Journal
GENOME BIOLOGY
Volume 17, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-016-1077-y
Keywords
Single-cell RNA-seq; Differential expression; Cellular heterogeneity; Mixture modeling
Funding
- National Institutes of Health (NIH) [GM102756]
- NIH [U54AI117924, 4UH3TR000506-03]
- [5U01HL099773-06]
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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.
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