Nothing
R/slingshot.R
In slingshot: Tools for ordering single-cell sequencing
#' @rdname slingshot
#'
#' @description Given a reduced-dimensional data matrix \code{n} by \code{p} and
#' a vector of cluster labels (or matrix of soft cluster assignments,
#' potentially including a \code{-1} label for "unclustered"), this function
#' performs lineage inference using a cluster-based minimum spanning tree and
#' constructing simultaneous principal curves for branching paths through the
#' tree.
#'
#' @description This wrapper function performs lineage inference in two steps:
#' (1) identify lineage structure with a cluster-based minimum spanning tree
#' with the \code{\link{getLineages}} function and (2) construct smooth
#' representations of each lineage using simultaneous principal curves from
#' the function \code{\link{getCurves}}.
#'
#' @param data a data object containing the matrix of coordinates to be used for
#' lineage inference. Supported types include \code{matrix},
#' \code{\link{SingleCellExperiment}}, and \code{\link{SlingshotDataSet}}.
#' @param clusterLabels character, a vector of length \code{n} denoting cluster
#' labels, optionally including \code{-1}'s for "unclustered." If
#' \code{reducedDim} is a \code{SlingshotDataSet}, cluster labels will be
#' taken from it.
#' @param reducedDim (optional) identifier to be used if \code{reducedDim(data)}
#' contains multiple elements. Otherwise, the first element will be used by
#' default.
#' @param start.clus (optional) character, indicates the cluster(s) of origin.
#' Lineages will be represented by paths coming out of this cluster.
#' @param end.clus (optional) character, indicates the cluster(s) which will be
#' forced leaf nodes. This introduces a constraint on the MST algorithm.
#' @param dist.fun (optional) function, method for calculating distances between
#' clusters. Must take two matrices as input, corresponding to subsets of
#' \code{reducedDim}. If the minimum cluster size is larger than the number
#' dimensions, the default is to use the joint covariance matrix to find
#' squared distance between cluster centers. If not, the default is to use the
#' diagonal of the joint covariance matrix.
#' @param omega (optional) numeric, this granularity parameter determines the
#' distance between every real cluster and the artificial cluster,
#' \code{.OMEGA}. In practice, this makes \code{omega} the maximum allowable
#' distance between two connected clusters. By default, \code{omega = Inf}. If
#' \code{omega = TRUE}, the maximum edge length will be set to the median edge
#' length of the unsupervised MST times a scaling factor (\code{omega_scale},
#' default \code{= 3}). This value is provided as a potentially useful rule of
#' thumb for datasets with outlying clusters or multiple, distinct
#' trajectories, but it is not otherwise recommended.
#' @param omega_scale (optional) numeric, scaling factor to use when \code{omega
#' = TRUE}. The maximum edge length will be set to the median edge length of
#' the unsupervised MST times \code{omega_scale} (default \code{= 3}).
#' @param lineages list generated by \code{\link{getLineages}}, denotes lineages
#' as ordered sets of clusters and contains the \code{K x K} connectivity
#' matrix constructed on the clusters by \code{\link{getLineages}}.
#' @param thresh numeric, determines the convergence criterion. Percent change
#' in the total distance from cells to their projections along curves must be
#' less than \code{thresh}. Default is \code{0.001}, similar to
#' \code{\link[princurve]{principal_curve}}.
#' @param maxit numeric, maximum number of iterations, see
#' \code{\link[princurve]{principal_curve}}.
#' @param stretch numeric factor by which curves can be extrapolated beyond
#' endpoints. Default is \code{2}, see
#' \code{\link[princurve]{principal_curve}}.
#' @param approx_points logical or numeric, whether curves should be
#' approximated by a fixed number of points. If \code{FALSE}, no approximation
#' will be performed and curves will contain as many points as the input data.
#' If numeric, curves will be approximated by this number of points;
#' preferably about 100 (see \code{\link[princurve]{principal_curve}}).
#' @param smoother, choice of scatter plot smoother. Same as
#' \code{\link[princurve]{principal_curve}}, but \code{"lowess"} option is
#' replaced with \code{"loess"} for additional flexibility.
