TY - JOUR T1 - Deep Generative Models for Detecting Differential Expression in Single Cells JF - bioRxiv DO - 10.1101/794289 SP - 794289 AU - Boyeau, Pierre AU - Lopez, Romain AU - Regier, Jeffrey AU - Gayoso, Adam AU - Jordan, Michael I. AU - Yosef, Nir Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/10/04/794289.abstract N2 - Detecting differentially expressed genes is important for characterizing subpopulations of cells. However, in scRNA-seq data, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. First, we show that deep generative models, which combined Bayesian statistics and deep neural networks, better estimate the log-fold-change in gene expression levels between subpopulations of cells. Second, we use Bayesian decision theory to detect differentially expressed genes while controlling the false discovery rate. Our experiments on simulated and real datasets show that our approach out-performs state-of-the-art DE frameworks. Finally, we introduce a technique for improving the posterior approximation, and show that it also improves differential expression performance. ER -