RT Journal Article
SR Electronic
T1 Deep Generative Models for Detecting Differential Expression in Single Cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 794289
DO 10.1101/794289
A1 Pierre Boyeau
A1 Romain Lopez
A1 Jeffrey Regier
A1 Adam Gayoso
A1 Michael I. Jordan
A1 Nir Yosef
YR 2019
UL http://biorxiv.org/content/early/2019/10/04/794289.abstract
AB 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.