A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

作者：Korthauer, Keegan D.; Chu, Li-Fang; Newton, Michael A.; Li, Yuan; Thomson, James; Stewart, Ron; Kendziorski, Christina^*

来源：Genome Biology, 2016, 17(1): 222.

DOI：10.1186/s13059-016-1077-y

• Single-cell RNA-seq
• Differential expression
• Cellular heterogeneity
• Mixture modeling

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.

• 出版日期2016-10-25