Examples.R

## `hclust` and `dendrogram` objects

# genieclust::gclust() also output `hclust` object
# amap::hcluster() also output `hclust` object
# FactoClass::ward.cluster() also output `hclust` object


d <- dist(USArrests)

hc <- hclust(d, "ave")
den <- as.dendrogram(hc)

p1 <- autoplot(hc) + geom_tiplab()
p2 <- autoplot(den) + geom_rect_subtree(4)
plot_list(p1, p2, ncol=2)



## `linkage` object


library("mdendro")
lnk <- linkage(d, digits = 1, method = "complete")
autoplot(lnk, layout = 'circular') + geom_tiplab() + 
  scale_color_subtree(4) + theme_tree()


## `agnes`, `diana` and `twins` objects

library(cluster)
x1 <- agnes(mtcars)
x2 <- diana(mtcars)

p1 <- autoplot(x1) + geom_tiplab()
p2 <- autoplot(x2) + geom_tiplab()
plot_list(p1, p2, ncol=2)



## `pvclust` object


library(pvclust)
data(Boston, package = "MASS")

set.seed(123)
result <- pvclust(Boston, method.dist="cor", method.hclust="average", nboot=1000, parallel=TRUE)
autoplot(result, label_edge=TRUE, pvrect = TRUE) + geom_tiplab()


## `bclust` object

### S4
library(flexclust)
data(iris)
bc1 <- bclust(iris[,1:4], 3, base.k=5)
autoplot(bc1)

### S3
library(e1071)
bc2 <- bclust(iris[,1:4], 3, base.centers=5)
autoplot(bc2)

## `protoclust` object

library(protoclust)
data(Boston, package = "MASS")
d <- dist(Boston)
pc <- protoclust(d)
plotwithprototypes(pc)
autoplot(pc)


## `hdbscan` object

library(dbscan)
res <- hdbscan(moons, minPts = 5)
autoplot(res)


## `hkmeans` object

library(factoextra)
# Load data
data(USArrests)
# Scale the data
df <- scale(USArrests)

# Compute hierarchical k-means clustering
res.hk <-hkmeans(df, 4)

hkmeans_tree(res.hk, cex = 0.6)
fviz_dend(res.hk, cex = 0.6)

library(ggtreeDendro)
autoplot(res.hk) + geom_rect_subtree(4, color=c("red", "blue", "green", "purple"))
autoplot(res.hk$hclust) + scale_color_subtree(4)


# ClusterExperiment

library(clusterExperiment)
data(simData)

#create a clustering, for 8 clusters (truth was 3) 
cl <-clusterSingle(simData, subsample=FALSE, 
                   sequential=FALSE, 
                   mainClusterArgs=list(clusterFunction="pam", clusterArgs=list(k=8)))

#create dendrogram of clusters and then 
# merge clusters based ondendrogram: 
cl <- makeDendrogram(cl) 
cl <- mergeClusters(cl,mergeMethod="adjP",DEMethod="limma",
                    cutoff=0.1,plot=FALSE) 

plotDendrogram(cl,leafType="samples",whichClusters="all",plotType="colorblock")

library(ggtreeDendro)
autoplot(cl)

## dendro

library(ggplot2)
library(ggtree)
library(ggdendro)
library(ggtreeDendro)

hc <- hclust(dist(USArrests), "ave")
x <- dendro_data(hc)

autoplot(x) + geom_tiplab()


## HGC

library(HGC)
data(Pollen)
Pollen.PCs <- Pollen[["PCs"]]
Pollen.Label.Tissue <- Pollen[["Tissue"]]
Pollen.Label.CellLine <- Pollen[["CellLine"]]

Pollen.SNN <- SNN.Construction(Pollen.PCs)
rownames(Pollen.SNN) <- rownames(Pollen.PCs)
Pollen.ClusteringTree <- HGC.dendrogram(G = Pollen.SNN)
Pollen.labels <- data.frame(Tissue = Pollen.Label.Tissue,
                            CellLine = Pollen.Label.CellLine)
HGC.PlotDendrogram(tree = Pollen.ClusteringTree,
                   k = 5, plot.label = TRUE,
                   labels = Pollen.labels)


## 

library(treeio)
library(ggtree)
library(ggtreeDendro)

x <- as.phylo(as.dendrogram(Pollen.ClusteringTree))

p1 <- ggtree(x, layout = 'dendrogram', ladderize = FALSE)

p <- ggtree(x, layout = 'dendrogram', ladderize = FALSE, branch.length = 'none')

g <- gheatmap(p, Pollen.labels[,1, drop=FALSE], width=.05, colnames_angle = 0, colnames_offset_y = -20, color=NA)
g <- g + ggnewscale::new_scale_fill()
g <- gheatmap(g, Pollen.labels[,2, drop=FALSE], width=.05, colnames_angle = 0, colnames_offset_y = -20, color=NA, offset = 1) 

g + scale_color_subtree(5)