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 that improves the detection of outlying members of each cluster. This variant can be considered a hybrid of hierarchical clustering and modified partitioning around medoids (PAM; Kaufman and Rousseeuw, 1990).
The protein-protein interaction network in Drosophila (BioGRID) is analyzed with reference to known gene ontologies. Analysis of simulated gene expression data with known cluster membership demonstrates the superiority of the new methods over both static tree cut (fixed height cutoff) and PAM (objects assigned to closest medoid).
Summary: Hierarchical clustering is a widely used method for detecting clusters in genomic data. Clusters are defined by cutting branches off the dendrogram. A common but inflexible method uses a constant height cutoff value; this method exhibits suboptimal performance on complicated dendrograms. We present the Dynamic Tree Cut R package that implements novel dynamic branch cutting methods for detecting clusters in a dendrogram depending on their shape. Compared to the constant height cutoff method, our techniques offer the following advantages: (1) they are capable of identifying nested clusters; (2) they are flexible—cluster shape parameters can be tuned to suit the application at hand; (3) they are suitable for automation; and (4) they can optionally combine the advantages of hierarchical clustering and partitioning around medoids, giving better detection of outliers. We illustrate the use of these methods by applying them to protein–protein interaction network data and to a simulated gene expression data set.
Availability: The Dynamic Tree Cut method is implemented in an R package available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/BranchCutting
Supplementary information: Supplementary data are available at Bioinformatics online.
Peter Langfelder, Bin Zhang, Steve Horvath. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics (2008) 24(5): 719-720 first published online November 16, 2007 doi:10.1093/bioinformatics/btm563