Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics

Abstract

Contemporary single-cell omics technologies have enabled complex experimental designs incorporating hundreds of samples accompanied by detailed information on sample-level conditions. Current approaches for analyzing condition-level heterogeneity in these experiments often rely on a simplification of the data such as an aggregation at the cell-type or cell-state-neighborhood level. Here we present MrVI, a deep generative model that provides sample-sample comparisons at a single-cell resolution, permitting the discovery of subtle sample-specific effects across cell populations. Additionally, the output of MrVI can be used to quantify the association between sample-level metadata and cell state variation. We benchmarked MrVI against conventional meta-analysis procedures on two synthetic datasets and one real dataset with a well-controlled experimental structure. This work introduces a novel approach to understanding sample-level heterogeneity while leveraging the full resolution of single-cell sequencing data.