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

Description

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 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"),
  ...
)

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.

TODO

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

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.

Examples

Run this code

library(nat)
kcs20.scoremat=nblast_allbyall(kcs20)
kcs20.hclust=nhclust(scoremat=kcs20.scoremat)
plot(kcs20.hclust)