library(ClassDiscovery)
suppressWarnings( RNGversion("3.5.3") )
set.seed(147676)
# 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))
# peform your basic hierarchical clustering...
hc <- hclust(distanceMatrix(dd, 'pearson'), method='complete')

# bootstrap the clusters arising from hclust
bc <- PerturbationClusterTest(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 K-means
kmc <- PerturbationClusterTest(dd, cutPam, nTimes=200, k=3)
image(kmc, 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 <- PerturbationClusterTest(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, kmc, xx, hct, bct)