hclustfc | R Documentation |
Given a vector of flycircuit gene/neuron names or neuronids use
hclust to carry out a hierarchical clustering. The default value of distfun
will handle square distance matrices and R dist objects. Note that hclustfc
is a thin wrapper around the nhclust
function and that
is what you want to use if you have calculated a score matrix yourself e.g.
for a set of all by all pairwise neurons similarities computed by
nblast
or nblast_allbyall
.
hclustfc( gns, method = "ward", scoremat = getOption("flycircuit.scoremat"), unsquare = FALSE, distfun = as.dist, ..., maxneurons = 4000 )
gns |
FlyCircuit identifiers (passed to fc_gene_name). |
method |
clustering method (default Ward's). |
scoremat |
score matrix to use (see |
unsquare |
Whether to return the square root of the distance calculated
by |
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. |
when method
is ward, ward.D or ward.D2 it may make sense to
unsquare the resultant distance before plotting.
fc_gene_name, nhclust,
hclust, dist, plot3d.hclust
data(kcs20, package='nat') hckcs=hclustfc(names(kcs20)) # plot basic dendrogram plot(hckcs) # plot dendrogram using unsquared distance plot(hclustfc(names(kcs20), unsquare=TRUE)) # divide hclust object into 3 groups library(dendroextras) plot(colour_clusters(hckcs, k=3)) # 3d plot of neurons in those clusters (with matching colours) library(nat) plot3d(hckcs, k=3, db=kcs20) # names of neurons in 3 groups subset(hckcs, k=3)
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