mhca

Mahalanobis distance-based hierarchical cluster analysis, in which elliptical clusters get found naturally.

Install:

devtools::install_github("tsieger/mhca")

Example (Comparison with Classical HCA):

library(mhca)
opar<-par(mfrow=c(2,2))

k<-3
n<-nrow(xy)

# classical HCA
h<-hclust(dist(xy))

# Mahalanobis HCA
mh<-mhclust(xy,thresh=.3)

ch<-cutree(h,k=k)
cmh<-cutree(mh,k=k)

# feature space plots with 3 top clusters
plot(xy[,1],xy[,2],asp=1,col=ch,main='HCA',frame=FALSE)
plot(xy[,1],xy[,2],asp=1,col=cmh,main='Mahalanobis HCA',frame=FALSE)

# HCA dendrogram
plot(h,hang=0,labels=FALSE,main='Dendrogram of HCA')
y<-min(h$height)-diff(range(h$height))/20
text(1:n,y,(1:n)[h$order],col=ch[h$order],srt=90)

# MHCA dendrogram
plot(mh,labels=FALSE,main='Dendrogram of MHCA')
y<-min(mh$height)-diff(range(mh$height))/10
text(1:n,y,(1:n)[mh$order],col=cmh[mh$order],srt=90)

par(opar)

Find out more at https://github.com/tsieger/mhca.

You can use https://github.com/tsieger/idendro or https://github.com/tsieger/idendr0 to interactively explore HCA dendrograms:

Reference

Please cite the paper "Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data" in Cytometry Part A.