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Activity Number: 418
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #319378 View Presentation
Title: ScDD: A Statistical Approach for Identifying Differential Distributions in Single-Cell RNA-Seq Experiments
Author(s): Keegan Korthauer* and Michael Newton and Christina Kendziorski
Companies: Dana-Farber Cancer Institute and University of Wisconsin - Madison and University of Wisconsin
Keywords: single-cell RNA-seq ; differential expression ; cellular heterogeneity ; mixture modeling

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.

Authors who are presenting talks have a * after their name.

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