Analysis of multi-condition single-cell data with latent embedding multivariate regression

Abstract

Identifying gene expression differences in heterogeneous tissues across experimental or observational conditions is a fundamental biological task, enabled by single-cell assays such as multi-condition sc-RNA-seq. Current data analysis approaches divide the constituent cells into clusters meant to represent cell types, and identify differentially expressed genes for each cluster. However, such discrete categorization tends to be an unsatisfactory model of the underlying biology. Use of more gradual representations of cell type or cell state promises higher statistical power, better usability and better interpretability. Here, we introduce Latent Embedding Multivariate Regression (LEMUR), a generative model that enables differential expression analysis using a continuous low-dimensional latent space parameterization of cell type and state diversity. It operates without, or before, commitment to discrete categorization. LEMUR (1) integrates data from the different conditions, (2) predicts how each cells gene expression would change as a function of the conditions and its position in latent space, and (3) for each gene, identifies compact neighborhoods of cells with consistent differential expression. Unlike statically defined clusters, these neighborhoods adapt to the underlying gene expression changes. We assess LEMURs performance on a compendium of single-cell datasets and show applications to the identification of tumor subpopulations with distinct drug responses, the interplay between cell state and developmental time in zebrafish embryos, and the discovery of cell state x environment interactions in a spatial single-cell study of plaques in Alzheimers disease. LEMUR is broadly applicable as a first-line analysis approach to multi-condition sc-RNA-seq data. Software availabilityhttps://bioconductor.org/packages/lemur Contactconstantin.ahlmann@embl.de