Name
Title | ||
|---|---|---|
Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. |
Abstract | ||
|---|---|---|
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 flexiblecluster 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 proteinprotein interaction network data and to a simulated gene expression data set. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/bioinformatics/btm563 | BIOINFORMATICS |
Keywords | Field | DocType |
coexpressionnetwork/branchcutting contact: stevitihit@yahoo.com supplementary information: supplementary data are available at bioinformatics online.,data clustering,genetics,shape parameter,hierarchical clustering | Hierarchical clustering,Cluster (physics),Data mining,Dendrogram,Computer science,Hierarchical clustering of networks,Outlier,Interaction network,Bioinformatics,Single-linkage clustering,Medoid | Journal |
Volume | Issue | ISSN |
24 | 5 | 1367-4803 |
Citations | PageRank | References |
126 | 8.45 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Langfelder | 1 | 514 | 27.95 |
| Bin Zhang | 2 | 159 | 13.37 |
| Steve Horvath | 3 | 747 | 50.14 |