Bioinformatics Advance Access originally published online on November 16, 2007
Bioinformatics 2008 24(5):719-720; doi:10.1093/bioinformatics/btm563
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Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
1Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and 2Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA
*To whom correspondence should be addressed.
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Abstract |
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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: stevitihit{at}yahoo.com
Supplementary information: Supplementary data are available at Bioinformatics online.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
Received on September 12, 2007; revised on September 12, 2007; accepted on November 6, 2007
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