释性的结果。对模拟数据集和真实世界数据集的比较分析表明，与其他先进的双聚类方法相比，我们的方法具有更高的精度和稳健性。scBC在揭示以复杂基因共表达模式为特征的多基因疾病潜在机制方面具有潜力。
