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10.1186/s12859-016-0944-6 http://scihub22266oqcxt.onion/10.1186/s12859-016-0944-6 C4772470!4772470!26927822     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 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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 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\26927822.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Bioinformatics 2016 ; 17 (ä): ä Nephropedia Template TP {{tp|p=26927822|t=2016. Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data |pdf=|usr=}}{{26927822}} gab.com Text Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26927822 #FOAvip Background: The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions. Results: We present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D3E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression. Conclusion: Evaluation using synthetic data shows that D3E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D3E makes it possible to gain additional biological insights. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-016-0944-6) contains supplementary material, which is available to authorized users. Twit Text FOAVip Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26927822 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26927822 #FOAvip English Wikipedia <ref>{{Cite pmid|26927822}}</ref> Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data #MMPMID26927822 Delmans M; Hemberg M BMC Bioinformatics 2016[]; 17 (ä): ä PMID26927822show ga Background: The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions. Results: We present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D3E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression. Conclusion: Evaluation using synthetic data shows that D3E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D3E makes it possible to gain additional biological insights. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-016-0944-6) contains supplementary material, which is available to authorized users. äDiscrete distributional differential expression (D3E) - a tool for gen(epmc).pdf DeepDyve Pubget Overpricing