Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression

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. [Display omitted] •scBC detects gene network biomarkers in scRNA and snRNA-seq data•scBC incorporates existing biological information to guide single-cell biclustering•scBC outperforms other biclustering methods in a variety of settings•scBC reveals cell-specific gene module perturbations in Alzheimer disease Alzheimer disease (AD) is a highly complex and debilitating neurodegenerative disorder that has been the subject of extensive research and public attention in recent years. The pathogenesis of AD involves intricate changes to gene networks occurring across multiple cell types. Investigating individual genes or focusing on single cell types alone may therefore present limitations to fully comprehending the disease. We sought to develop a more comprehensive approach to analyze AD, one that can simultaneously capture the complexity of gene interactions and cellular heterogeneity. Gong et al. develop a single-cell Bayesian biclustering (scBC) framework that uncovers functional gene-module perturbations of different cell types in Alzheimer disease. Using scRNA and snRNA-seq data, scBC detects gene network biomarkers, overcoming challenges such as batch effects and high dropout rates. Outperforming other methods, scBC provides insights into complex disease mechanisms.