Large-scale snRNA-seq meta-analysis of microglia role in Alzheimer’s disease across statistical methods

Abstract

Microglia orchestrate complex neurodegeration processes that drives Alzheimer’s disease (AD), yet their transcriptional signatures remain inconsistently reported across single-nucleus RNA-seq studies. We analyze three pre-frontal-cortex cohorts (Prater, SEA-AD, ROSMAP; ranging from 22 to 345 donors) with five differential-expression (DE) pipelines and introduce Was2CoDE – a Wasserstein-2–based test that partitions donor-to-donor differences into mean, variance and shape components. Three principal findings emerge. First, study design matters: the rigorously curated SEA-AD cohort reproducibly recovers the highest fraction of literature-validated microglia pathways, and power scales chiefly with the number of donors, not nuclei or read depth. Second, among DE frameworks, the matrix-factorization approach eSVD-DE delivers the most consistent gene- and pathway-level signals across independent datasets. Third, we shed light on the opportunity to discover underlying microglia mechanisms by analyzing differential distributions, which is broader than differential mean expression. Specifically, Was2CoDE uncovers distributional shifts missed by mean-centric tests, revealing variance-driven dysregulation in immune and cell-motility programs and highlighting genes such as ARHGEF3, CD9, and SASH1 that escape standard DE thresholds. Together, these results provide quantitative guidance for cohort design, benchmark analytic robustness and supply an open-source tool for full-distribution inference. By integrating method, design and distributional insights, our framework advances the search for microglia therapeutic targets in AD.