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#' Wrappers for graph-based clustering
#'
#' Perform graph-based clustering using community detection methods on a nearest-neighbor graph,
#' where nodes represent cells or k-means centroids.
#' This has been deprecated in favor of directly using \code{\link{clusterRows}} from the \pkg{bluster} package,
#' with \code{BLUSPARAM} set to \code{\link{NNGraphParam}()} or \code{\link{TwoStepParam}()}.
#'
#' @inheritParams buildSNNGraph
#' @param use.kmeans Logical scalar indicating whether k-means clustering should be performed.
#' @param clusterFUN Function that can take a \link{graph} object and return a \link{communities} object,
#' see examples in the \pkg{igraph} package.
#' @param kmeans.centers Integer scalar specifying the number of clusters to use for k-means clustering.
#' Defaults to the square root of the number of cells in \code{x}.
#' @param kmeans.args List containing additional named arguments to pass to \code{\link{kmeans}}.
#' @param full.stats Logical scalar indicating whether to return more statistics regarding the k-means clustering.
#' @param ... For the generics, additional arguments to pass to the specific methods.
#'
#' For the ANY methods, additional arguments to pass to \code{\link{buildSNNGraph}} or \code{\link{buildKNNGraph}}.
#'
#' For the SummarizedExperiment method, additional arguments to pass to the corresponding ANY method.
#'
#' For the SingleCellExperiment method, additional arguments to pass to the corresponding SummarizedExperiment method.
#'
#' @return
#' If \code{full.stats=FALSE}, a factor is returned containing the cluster assignment for each cell.
#'
#' If \code{full.stats=TRUE} and \code{use.kmeans=TRUE}, a \linkS4class{DataFrame} is returned with one row per cell.
#' This contains the columns \code{kmeans}, specifying the assignment of each cell to a k-means cluster;
#' and \code{igraph}, specifying the assignment of each cell to a graph-based cluster operating on the k-means clusters.
#' In addition, the \code{\link{metadata}} contains \code{graph}, a \link{graph} object where each node is a k-means cluster;
#' and \code{membership}, the graph-based cluster to which each node is assigned.
#'
#' @author Aaron Lun
#'
#' @details
#' We suggest using the \code{\link{clusterRows}} functionality instead as it provides a more general interface to clustering.
#' \itemize{
#' \item \code{clusterSNNGraph(x)} for a matrix-like object \code{x} is equivalent to
#' \code{clusterRows(t(x), NNGraphParam())}.
#' \item \code{clusterKNNGraph(x)} for a matrix-like object \code{x} is equivalent to
#' \code{clusterRows(t(x), NNGraphParam(shared=FALSE))}.
#' \item \code{clusterSNNGraph(x)} for a SummarizedExperiment object \code{x} is equivalent to
#' \code{clusterRows(t(logcounts(x)), NNGraphParam())}.
#' \item \code{clusterSNNGraph(x, use.dimred="PCA")} for a SingleCellExperiment object \code{x} is equivalent to
#' \code{clusterRows(reducedDim(x, "PCA"), NNGraphParam())}.
#' \item \code{clusterSNNGraph(x, use.kmeans=TRUE, use.dimred="PCA")} for a SingleCellExperiment object \code{x} is equivalent to
#' \code{clusterRows(reducedDim(x, "PCA"), TwoStepParam())}.
#' }
#'
#' @name clusterSNNGraph
#' @examples
#' library(scuttle)
#' sce <- mockSCE(ncells=500)
#' sce <- logNormCounts(sce)
#'
#' clusters <- clusterSNNGraph(sce)
#' table(clusters)
#'
#' # Can pass usual arguments to buildSNNGraph:
#' clusters2 <- clusterSNNGraph(sce, k=5)
#' table(clusters2)
#'
#' # Works with low-dimensional inputs:
#' sce <- scater::runPCA(sce, ncomponents=10)
#' clusters3 <- clusterSNNGraph(sce, use.dimred="PCA")
#' table(clusters3)
#'
#' # Turn on k-means for larger datasets, e.g.,
#' # assuming we already have a PCA result:
#' set.seed(101010)
#' bigpc <- matrix(rnorm(2000000), ncol=20)
#' clusters4 <- clusterSNNGraph(bigpc, d=NA, use.kmeans=TRUE, transposed=TRUE)
#' table(clusters4)
#'
#' # Extract the graph for more details:
#' clusters5 <- clusterSNNGraph(sce, use.dimred="PCA",
#' use.kmeans=TRUE, full.stats=TRUE)
#' head(clusters5)
#' metadata(clusters5)$graph
#'
#' @seealso
#' \code{\link{clusterRows}} with \code{BLUSPARAM} set to an instance of \linkS4class{NNGraphParam} or \linkS4class{TwoStepParam}.
