library(ClassDiscovery)
suppressWarnings( RNGversion("3.5.3") )
set.seed(587677)
# simulate data from three different sample groups
d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
dd <- cbind(d1, d2, d3)
kind <- factor(rep(c('red', 'green', 'blue'), each=10))

# prepare the Mosaic object
m <- Mosaic(dd, sampleMetric='pearson', geneMetric='spearman', center=TRUE, usecor=TRUE)
summary(m)

# The default plot with red-green color map
plot(m, col=redgreen(64))

# change to a blue-yellow color map, and mark the four top splits in the sample
# direction with a color bar along the top
plot(m, col=blueyellow(128), sampleClasses=4,
     sampleColors=c('red', 'green', 'blue', 'black'))

# This time, mark the three classes that we know are there
plot(m, col=blueyellow(128), sampleClasses=kind,
     sampleColors=c('red', 'green', 'blue'))

plot(m, col=blueyellow(128), geneClasses=3, geneColors=c('red', 'green', 'black'))

# In addition, mark the top 5 splits in the gene dendrogram
plot(m, col=blueyellow(128),
     sampleClasses=kind, sampleColors=c('red', 'green', 'black'),
     geneClasses=5, geneColors=c('cyan', 'magenta', 'royalblue', 'darkgreen', 'orange'))

# plot the sample dendrogram by itself
cols <- as.character(kind)
pltree(m, labels=1:30, colors=cols)

# cleanup
rm(d1, d2, d3, dd, kind, cols, m)