Isolating structured salient variations in single-cell transcriptomic data with StrastiveVI

Single-cell RNA sequencing (scRNA-seq) has provided deeper insights into biological processes by highlighting differences at the cellular level. Within these single-cell omics measurements, researchers are often interested in identifying variations associated with a specific covariate. For instance, in aging research, it becomes vital to differentiate variations related to aging. To address this, we introduce StrastiveVI (Structured Contrastive Variational Inference; https://github.com/suinleelab/StrastiveVI), which effectively separates the variations of interest from other dominant biological signals in scRNA-seq datasets. When deployed on aging and Alzheimers disease (AD) datasets, StrastiveVI efficiently isolates aging and AD-associated patterns, distinguishing them from dominant variations linked to sex, tissue, and cell type that are unrelated to aging or AD. In doing so, it underscores both well-known genes and potential novel genes related to aging or AD.