nhclust | R Documentation |
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.
nhclust(
neuron_names,
method = "ward",
scoremat = NULL,
distfun = as.dist,
...,
maxneurons = 4000
)
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 |
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.
hclust, dist
Other scoremats:
sub_dist_mat()
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)
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