Given an nblast all by all score matrix (which may be specified by a package
default) and/or a vector of neuron identifiers use hclust
to
carry out a hierarchical clustering. The default value of the distfun
argument will handle square distance matrices and R dist
objects.
1 2 
neuron_names 
character vector of neuron identifiers. 
method 
clustering method (default Ward's). 
scoremat 
score matrix to use (see 
distfun 
function to convert distance matrix returned by

... 
additional parameters passed to hclust. 
maxneurons 
set this to a sensible value to avoid loading huge (order N^2) distances directly into memory. 
An object of class hclust
which describes the tree
produced by the clustering process.
Other scoremats: sub_dist_mat
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  library(nat)
kcscores=nblast_allbyall(kcs20)
hckcs=nhclust(scoremat=kcscores)
# divide hclust object into 3 groups
library(dendroextras)
dkcs=colour_clusters(hckcs, k=3)
# change dendrogram labels to neuron type, extracting this information
# from type column in the metadata data.frame attached to kcs20 neuronlist
labels(dkcs)=with(kcs20[labels(dkcs)], type)
plot(dkcs)
# 3d plot of neurons in those clusters (with matching colours)
open3d()
plot3d(hckcs, k=3, db=kcs20)
# names of neurons in 3 groups
subset(hckcs, k=3)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.