nhclust
From nat.nblast v1.6.2
by James Manton
Cluster a set of neurons
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.
Usage
nhclust(neuron_names, method = "ward", scoremat = NULL, distfun = as.dist, ..., maxneurons = 4000)
Arguments
 neuron_names
 character vector of neuron identifiers.
 method
 clustering method (default Ward's).
 scoremat
 score matrix to use (see
sub_score_mat
for details of default).  distfun
 function to convert distance matrix returned by
sub_dist_mat
into R dist object (default=as.dist
).  ...
 additional parameters passed to hclust.
 maxneurons
 set this to a sensible value to avoid loading huge (order N^2) distances directly into memory.
Value

An object of class
hclust
which describes the tree
produced by the clustering process.
See Also
Other scoremats: sub_dist_mat
Examples
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)
Community examples
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