Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut library for R

Summary: Hierarchical clustering is a widely used method for detecting clusters in genomic data. Clusters are defined by cut-ting branches off the dendrogram. A common but inflexible method uses a constant height cutoff value; this method exhibits subopti-mal performance on complicated dendrograms. We present the Dynamic Tree Cut R library 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 library available a