R: Dynamic Dendrogram Pruning Based on Dendrogram Only

cutreeDynamicTree {dynamicTreeCut} | R Documentation

## Dynamic Dendrogram Pruning Based on Dendrogram Only

### Description

Detect clusters in a hierarchical dendrogram using a variable cut height approach. Uses only the information in the dendrogram itself is used (which may give incorrect assignment for outlying objects).

### Usage

    cutreeDynamicTree(dendro, maxTreeHeight = 1, deepSplit = TRUE, minModuleSize = 50)

### Arguments

`dendro` | Hierarchical clustering dendrogram such produced by `hclust`.
`maxTreeHeight` | Maximum joining height of objects to be considered part of clusters.
`deepSplit` | If `TRUE`, method will favor sensitivity and produce more smaller clusters. When `FALSE`, there will be fewer bigger clusters.
`minModuleSize` | Minimum module size. Branches containing fewer than `minModuleSize` objects will be left unlabeled.

### Details

A variable height branch pruning technique for dendrograms produced by hierarchical clustering. Initially, branches are cut off at the height `maxTreeHeight`; the resulting clusters are then examined for substructure and if subclusters are detected, they are assigned separate labels. Subclusters are detected by structure and are required to have a minimum of `minModuleSize` objects on them to be assigned a separate label. A rough degree of control over what it means to be a subcluster is implemented by the parameter `deepSplit`.

### Value

A vector of numerical labels giving assignment of objects to modules. Unassigned objects are labeled 0, the largest module has label 1, next largest 2 etc.

### Author(s)

Bin Zhang, binzhang.ucla@gmail.com, with contributions by Peter Langfelder, Peter.Langfelder@gmail.com.

### References

http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/BranchCutting

### See Also

`hclust`, `cutreeHybrid`

[Package dynamicTreeCut version 1.63-1 Index]