R Under development (unstable) (2024-04-27 r86487 ucrt) -- "Unsuffered Consequences"
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> library(ClassDiscovery)
Loading required package: cluster
Loading required package: oompaBase
> 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)
My mosaic, an object of the Mosaic class.

Call:
	Mosaic(data = dd, sampleMetric = "pearson", geneMetric = "spearman", usecor = TRUE, center = TRUE)

Sample dendrogram constructed with "average" linkage and "pearson" distance metric.

Gene dendrogram constructed with "average" linkage and "spearman" distance metric.
> 
> # 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)
> 
> proc.time()
   user  system elapsed 
   0.67    0.09    0.75