Wrapper function to compute all by all NBLAST scores for a set of neurons

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.

Usage

nblast_allbyall(x, ...)

# S3 method for class 'character'
nblast_allbyall(x, smat = NULL, db = getOption("nat.default.neuronlist"), ...)

# S3 method for class 'neuronlist'
nblast_allbyall(
  x,
  smat = NULL,
  distance = FALSE,
  normalisation = c("raw", "normalised", "mean"),
  ...
)

Arguments

x

Input neurons (neuronlist or character vector)

...

Additional arguments for methods or nblast

smat

the scoring matrix to use (see details of nblast for meaning of default NULL value)

db

A neuronlist or a character vector naming one. Defaults to value of options("nat.default.neuronlist")

distance

logical indicating whether to return distances or scores.

normalisation

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.

Details

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.

TODO

It would be a good idea in the future to implement a parallel version of this function.

See also

nblast, sub_score_mat, nhclust

Examples

library(nat)
kcs20.scoremat=nblast_allbyall(kcs20)
kcs20.hclust=nhclust(scoremat=kcs20.scoremat)
#> The "ward" method has been renamed to "ward.D"; note new "ward.D2"
plot(kcs20.hclust)