Korthauer, KeeganLi, YuanChu, Li-FangNewton, Michael A.Thomson, JamesStewart, RonKendziorski, Christina2016-12-022016Korthauer, Keegan D., Li-Fang Chu, Michael A. Newton, Yuan Li, James Thomson, Ron Stewart, and Christina Kendziorski. 2016. “A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.” Genome Biology 17 (1): 222. doi:10.1186/s13059-016-1077-y. http://dx.doi.org/10.1186/s13059-016-1077-y.1474-7596http://nrs.harvard.edu/urn-3:HUL.InstRepos:29626163The 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. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1077-y) contains supplementary material, which is available to authorized users.en-USSingle-cell RNA-seqDifferential expressionCellular heterogeneityMixture modelingJournal Article2016-12-0210.1186/s13059-016-1077-y