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10.1016/j.gpb.2016.01.005 http://scihub22266oqcxt.onion/10.1016/j.gpb.2016.01.005 C4792842!4792842!26876720     free     free     free       Genomics+Proteomics+Bioinformatics 2016 ; 14 (1): 21-30 Nephropedia Template TP {{tp|p=26876720|t=2016. Single-cell Transcriptome Study as Big Data |pdf=|usr=}}{{26876720}} gab.com Text Single-cell Transcriptome Study as Big Data JO: Genomics Proteomics Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26876720 #FOAvip The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies. Twit Text FOAVip Single-cell Transcriptome Study as Big Data ????Genomics Proteomics Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26876720 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26876720 #FOAvip English Wikipedia <ref>{{Cite pmid|26876720}}</ref> Single-cell Transcriptome Study as Big Data #MMPMID26876720 Yu P; Lin W Genomics Proteomics Bioinformatics 2016[Feb]; 14 (1): 21-30 PMID26876720show ga The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies. äSingle-cell Transcriptome Study as Big Data (epmc).pdf DeepDyve Pubget Overpricing