dynamicTreeCut: cutreeDynamicTree – R documentation

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cutreeDynamicTree

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