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

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ID: 291287
2007
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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: stevitihit@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W2156247618 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Peter Langfelder, Bin Zhang, Steve Horvath
Journal BMC Bioinformatics
Year 2007
DOI
10.1093/bioinformatics/btm563
URL
https://doi.org/10.1093/bioinformatics/btm563
Keywords Keywords not found

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