A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Korthauer, Keegan; Chu, Li-Fang; Newton, Michael; Li, Yuan; Thomson, James; Stewart, Ron; Kendziorski, Christina
• Is Part Of: Genome biology. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 15
• Publication Date: 2016-12
• Subjects: Single-cell RNA-seq -- Differential expression -- Cellular heterogeneity -- Mixture modeling; Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-016-1077-y
• Languages: English
• ISSNs: 1474-760X

Abstract

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.