Dynamic Tree Cut

[…] co-expression network was visualized using cytoscape v3.4 (), and the network topology parameters were calculated using the networkanalyzer plugin (). co-expression modules were identified using dynamic tree cut software ()., gene ontology (go) enrichment analyses were performed using bingo software (). hypergeometric tests with an fdr of 5% as a cutoff were used to select significantly […]

[…] genes. to identify modules of highly co-expressed genes, we used average linkage hierarchical clustering to group genes based on the topological overlap of their connectivity, followed by a dynamic tree-cut algorithm to cluster dendrogram branches into gene modules []. each of the resulting modules was assigned a color. for each gene, we calculated a module membership (mm) whose values […]

[…] from these processed expression data, we followed the protocols of wgcna () to create a gene co-expression network. modules were defined as branches of a hierarchical cluster tree using the top-down dynamic tree cut method (). for each module, the expression patterns were summarized by the module eigengene (me), defined as the singular vector of the standardized expression patterns. pairs […]

[…] 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 […]

[…] 3500 to save running time, and the threshold for network output was set as 0.5 to achieve more stringent connectivity of nodes in the network. co-expressed genes were clustered by applying tom and dynamictreecut functions to form different co-expression modules. a unique color was assembled to name each module. correlations of ips with modules were calculated using the cor() function in r […]

[…] 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., chromhmm was used to perform hidden markov modeling on the five histone marks and, by default, chromatin states were analyzed […]

[…] beta value satisfying the scale free topology criteria (optimal beta equal to 5 in rpmf group and 8 in rlf group). we set up values 2 and 10 as the parameters of deepsplit and minmodulesize for the dynamic tree cut function. the module eigenotu, which was defined as the first principle component of a module, was used to calculate the pearson correlation between a module and a metabolic trait. […]

[…] of a topological overlap measure (tom) dissimilarity matrix, which serves as input for average linkage hierarchical clustering. branches from the resulting tree were divided into modules using the dynamictreecut algorithm with the option deepsplit = 0. the choice of the value that specifies the sensitivity of splitting clusters was guided by visual inspection of the tom plot, a color-coded […]

[…] is the degree of node i. hierarchical clustering with average linkage method was applied using tom as the distance matrix, followed by the dynamic tree cutting procedure implemented in the r package dynamictreecut [], requiring minimal module size as 20. for each identified module, a wilcoxon signed rank test was applied to the expression alteration scores of proteins in the module. modules […]

[…] r function on the distances described as (1-coinhibition). sub-clustering was identified in the obtained dendrogram using a dynamic branch cutting method by using the cutreedynamic function of the dynamictreecut r package with the non-default parameter settings minimum cluster size = 3 and deepsplit = 4., by4742 s. cerevisiae cells were grown overnight in ypd medium. the day after, 1 × 109 […]