hclust.progenyClust {progenyClust}

Hierarchical Clustering

Description

hierarchical clustering function for progeny clustering

Usage

hclust.progenyClust(x,k,h.method='ward.D2',dist='euclidean',p=2,...)

Arguments

x

a numeric matrix, data frame or dist object.

k

an integer specifying the number of clusters.

h.method

the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2" (default), "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

dist

the distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given.

p

The power of the Minkowski distance, when dist="minkowski".

...

additional arguments in hclust(...).

Details

The function hclust.progenyClust mainly streamlines dist, hclust and cutree into one, and structures the output to be directly used by progenyClust. Most arguments and explanations were kept the same to ensure consistancy and avoid confusion. For more details, please check each individual function.

Value

cluster

A vector of integers (from 1:k) indicating the cluster membership for each sample.

tree

An object of class hclust which describes the tree produced by the clustering process.

dist

A dissimilarity structure as produced by dist.

Author(s)

C.W. Hu, Rice University

References

Hu, C.W., et al. "Progeny Clustering: A Method to Identify Biological Phenotypes." Scientific reports 5 (2015).
http://www.nature.com/articles/srep12894

Examples

# a 3-cluster 2-dimensional example dataset
data('test')

# default progeny clsutering
progenyClust(test,FUNclust=hclust.progenyClust,ncluster=2:5)->pc

# plot the scores to select the optimal cluster number
plot(pc)

# plot the clustering results with the optimal cluster number
plot(pc,test)