A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Issue 1 (December 2016) - Korthauer, Keegan; Chu, Li-Fang; Newton, Michael; Li, Yuan; Thomson, James; Stewart, Ron; Kendziorski, Christina
Abstract The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.