Nothing
#' @include aaa.R
#' @include generics.R
#' @include grouping.R
#' @include pgVirtual.R
NULL
#' @describeIn kmerLink Linking for pgVirtual subclasses
#'
#' @param lowMem logical. Should low memory footprint be ensured over
#' computation speed
#'
#' @param kmerSize The size of kmers to use for similarity calculations.
#'
#' @param lowerLimit The lower threshold for similarity below which it is set to
#' 0
#'
#' @param rescale Should Similarities be normalised between lowerLimit and 1
#'
#' @param transform Transformation function to apply to similarities
#'
#' @param pParam An optional BiocParallelParam object that defines the workers
#' used for parallelisation.
#'
#' @param algorithm The name of the community detection algorithm from igraph to
#' use for gene grouping. See \code{\link[igraph]{communities}} for an overview.
#' The trailing '.community' can be omitted from the name. Default is 'infomap',
#' which is also the recommended.
#'
#' @importFrom kebabs getExRep spectrumKernel linearKernel
#' @importFrom BiocParallel SerialParam bpnworkers
#'
setMethod(
'kmerLink', 'pgVirtual',
function(object, lowMem, kmerSize, lowerLimit, rescale, transform, pParam, algorithm, ...) {
.fillDefaults(defaults(object))
gRep <- getRep(object, 'random')
er <- getExRep(gRep, spectrumKernel(kmerSize), sparse = TRUE)
if(missing(pParam)) {
sim <- linearKernel(er, sparse=TRUE, diag=FALSE, lowerLimit=lowerLimit)
} else {
sim <- lkParallel(er, pParam, bpnworkers(pParam), lowerLimit=lowerLimit)
}
sim <- transformSim(sim, lowerLimit, rescale, transform)
members <- igGroup(sim, algorithm, ...)
groupInfo(object)$paralogue <- members
object
}
)
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