title: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.
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.
authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, 02215, MA, USA.|Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, 02115, MA, USA.
authors: Keegan D
authors: KD
authors: Korthauer
authors: Morgridge Institute for Research, University of Wisconsin, Madison, 53706, WI, USA.
authors: Li-Fang
authors: LF
authors: Chu
authors: Department of Biostatistics, University of Wisconsin, Madison, 53706, WI, USA.|Department of Statistics, University of Wisconsin, Madison, 53706, WI, USA.
authors: Michael A
authors: MA
authors: Newton
authors: Department of Statistics, University of Wisconsin, Madison, 53706, WI, USA.
authors: Yuan
authors: Y
authors: Li
authors: Morgridge Institute for Research, University of Wisconsin, Madison, 53706, WI, USA.|Department of Cell and Regenerative Biology, University of Wisconsin, Madison, 53706, WI, USA.|Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, 93106, CA, USA.
authors: James
authors: J
authors: Thomson
authors: Morgridge Institute for Research, University of Wisconsin, Madison, 53706, WI, USA.
authors: Ron
authors: R
authors: Stewart
authors: Department of Biostatistics, University of Wisconsin, Madison, 53706, WI, USA. kendzior@biostat.wisc.edu.|Department of Statistics, University of Wisconsin, Madison, 53706, WI, USA. kendzior@biostat.wisc.edu.
authors: Christina
authors: C
authors: Kendziorski
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D012313:RNA / Q000235:genetics*; D017423:Sequence Analysis, RNA; D059010:Single-Cell Analysis / Q000379:methods / Q000706:statistics & numerical data*; D012984:Software
chemical_list: D012313:RNA
keywords: Cellular heterogeneity; Differential expression; Mixture modeling; Single-cell RNA-seq