Class "BootstrapClusterTest"
Description
Performs a nonparametric bootstrap (sampling with replacement) test to determine whether the clusters found by an unsupervised method appear to be robust in a given data set.
Usage
1  BootstrapClusterTest(data, FUN, subsetSize, nTimes=100, verbose=TRUE, ...)

Arguments
data 
A data matrix, numerical data frame, or

FUN 
A 
... 
Additional arguments passed to the classifying function, 
subsetSize 
An optional integer argument. If present,
each iteration of the bootstrap selects 
nTimes 
The number of bootstrap samples to collect. 
verbose 
A logical flag 
Objects from the Class
Objects should be created using the BootstrapClusterTest
function, which performs the requested bootstrap on the
clusters. Following the standard R paradigm, the resulting object can be
summarized and plotted to determine the results of the test.
Slots
f
:A
function
that, given a data matrix, returns a vector of cluster assignments. Examples of functions with this behavior arecutHclust
,cutKmeans
,cutPam
, andcutRepeatedKmeans
.subsetSize
:The number of rows to be included in each bootstrap sample.
nTimes
:An integer, the number of bootstrap samples that were collected.
call
:An object of class
call
, which records how the object was produced.result
:Object of class
matrix
containing, for each pair of columns in the original data, the number of times they belonged to the same cluster of a bootstrap sample.
Extends
Class ClusterTest
, directly. See that class for
descriptions of the inherited methods image
and hist
.
Methods
 summary
signature(object = BootstrapClusterTest)
: Write out a summary of the object.
Author(s)
Kevin R. Coombes krc@silicovore.com
References
Kerr MK, Churchill GJ.
Bootstrapping cluster analysis: Assessing the reliability of
conclusions from microarray experiments.
PNAS 2001; 98:89618965.
See Also
ClusterTest
,
PerturbationClusterTest
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  showClass("BootstrapClusterTest")
## simulate data from two different groups
d1 < matrix(rnorm(100*30, rnorm(100, 0.5)), nrow=100, ncol=30, byrow=FALSE)
d2 < matrix(rnorm(100*20, rnorm(100, 0.5)), nrow=100, ncol=20, byrow=FALSE)
dd < cbind(d1, d2)
cols < rep(c('red', 'green'), times=c(30,20))
colnames(dd) < paste(cols, c(1:30, 1:20), sep='')
## peform your basic hierarchical clustering...
hc < hclust(distanceMatrix(dd, 'pearson'), method='complete')
## bootstrap the clusters arising from hclust
bc < BootstrapClusterTest(dd, cutHclust, nTimes=200, k=3, metric='pearson')
summary(bc)
## look at the distribution of agreement scores
hist(bc, breaks=101)
## let heatmap compute a new dendrogram from the agreement
image(bc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)
## plot the agreement matrix with the original dendrogram
image(bc, dendrogram=hc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)
## bootstrap the results of PAM
pamc < BootstrapClusterTest(dd, cutPam, nTimes=200, k=3)
image(pamc, dendrogram=hc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)
## contrast the behavior when all the data comes from the same group
xx < matrix(rnorm(100*50, rnorm(100, 0.5)), nrow=100, ncol=50, byrow=FALSE)
hct < hclust(distanceMatrix(xx, 'pearson'), method='complete')
bct < BootstrapClusterTest(xx, cutHclust, nTimes=200, k=4, metric='pearson')
summary(bct)
image(bct, dendrogram=hct, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)
## cleanup
rm(d1, d2, dd, cols, hc, bc, pamc, xx, hct, bct)