NULL
#' @importFrom Matrix t
#' @importFrom stats kmeans
#' @importFrom igraph V V<-
.clusterXNNGraph <- function(x, ..., subset.row=NULL, transposed=FALSE, use.kmeans=FALSE, kmeans.centers=NULL,
kmeans.args=list(), full.stats=FALSE, graphFUN, clusterFUN)
{
if (use.kmeans) {
if (is.null(kmeans.centers)) {
kmeans.centers <- ceiling(sqrt(nrow(x)))
}
if (!transposed) {
if (!is.null(subset.row)) {
x <- x[subset.row,,drop=FALSE]
}
x <- t(x)
}
kmeans.args <- c(list(as.matrix(x), centers=kmeans.centers), kmeans.args)
k.out <- do.call(kmeans, kmeans.args)
nn.g <- graphFUN(k.out$centers, ..., transposed=TRUE)
clust.g <- clusterFUN(nn.g)$membership
output <- clust.g[k.out$cluster]
if (full.stats) {
df <- DataFrame(
kmeans=k.out$cluster,
igraph=factor(output)
)
metadata(df) <- list(graph=nn.g, membership=clust.g)
return(df)
}
} else {
nn.g <- graphFUN(x, ..., subset.row=subset.row, transposed=transposed)
output <- clusterFUN(nn.g)$membership
}
factor(output)
}
#' @importFrom igraph cluster_walktrap
.clusterKNNGraph <- function(x, ..., clusterFUN=cluster_walktrap, subset.row=NULL, transposed=FALSE,
use.kmeans=FALSE, kmeans.centers=NULL, kmeans.args=list(), full.stats=FALSE)
{
.Deprecated(msg="'clusterKNNGraph' is deprecated.\nUse 'bluster::clusterRows' with 'NNGraphParam' or 'TwoStepParam' instead.")
.clusterXNNGraph(x=x, ..., clusterFUN=clusterFUN, subset.row=subset.row, transposed=transposed,
use.kmeans=use.kmeans, kmeans.centers=kmeans.centers, kmeans.args=kmeans.args,
full.stats=full.stats, graphFUN=buildKNNGraph)
}
#' @importFrom igraph cluster_walktrap
.clusterSNNGraph <- function(x, ..., clusterFUN=cluster_walktrap, subset.row=NULL, transposed=FALSE,
use.kmeans=FALSE, kmeans.centers=NULL, kmeans.args=list(), full.stats=FALSE)
{
.Deprecated(msg="'clusterKNNGraph' is deprecated.\nUse 'bluster::clusterRows' with 'NNGraphParam' or 'TwoStepParam' instead.")
.clusterXNNGraph(x=x, ..., clusterFUN=clusterFUN, subset.row=subset.row, transposed=transposed,
use.kmeans=use.kmeans, kmeans.centers=kmeans.centers, kmeans.args=kmeans.args,
full.stats=full.stats, graphFUN=buildSNNGraph)
}
#########################
# S4 method definitions #
#########################
#' @export
#' @rdname clusterSNNGraph
setGeneric("clusterSNNGraph", function(x, ...) standardGeneric("clusterSNNGraph"))
#' @export
#' @rdname clusterSNNGraph
setMethod("clusterSNNGraph", "ANY", .clusterSNNGraph)
#' @export
#' @rdname clusterSNNGraph
#' @importFrom SummarizedExperiment assay
setMethod("clusterSNNGraph", "SummarizedExperiment", function(x, ..., assay.type="logcounts") {
.clusterSNNGraph(assay(x, assay.type), ..., transposed=FALSE)
})
#' @export
#' @rdname clusterSNNGraph
#' @importFrom SummarizedExperiment assay
#' @importFrom SingleCellExperiment reducedDim
setMethod("clusterSNNGraph", "SingleCellExperiment", function(x, ..., use.dimred=NULL) {
if (!is.null(use.dimred)) {
.clusterSNNGraph(reducedDim(x, use.dimred), ..., d=NA, transposed=TRUE)
} else {
callNextMethod(x=x, ...)
}
})
#' @export
#' @rdname clusterSNNGraph
setGeneric("clusterKNNGraph", function(x, ...) standardGeneric("clusterKNNGraph"))
#' @export
#' @rdname clusterSNNGraph
setMethod("clusterKNNGraph", "ANY", .clusterKNNGraph)
#' @export
#' @rdname clusterSNNGraph
#' @importFrom SummarizedExperiment assay
setMethod("clusterKNNGraph", "SummarizedExperiment", function(x, ..., assay.type="logcounts") {
.clusterKNNGraph(assay(x, assay.type), ..., transposed=FALSE)
})
#' @export
#' @rdname clusterSNNGraph
#' @importFrom SummarizedExperiment assay
#' @importFrom SingleCellExperiment reducedDim
setMethod("clusterKNNGraph", "SingleCellExperiment", function(x, ..., use.dimred=NULL) {
if (!is.null(use.dimred)) {
.clusterKNNGraph(reducedDim(x, use.dimred), ..., d=NA, transposed=TRUE)
} else {
callNextMethod(x=x, ...)
}
})
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