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10.1371/journal.pone.0193067 http://scihub22266oqcxt.onion/10.1371/journal.pone.0193067 C5832224!5832224 !29494600     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29494600 &cmd=llinks): Failed to open stream: HTTP request failed! 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Computational de novo discovery of distinguishing genes for biological processes and cell types in complex tissues |pdf=|usr=}}{{29494600 }} gab.com Text Computational de novo discovery of distinguishing genes for biological processes and cell types in complex tissues JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29494600 #FOAvip Bulk tissue samples examined by gene expression studies are usually heterogeneous. The data gained from these samples display the confounding patterns of mixtures consisting of multiple cell types or similar cell types in various functional states, which hinders the elucidation of the molecular mechanisms underlying complex biological phenomena. A realistic approach to compensate for the limitations of experimentally separating homogenous cell populations from mixed tissues is to computationally identify cell-type specific patterns from bulk, heterogeneous measurements. We designed the CellDistinguisher algorithm to analyze the gene expression data of mixed samples, identifying genes that best distinguish biological processes and cell types. Coupled with a deconvolution algorithm that takes cell type specific gene lists as input, we show that CellDistinguisher performs as well as partial deconvolution algorithms in predicting cell type composition without the need for prior knowledge of cell type signatures. This approach is also better in predicting cell type signatures than the one-step traditional complete deconvolution methods. To illustrate its wide applicability, the algorithm was tested on multiple publicly available data sets. In each case, CellDistinguisher identified genes reflecting biological processes typical for the tissues and development stages of interest and estimated the sample compositions accurately. Twit Text FOAVip Computational de novo discovery of distinguishing genes for biological processes and cell types in complex tissues ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29494600 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29494600 #FOAvip English Wikipedia <ref>{{Cite pmid|29494600 }}</ref> Computational de novo discovery of distinguishing genes for biological processes and cell types in complex tissues #MMPMID29494600 Newberg LA ; Chen X ; Kodira CD ; Zavodszky MI PLoS One 2018[]; 13 (3 ): e0193067 PMID29494600 show ga Bulk tissue samples examined by gene expression studies are usually heterogeneous. The data gained from these samples display the confounding patterns of mixtures consisting of multiple cell types or similar cell types in various functional states, which hinders the elucidation of the molecular mechanisms underlying complex biological phenomena. A realistic approach to compensate for the limitations of experimentally separating homogenous cell populations from mixed tissues is to computationally identify cell-type specific patterns from bulk, heterogeneous measurements. We designed the CellDistinguisher algorithm to analyze the gene expression data of mixed samples, identifying genes that best distinguish biological processes and cell types. Coupled with a deconvolution algorithm that takes cell type specific gene lists as input, we show that CellDistinguisher performs as well as partial deconvolution algorithms in predicting cell type composition without the need for prior knowledge of cell type signatures. This approach is also better in predicting cell type signatures than the one-step traditional complete deconvolution methods. To illustrate its wide applicability, the algorithm was tested on multiple publicly available data sets. In each case, CellDistinguisher identified genes reflecting biological processes typical for the tissues and development stages of interest and estimated the sample compositions accurately. |Algorithms [MESH] |Animals [MESH] |B-Lymphocytes/cytology/metabolism [MESH] |Brain/cytology/metabolism [MESH] |Gene Expression [MESH] |Gene Expression Profiling/*methods [MESH] |Genomics/*methods [MESH] |Humans [MESH] |Liver/cytology/metabolism [MESH] |Lung/cytology/metabolism [MESH] |Oligonucleotide Array Sequence Analysis/methods [MESH] |Rats [MESH] |Sequence Analysis, RNA/methods [MESH]Computational de novo discovery of distinguishing genes for biological(epmc).pdf DeepDyve Pubget Overpricing