Description Usage Arguments Details TODO See Also Examples

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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"),
...
)
``` |

`x` |
Input neurons ( |

`...` |
Additional arguments for methods or |

`smat` |
the scoring matrix to use (see details of |

`db` |
A |

`distance` |
logical indicating whether to return distances or scores. |

`normalisation` |
the type of normalisation procedure that should be
carried out, selected from |

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.

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

`nblast, sub_score_mat, nhclust`

1 2 3 4 | ```
library(nat)
kcs20.scoremat=nblast_allbyall(kcs20)
kcs20.hclust=nhclust(scoremat=kcs20.scoremat)
plot(kcs20.hclust)
``` |

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