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10.1073/pnas.1617317113 http://scihub22266oqcxt.onion/10.1073/pnas.1617317113 C5187682!5187682!27930330     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2016 ; 113 (51): 14662-7 Nephropedia Template TP {{tp|p=27930330|t=2016. Simultaneous dimension reduction and adjustment for confounding variation |pdf=|usr=}}{{27930330}} gab.com Text Simultaneous dimension reduction and adjustment for confounding variation JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=27930330 #FOAvip With the advancement in high-throughput technologies, analyzing high-dimensional data has become a common task. Dimension reduction methods have been applied to visualize and identify dominant patterns in high-dimensional data. Confounding factors, commonly observed in high-throughput biological experiments, can affect the performance of these methods, and other downstream analysis. Here, we develop a method by coupling dimension reduction with the adjustment for confounder effects. Our method is able to capture the underlying patterns, as demonstrated by a human brain exon array dataset, a model organism ENCODE RNA sequencing dataset, and simulations. Twit Text FOAVip Simultaneous dimension reduction and adjustment for confounding variation ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=27930330 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27930330 #FOAvip English Wikipedia <ref>{{Cite pmid|27930330}}</ref> Simultaneous dimension reduction and adjustment for confounding variation #MMPMID27930330 Lin Z; Yang C; Zhu Y; Duchi J; Fu Y; Wang Y; Jiang B; Zamanighomi M; Xu X; Li M; Sestan N; Zhao H; Wong WH Proc Natl Acad Sci U S A 2016[Dec]; 113 (51): 14662-7 PMID27930330show ga With the advancement in high-throughput technologies, analyzing high-dimensional data has become a common task. Dimension reduction methods have been applied to visualize and identify dominant patterns in high-dimensional data. Confounding factors, commonly observed in high-throughput biological experiments, can affect the performance of these methods, and other downstream analysis. Here, we develop a method by coupling dimension reduction with the adjustment for confounder effects. Our method is able to capture the underlying patterns, as demonstrated by a human brain exon array dataset, a model organism ENCODE RNA sequencing dataset, and simulations. äSimultaneous dimension reduction and adjustment for confounding variat(epmc).pdf DeepDyve Pubget Overpricing