Dynamic Tree Cut

[…] inary distance and Ward linkage metrics. Clustering of chromatin states regions was performed using hierarchical clustering and the number of clusters was determined using the cutreeDynamic function (dynamicTreeCut R package) with a minimum size module of 500 features. […]

[…] Overlap Measure (TOM) corresponding to the overlap between pairs of interconnected genes [].TOM was used to build a gene clustering dendrogram. Highly interconnected genes were clustered by using the dynamicTreeCut algorithm [] with all the parameters at default values. Each cluster also called “module” was arbitrarily labelled with a unique colour (e.g brown4).Next, for each module a module eigen […]

[…] the integrated similarity score of these newly introduced weighted rule-based similarity measures among the pairwise rules, and then applied the average linkage clustering using the integrated score. Dynamic tree cut method [,] was then utilized on the resultant dendrogram of the clustering for recognizing co-expressed rule-modules. In addition, we identified the list of evolved condensed markers […]

[…] od or brain region, with an additional criteria of a minimum fold change of 2 for genes included in GO analysis.DEX genes over developmental period or brain region were clustered using the R package ‘dynamicTreeCut’. Euclidean distances between genes were clustered using the ‘hclust’ function (method=‘average’) prior to cluster discretisation using a dynamic tree cut (method=‘hybrid’, deepSplit=T, […]

[…] biological variance between our samples as already indicated by the clustering and MDS analyses (Fig.  and Supplementary Fig. ). However, subsequent quantification of module similarity revealed that DynamicTreeCut might have identified modules which are very similar (Supplementary Fig. ). Therefore modules were merged based on module eigengene correlations of 0.9 (MEDissThres = 0.1). Although mod […]

[…] k. The reciprocal topological dissimilarity matrix was used as input for hierarchical clustering, and gene modules were defined based on hierarchical clustering guided by topological overlap, using a dynamic tree cut algorithm to establish modules []. Finally, highly correlated modules were merged based on a merging threshold set at a height cut-off of 0.25. In the resulting network, as neighbors […]