COMMUNAL-class: Class '"COMMUNAL"'

In COMMUNAL: Robust Selection of Cluster Number K

Description

This is the class of the object returned by the COMMUNAL function. It contains all the clustering results, including the values of the validation measures, and the cluster assignments. Cluster assignments for a particular number of clusters can be conveniently extracted with the getClustering(k) method.

Extends

All reference classes extend and inherit methods from "envRefClass".

Fields

cluster.list:

Object of class list List of all cluster assignments from each algorithm. Use the getClustering method to conveniently extract clusters for a given value of k.

measures:

Object of class array The validation measures calculated

clus.methods:

Object of class character The clustering algorithms used.

ks:

Object of class numeric The range of cluster numbers tested.

validation:

Object of class character The validation measures used.

dist.metric:

Object of class character The distance metric used to calculate validation scores.

item.names:

Object of class character The names of the clustered items.

call:

Object of class call The call to COMMUNAL used to create the object.

Methods

getClustering(k):

For given value of k, extract the cluster assignments from each clustering algorithm.

show():

Default print method.

Author(s)

Albert Chen and Timothy E Sweeney
Maintainer: Albert Chen [e-mail]

See Also

Vignette and COMMUNAL. The function COMMUNAL returns an object of this class.

Examples

showClass("COMMUNAL")
## Not run: 
## create artificial data set with 3 distinct clusters
set.seed(1)
V1 = c(abs(rnorm(100, 2)), abs(rnorm(100, 50)), abs(rnorm(100, 140)))
V2 = c(abs(rnorm(100, 2, 8)), abs(rnorm(100, 55, 4)), abs(rnorm(100, 105, 1)))
data <- t(data.frame(V1, V2))
colnames(data) <- paste("Sample", 1:ncol(data), sep="")
rownames(data) <- paste("Gene", 1:nrow(data), sep="")

## run COMMUNAL with defaults
result <- COMMUNAL(data=data, ks=seq(2,5))  # result is a COMMUNAL object
k <- 3                                # suppose optimal cluster number is 3
clusters <- result$getClustering(k)   # extract clusters
mat.key <- clusterKeys(clusters)      # get core clusters
examineCounts(mat.key)                # all algorithms agree
core <- returnCore(mat.key, agreement.thresh=50) # find 'core' cluster assignments
table(core) # the 'core' cluster sizes

result$measures # access validation measures

## End(Not run)

COMMUNAL documentation built on May 29, 2017, 6:36 p.m.