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10.3389/fgene.2021.810816 http://scihub22266oqcxt.onion/10.3389/fgene.2021.810816 35186014!8855158!35186014     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 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\35186014.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Front+Genet 2021 ; 12 (ä): 810816 Nephropedia Template TP {{tp|p=35186014|t=2021. Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models |pdf=|usr=}}{{35186014}} gab.com Text Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models JO: Front Genet http://www.kidney.de/pget.php?&tw=1&pmid=35186014 #FOAvip Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing provides new opportunities to explore these changing gene-gene interactions. Here, we present a sparse hierarchical Bayesian factor model to identify differences across network structures from different biological conditions in scRNA-seq data. Our methodology utilizes latent factors to impact gene expression values for each cell to help account for zero-inflation, increased cell-to-cell variability, and overdispersion that are unique characteristics of scRNA-seq data. Condition-dependent parameters determine which latent factors are activated in a gene, which allows for not only the calculation of gene-gene co-expression within each group but also the calculation of the co-expression differences between groups. We highlight our methodology's performance in detecting differential gene-gene associations across groups by analyzing simulated datasets and a SARS-CoV-2 case study dataset. Twit Text FOAVip Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models ????Front Genet http://www.kidney.de/pget.php?&tw=1&pmid=35186014 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35186014 #FOAvip English Wikipedia <ref>{{Cite pmid|35186014}}</ref> Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models #MMPMID35186014 Sekula M; Gaskins J; Datta S Front Genet 2021[]; 12 (ä): 810816 PMID35186014show ga Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing provides new opportunities to explore these changing gene-gene interactions. Here, we present a sparse hierarchical Bayesian factor model to identify differences across network structures from different biological conditions in scRNA-seq data. Our methodology utilizes latent factors to impact gene expression values for each cell to help account for zero-inflation, increased cell-to-cell variability, and overdispersion that are unique characteristics of scRNA-seq data. Condition-dependent parameters determine which latent factors are activated in a gene, which allows for not only the calculation of gene-gene co-expression within each group but also the calculation of the co-expression differences between groups. We highlight our methodology's performance in detecting differential gene-gene associations across groups by analyzing simulated datasets and a SARS-CoV-2 case study dataset. äSingle-Cell Differential Network Analysis with Sparse Bayesian Factor (epmc).pdf DeepDyve Pubget Overpricing