RT Journal Article
SR Electronic
T1 DECENT: Differential Expression with Capture Efficiency adjustmeNT for single-cell RNA-seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 225177
DO 10.1101/225177
A1 Chengzhong Ye
A1 Terence P Speed
A1 Agus Salim
YR 2018
UL http://biorxiv.org/content/early/2018/08/17/225177.abstract
AB Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the dropout process. We develop DECENT, a DE method for scRNA-seq data that explicitly models the dropout process and performs statistical analyses on the inferred pre-dropout counts. We demonstrate using simulated and real datasets the superior performance of DECENT compared to existing methods. DECENT does not require spike-in data, but spike-ins can be used to improve performance when available. The method is implemented in a publicly-available R package.