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10.1186/s13059-016-1077-y http://scihub22266oqcxt.onion/10.1186/s13059-016-1077-y C5080738!5080738!27782827     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27782827.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Genome+Biol 2016 ; 17 (ä): ä Nephropedia Template TP {{tp|p=27782827|t=2016. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments |pdf=|usr=}}{{27782827}} gab.com Text A statistical approach for identifying differential distributions in single-cell RNA-seq experiments JO: Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=27782827 #FOAvip 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. Twit Text FOAVip A statistical approach for identifying differential distributions in single-cell RNA-seq experiments ????Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=27782827 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27782827 #FOAvip English Wikipedia <ref>{{Cite pmid|27782827}}</ref> 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. äA statistical approach for identifying differential distributions in s(epmc).pdf DeepDyve Pubget Overpricing