PT - JOURNAL ARTICLE AU - Keegan D. Korthauer AU - Li-Fang Chu AU - Michael A. Newton AU - Yuan Li AU - James Thomson AU - Ron Stewart AU - Christina Kendziorski TI - scDD: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments AID - 10.1101/035501 DP - 2016 Jan 01 TA - bioRxiv PG - 035501 4099 - http://biorxiv.org/content/early/2016/05/13/035501.short 4100 - http://biorxiv.org/content/early/2016/05/13/035501.full AB - The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. Although understanding such heterogeneity is of primary interest in a number of studies, for convenience, statistical methods often treat cellular heterogeneity as a nuisance factor. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. Using simulated and case study data, we demonstrate that the modeling framework is able to detect differential expression patterns of interest under a wide range of settings. Compared to existing approaches, scDD has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and is able to characterize those differences. The freely available R package scDD implements the approach.scRNA-seqsingle-cell RNA sequencingscDDsingle-cell differential distributions DE: differential expressionDPdifferential proportionDMdifferential modalityDBdifferential both (expression and modality)DZdifferential zeroeshESChuman embryonic stem cellDECdefinitive endoderm celNPCneuronal progenitor cellDPMDirichlet process mixturePPMproduct partition modelMAPmaximum a posterioriBICBayesian information criterion