1. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.
- Author
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Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, and Kendziorski C
- Subjects
- Algorithms, Computational Biology, Gene Expression Profiling, High-Throughput Nucleotide Sequencing methods, Humans, Sequence Analysis, RNA, Single-Cell Analysis methods, High-Throughput Nucleotide Sequencing statistics & numerical data, RNA genetics, Single-Cell Analysis statistics & numerical data, Software
- 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.
- Published
- 2016
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