Discovery of disease-associated cellular states using ResidPCA in single-cell RNA and ATAC sequencing data

Abstract

To enhance understanding of cellular heterogeneity and disease from single-cell sequencing data, we introduce ResidPCA, a robust method for cell state identification that models cell type heterogeneity. Simulations demonstrate ResidPCAs efficacy, particularly in complex scenarios, with its accuracy more than four times higher than conventional Principal Component Analysis (PCA) and over three times higher than Non-negative Matrix Factorization (NMF)-based methods in identifying states expressed across multiple cell types. In scRNA-seq data from light-stimulated mouse visual cortex cells, ResidPCA captures stimulus-driven variability with an accuracy more than five times higher than NMF methods. In single nucleus data from an Alzheimers disease cohort, ResidPCA identified 44 snATAC-based and 42 snRNA-based states. 30 snATAC states were significantly enriched for Alzheimers disease heritability and were often more significantly enriched than established cell types such as microglia. The ResidPCA-based snATAC state most significantly enriched for Alzheimers disease heritability further elucidates a recently identified mechanism involving the neuron-ODC-microglial axis. This state links early amyloid production in neurons and oligodendrocytes with later-stage microglial activation and immune response, driving Alzheimers disease progression. These results demonstrate ResidPCAs ability to reveal additional biological variation in single-cell data and uncover disease-relevant cell states.