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10.1186/s12918-017-0420-6 http://scihub22266oqcxt.onion/10.1186/s12918-017-0420-6 C5389000!5389000!28403906     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28403906&cmd=llinks): Failed to open stream: HTTP request failed! 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An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks |pdf=|usr=}}{{28403906}} gab.com Text An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks JO: BMC Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=28403906 #FOAvip Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn). Results: We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. Conclusions: The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful. Electronic supplementary material: The online version of this article (doi:10.1186/s12918-017-0420-6) contains supplementary material, which is available to authorized users. Twit Text FOAVip An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks ????BMC Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=28403906 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28403906 #FOAvip English Wikipedia <ref>{{Cite pmid|28403906}}</ref> An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks #MMPMID28403906 Botía JA; Vandrovcova J; Forabosco P; Guelfi S; D?Sa K; Hardy J; Lewis CM; Ryten M; Weale ME BMC Syst Biol 2017[]; 11 (ä): ä PMID28403906show ga Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn). Results: We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. Conclusions: The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful. Electronic supplementary material: The online version of this article (doi:10.1186/s12918-017-0420-6) contains supplementary material, which is available to authorized users. äAn additional k-means clustering step improves the biological features(epmc).pdf DeepDyve Pubget Overpricing