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10.1111/mec.13536 http://scihub22266oqcxt.onion/10.1111/mec.13536 C4769650!4769650 !26786791     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26786791 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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\26786791 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Mol+Ecol 2016 ; 25 (5 ): 1032-57 Nephropedia Template TP {{tp|p=26786791 |t=2016. Application of multivariate statistical techniques in microbial ecology |pdf=|usr=}}{{26786791 }} gab.com Text Application of multivariate statistical techniques in microbial ecology JO: Mol Ecol http://www.kidney.de/pget.php?&tw=1&pmid=26786791 #FOAvip Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. Twit Text FOAVip Application of multivariate statistical techniques in microbial ecology ????Mol Ecol http://www.kidney.de/pget.php?&tw=1&pmid=26786791 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26786791 #FOAvip English Wikipedia <ref>{{Cite pmid|26786791 }}</ref> Application of multivariate statistical techniques in microbial ecology #MMPMID26786791 Paliy O ; Shankar V Mol Ecol 2016[Mar]; 25 (5 ): 1032-57 PMID26786791 show ga Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. |*Environmental Microbiology [MESH] |*Multivariate Analysis [MESH] |Cluster Analysis [MESH] |Discriminant Analysis [MESH] |Ecology/*methods [MESH] |Principal Component Analysis [MESH]Application of multivariate statistical techniques in microbial ecolog(epmc).pdf DeepDyve Pubget Overpricing