Nothing
#' Neuron similarity, search and clustering tools
#'
#' \bold{nat.nblast} provides tools to compare neuronal morphology using the
#' NBLAST algorithm (Costa et al. 2016).
#'
#' @section Similarity and search:
#'
#' The main entry point for similarity and search functions is
#' \code{\link{nblast}}. Traced neurons will normally be converted to the
#' \code{\link[nat]{dotprops}} format for search. When multiple neurons are
#' compared they should be in a \code{\link[nat]{neuronlist}} object.
#'
#' The current NBLAST version (2) depends on a scoring matrix. Default
#' matrices trained using \emph{Drosophila} neurons in the FCWB template brain
#' space are distributed with this package (see \code{\link{smat.fcwb}}); see
#' \bold{Scoring Matrices} section below for creating new scoring matrices.
#'
#' \code{nblast} makes use of a more flexible but more complicated function
#' \code{NeuriteBlast} which includes several additional options. The function
#' \code{WeightedNNBasedLinesetMatching} provides the primitive functionality
#' of finding the nearest neighbour distances and absolute dot products for
#' two sets of segments. Neither of these functions are intended for end use.
#'
#' Calculating all by all similarity scores is facilitated by the
#' \code{\link{nblast_allbyall}} function which can take either a
#' \code{\link{neuronlist}} as input or a character vector naming (a subset)
#' of neurons in a (large) \code{\link{neuronlist}}. The
#' \code{\link{neuronlist}} containing the input neurons should be resident in
#' memory i.e. not the \code{\link{neuronlistfh}}.
#'
#' @section Clustering:
#'
#' Once an all by all similarity score matrix is available it can be used as
#' the input to a variety of clustering algorithms. \code{\link{nhclust}}
#' provides a convenient wrapper for R's hierarchical clustering function
#' \code{\link{hclust}}. If you wish to use another clustering function, then
#' you can use the \code{\link{sub_dist_mat}} to convert a raw similarity
#' score matrix into a normalised distance matrix (or R \code{\link{dist}}
#' object) suitable for clustering. If you need a similarity matrix or want to
#' modify the normalisation then you can use \code{\link{sub_score_mat}}.
#'
#' Note that raw NBLAST scores are not symmetric (i.e. S(A,B) is not equal to
#' S(B,A)) so before clustering we construct a symmetric similarity/distance
#' matrix \code{1/2 * ( S(A,B)/S(A,A) + S(B,A)/S(B,B) )}. See
#' \code{\link{sub_score_mat}}'s documentation for details.
#'
#' @section Cached scores:
#'
#' Although NBLAST is fast and can be parallelised, it makes sense to cache to
#' disk all by all similarity scores for a group of neurons that will be
#' subject to repeated clustering or other analysis. The matrix can simply be
#' saved to disk and then reloaded using base R functions like
#' \code{\link{save}} and \code{\link{load}}. \code{\link{sub_score_mat}} and
#' \code{\link{sub_dist_mat}} can be used to extract a subset of scores from
#' this raw score matrix. For large matrices, the \code{bigmemory} or
#' \code{ff} packages allow matrices to be stored on disk and portions loaded
#' into memory on demand. \code{\link{sub_score_mat}} and
#' \code{\link{sub_dist_mat}} work equally well for regular in-memory matrices
#' and these disk-backed matrices.
#'
#' To give an example, for 16,129 neurons from the flycircuit.tw dataset, the
#' 260,144,641 comparisons took about 250 hours of compute time (half a day on
#' ~20 cores). When saved to disk as single precision (i.e. 4 bytes per score)
#' \code{ff} matrix they occupy just over 1Gb.
#'
#' @section Calculating scoring matrices:
#'
#' The NBLAST algorithm depends on appropriately calibrated scoring matrices.
#' These encapsulate the log odds ratio that a pair of segments come from two
#' structurally related neurons rather than two unrelated neurons, given the
#' observed distance and absolute dot product of the two segments. Scoring
#' matrices can be constructed using the \code{\link{create_scoringmatrix}}
#' function, supplying a set of matching neurons and a set of non-matching
#' neurons. See the \code{create_scoringmatrix} documentation for links to
#' lower-level functions that provide finer control over construction of the
#' scoring matrix.
#'
#' @section Package Options:
#'
#' There is one package option \code{nat.nblast.defaultsmat} which is
#' \code{NULL} by default, but could for example be set to one of the scoring
#' matrices included with the package such as \code{"smat.fcwb"} or to a new
#' user-constructed matrix.
#'
#' @references Costa, M., Ostrovsky, A.D., Manton, J.D., Prohaska, S., and
#' Jefferis, G.S.X.E. (2014). NBLAST: Rapid, sensitive comparison of neuronal
#' structure and construction of neuron family databases. bioRxiv preprint.
#' \doi{10.1101/006346}.
#'
#' @name nat.nblast-package
#' @aliases nat.nblast
#' @docType package
#' @import methods
#' @keywords package
#' @seealso \code{\link{nblast}}, \code{\link{smat.fcwb}},
#' \code{\link{nhclust}}, \code{\link{sub_dist_mat}},
#' \code{\link{sub_score_mat}}, \code{\link{create_scoringmatrix}}
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.