Isolating salient variations of interest in single-cell data with contrastiveVI

Abstract

Single-cell datasets are routinely collected to investigate changes in cellular state between control cells and corresponding cells in a treatment condition, such as exposure to a drug or infection by a pathogen. To better understand heterogeneity in treatment response, it is desirable to disentangle latent structures and variations uniquely enriched in treated cells from those shared with controls. However, standard computational models of single-cell data are not designed to explicitly separate these variations. Here, we introduce Contrastive Variational Inference (contrastiveVI; https://github.com/suinleelab/contrastiveVI), a framework for analyzing treatment-control scRNA-seq datasets that explicitly disentangles the data into shared and treatment-specific latent variables. Using four treatment-control scRNA-seq dataset pairs, we apply contrastiveVI to perform a broad set of standard analysis tasks, including visualization, clustering, and differential expression testing. In each case, we find that our method consistently achieves results that agree with known biological ground truths, while previously proposed methods often fail to do so. We conclude by generalizing our framework to multimodal measurements and applying it to analyze a single-cell dataset with joint transcriptome and surface protein measurements.