cutreeDynamicTree: Dynamic Dendrogram Pruning Based on Dendrogram Only

In dynamicTreeCut: Methods for Detection of Clusters in Hierarchical Clustering Dendrograms

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, [e-mail], with contributions by Peter Langfelder, [e-mail].

References

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

See Also

hclust, cutreeHybrid

dynamicTreeCut documentation built on May 2, 2019, 6:12 a.m.