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Feb 03, Two methods for hierarchical clustering are introduced: (i) dynamic tree cut; and (ii) dynamic hybrid cut. Dynamic tree cut is a top-down algorithm that relies solely on the dendrogram. The algorithm implements an adaptive, iterative process of cluster decomposition and combination and stops when the number of clusters becomes stable. Dynamic hybrid cut is a bottom-up algorithm Estimated Reading Time: 4 mins. exibility, we present two variants of the method.

The rst variant, called \Dynamic Tree" cut, is a top-down algorithm relying only on the dendrogram and respecting the order of the clustered objects on it. This method has been utilized for identifying biologically meaningful gene modules in gene co-expression. Hierarchical clustering with dynamic tree cut methods are applied to discover the modules that are enriched with disease associated genes. The evaluation on real networks and simulated networks show that N2V-HC performs better than existing methods in network module treegrinding.bar: Tao Wang, Qidi Peng, Bo Liu, Yongzhuang Liu, Yadong Wang.

This algorithm re-adjusts the results of hierarchical clustering while retaining the advantages of the dynamic tree cut method. The validity of the algorithm is verified using six datasets from microarray and RNA-seq data.

The k-module algorithm has fewer iterations, which leads to lower complexity. We verify that the gene modules obtained by the k-module algorithm have high enrichment scores and strong Author: Jie Hou, Xiufen Ye, Chuanlong Li, Yixing Wang. The dynamic tree cut algorithm was applied to detect gene modules.

Gene significance (GS) was defined as the correlation coefficient between gene expression and module traits.

The module eigengene was calculated as a summary profile for each module. Module significance was defined as the correlation coefficient between a module’s eigengene and traits. ity topological overlap matrix.

Using dynamic tree cutting, different numbers of clusters (modules) are obtained. The second stage is the k-module algorithm proposed in this paper.

Third, we estimated the similarity of the modules and merged the genes with high co-expression.

The input of the k-module algorithm is the gene modules obtained by WGCNA. The main purpose of theAuthor: Jie Hou, Xiufen Ye, Chuanlong Li, Yixing Wang. # Set the minimum module size minModuleSize = 20; # Module identification using dynamic tree cut dynamicMods = cutreeDynamic(dendro = geneTree, method="tree", minClusterSize = minModuleSize); #dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM, method="hybrid", deepSplit = 2, pamRespectsDendro = FALSE, minClusterSize = minModuleSize.

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