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10.1186/s12859-016-0984-y http://scihub22266oqcxt.onion/10.1186/s12859-016-0984-y C4802652!4802652 !27005807     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 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\27005807 .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): 140 Nephropedia Template TP {{tp|p=27005807 |t=2016. pcaReduce: hierarchical clustering of single cell transcriptional profiles |pdf=|usr=}}{{27005807 }} gab.com Text pcaReduce: hierarchical clustering of single cell transcriptional profiles JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27005807 #FOAvip BACKGROUND: Advances in single cell genomics provide a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression analysis is the identification of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied, but there is considerable scope for the development of novel statistical approaches that are better adapted to the challenges of inferring cellular hierarchies. RESULTS: We have developed a novel agglomerative clustering method that we call pcaReduce to generate a cell state hierarchy where each cluster branch is associated with a principal component of variation that can be used to differentiate two cell states. Using two real single cell datasets, we compared our approach to other commonly used statistical techniques, such as K-means and hierarchical clustering. We found that pcaReduce was able to give more consistent clustering structures when compared to broad and detailed cell type labels. CONCLUSIONS: Our novel integration of principal components analysis and hierarchical clustering establishes a connection between the representation of the expression data and the number of cell types that can be discovered. In doing so we found that pcaReduce performs better than either technique in isolation in terms of characterising putative cell states. Our methodology is complimentary to other single cell clustering techniques and adds to a growing palette of single cell bioinformatics tools for profiling heterogeneous cell populations. Twit Text FOAVip pcaReduce: hierarchical clustering of single cell transcriptional profiles ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27005807 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27005807 #FOAvip English Wikipedia <ref>{{Cite pmid|27005807 }}</ref> pcaReduce: hierarchical clustering of single cell transcriptional profiles #MMPMID27005807 ?urauskien? J ; Yau C BMC Bioinformatics 2016[Mar]; 17 (ä): 140 PMID27005807 show ga BACKGROUND: Advances in single cell genomics provide a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression analysis is the identification of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied, but there is considerable scope for the development of novel statistical approaches that are better adapted to the challenges of inferring cellular hierarchies. RESULTS: We have developed a novel agglomerative clustering method that we call pcaReduce to generate a cell state hierarchy where each cluster branch is associated with a principal component of variation that can be used to differentiate two cell states. Using two real single cell datasets, we compared our approach to other commonly used statistical techniques, such as K-means and hierarchical clustering. We found that pcaReduce was able to give more consistent clustering structures when compared to broad and detailed cell type labels. CONCLUSIONS: Our novel integration of principal components analysis and hierarchical clustering establishes a connection between the representation of the expression data and the number of cell types that can be discovered. In doing so we found that pcaReduce performs better than either technique in isolation in terms of characterising putative cell states. Our methodology is complimentary to other single cell clustering techniques and adds to a growing palette of single cell bioinformatics tools for profiling heterogeneous cell populations. |*Algorithms [MESH] |*Transcriptome [MESH] |Animals [MESH] |Cell Line [MESH] |Cluster Analysis [MESH] |Humans [MESH] |Principal Component Analysis [MESH] |RNA/*metabolism [MESH] |Sequence Analysis, RNA [MESH]pcaReduce: hierarchical clustering of single cell transcriptional prof(epmc).pdf DeepDyve Pubget Overpricing