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

TLDR

Hierarchical clustering is widely used to detect clusters in genomic data, but the common constant‑height cutoff method is inflexible and performs poorly on complex dendrograms. We present the Dynamic Tree Cut R package, which implements novel dynamic branch‑cutting methods that adapt to dendrogram shape. The package’s methods are illustrated on protein–protein interaction networks and simulated gene‑expression data. Compared with the constant‑height cutoff, Dynamic Tree Cut identifies nested clusters, offers shape‑parameter tuning, supports automation, and can combine hierarchical clustering with PAM for improved outlier detection. The Dynamic Tree Cut package is available as an R package at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/BranchCutting, with supplementary data online.

Abstract

Abstract 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 Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.