RT Journal Article SR Electronic T1 An Empirical Bayes Method for Differential Expression Analysis of Single Cells with Deep Generative Models JF bioRxiv FD Cold Spring Harbor Laboratory SP 2022.05.27.493625 DO 10.1101/2022.05.27.493625 A1 Pierre Boyeau A1 Jeffrey Regier A1 Adam Gayoso A1 Michael I. Jordan A1 Romain Lopez A1 Nir Yosef YR 2022 UL http://biorxiv.org/content/early/2022/05/29/2022.05.27.493625.abstract AB Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. Deep generative models have been extensively applied to scRNA-seq data, with a special focus on embedding cells into a low-dimensional latent space and correcting for batch effects. However, little attention has been given to the problem of utilizing the uncertainty from the deep generative model for differential expression. Furthermore, the existing approaches do not allow controlling for the effect size or the false discovery rate. Here, we present lvm-DE, a generic Bayesian approach for performing differential expression from using a fitted deep generative model, while controlling the false discovery rate. We apply the lvm-DE framework to scVI and scSphere, two deep generative models. The resulting approaches outperform the state-of-the-art methods at estimating the log fold change in gene expression levels, as well as detecting differentially expressed genes between subpopulations of cells.Competing Interest StatementThe authors have declared no competing interest.