Calls nblast
to compute the actual scores. Can accept
either a neuronlist
or neuron names as a character vector. This is a thin
wrapper around nblast
and its main advantage is the option of "mean"
normalisation for forward and reverse scores, which is the most sensible
input to give to a clustering algorithm as well as the choice of returning
a distance matrix.
nblast_allbyall(x, ...)# S3 method for character
nblast_allbyall(x, smat = NULL, db = getOption("nat.default.neuronlist"), ...)
# S3 method for neuronlist
nblast_allbyall(
x,
smat = NULL,
distance = FALSE,
normalisation = c("raw", "normalised", "mean"),
...
)
Input neurons (neuronlist
or character vector)
Additional arguments for methods or nblast
the scoring matrix to use (see details of nblast
for meaning of default NULL
value)
A neuronlist
or a character vector naming one.
Defaults to value of options("nat.default.neuronlist")
logical indicating whether to return distances or scores.
the type of normalisation procedure that should be
carried out, selected from 'raw'
, 'normalised'
or
'mean'
(i.e. the average of normalised scores in both directions).
If distance=TRUE
then this cannot be raw.
It would be a good idea in the future to implement a parallel version of this function.
Note that nat
already provides a function
nhclust
for clustering, which is a wrapper for R's
hclust
function. nhclust
actually expects raw scores
as input.
nblast, sub_score_mat, nhclust
library(nat)
kcs20.scoremat=nblast_allbyall(kcs20)
kcs20.hclust=nhclust(scoremat=kcs20.scoremat)
plot(kcs20.hclust)
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