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10.1186/s13059-016-1077-y

http://scihub22266oqcxt.onion/10.1186/s13059-016-1077-y
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C5080738!5080738!27782827
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suck abstract from ncbi


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pmid27782827      Genome+Biol 2016 ; 17 (ä): ä
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  • A statistical approach for identifying differential distributions in single-cell RNA-seq experiments #MMPMID27782827
  • Korthauer KD; Chu LF; Newton MA; Li Y; Thomson J; Stewart R; Kendziorski C
  • Genome Biol 2016[]; 17 (ä): ä PMID27782827show ga
  • 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.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-016-1077-y) contains supplementary material, which is available to authorized users.
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