#' @param shrink logical or numeric between 0 and 1, determines whether and how
#' much to shrink branching lineages toward their average prior to the split.
#' @param extend character, how to handle root and leaf clusters of lineages
#' when constructing the initial, piece-wise linear curve. Accepted values are
#' \code{'y'} (default), \code{'n'}, and \code{'pc1'}. See 'Details' for more.
#' @param reweight logical, whether to allow cells shared between lineages to be
#' reweighted during curve-fitting. If \code{TRUE}, cells shared between
#' lineages will be iteratively reweighted based on the quantiles of their
#' projection distances to each curve. See 'Details' for more.
#' @param reassign logical, whether to reassign cells to lineages at each
#' iteration. If \code{TRUE}, cells will be added to a lineage when their
#' projection distance to the curve is less than the median distance for all
#' cells currently assigned to the lineage. Additionally, shared cells will be
#' removed from a lineage if their projection distance to the curve is above
#' the 90th percentile and their weight along the curve is less than
#' \code{0.1}.
#' @param shrink.method character denoting how to determine the appropriate
#' amount of shrinkage for a branching lineage. Accepted values are the same
#' as for \code{kernel} in \code{\link{density}} (default is \code{"cosine"}),
#' as well as \code{"tricube"} and \code{"density"}. See 'Details' for more.
#' @param allow.breaks logical, determines whether curves that branch very close
#' to the origin should be allowed to have different starting points.
#' @param ... Additional parameters to pass to scatter plot smoothing function,
#' \code{smoother}.
#'
#' @details The \code{connectivity} matrix is learned by fitting a (possibly
#' constrained) minimum-spanning tree on the clusters and the artificial
#' cluster, \code{.OMEGA}, which is a fixed distance away from every real
#' cluster. This effectively limits the maximum branch length in the MST to
#' the chosen distance, meaning that the output may contain multiple trees.
#'
#' @details Once the \code{connectivity} is known, lineages are identified in
#' any tree with at least two clusters. For a given tree, if there is an
#' annotated starting cluster, every possible path out of a starting cluster
#' and ending in a leaf that isn't another starting cluster will be returned.
#' If no starting cluster is annotated, every leaf will be considered as a
#' potential starting cluster and whichever configuration produces the longest
#' average lineage length (in terms of number of clusters included) will be
#' returned.
#'
#' @details When there is only a single lineage, the curve-fitting algorithm is
#' nearly identical to that of \code{\link[princurve]{principal_curve}}. When
#' there are multiple lineages and \code{shrink == TRUE}, an additional step
#' is added to the iterative procedure, forcing curves to be similar in the
#' neighborhood of shared points (ie., before they branch).
#'
#' @details The \code{extend} argument determines how to construct the
#' piece-wise linear curve used to initiate the recursive algorithm. The
#' initial curve is always based on the lines between cluster centers and if
#' \code{extend = 'n'}, this curve will terminate at the center of the
#' endpoint clusters. Setting \code{extend = 'y'} will allow the first and
#' last segments to extend beyond the cluster center to the orthogonal
#' projection of the furthest point. Setting \code{extend = 'pc1'} is similar
#' to \code{'y'}, but uses the first principal component of the cluster to
#' determine the direction of the curve beyond the cluster center. These
#' options typically have little to no impact on the final curve, but can
#' occasionally help with stability issues.
#'
#' @details When \code{shink == TRUE}, we compute a shrinkage curve,
#' \eqn{w_l(t)}, for each lineage, a non-increasing function of pseudotime
#' that determines how much that lineage should be shrunk toward a shared
#' average curve. We set \eqn{w_l(0) = 1}, so that the curves will perfectly
#' overlap the average curve at pseudotime \code{0}. The weighting curve
#' decreases from \code{1} to \code{0} over the non-outlying pseudotime values
#' of shared cells (where outliers are defined by the \code{1.5*IQR} rule).
#' The exact shape of the curve in this region is controlled by
#' \code{shrink.method}, and can follow the shape of any standard kernel
#' function's cumulative density curve (or more precisely, survival curve,
#' since we require a decreasing function). Different choices of
#' \code{shrink.method} seem to have little impact on the final curves, in
#' most cases.
#'
#' @details When \code{reweight = TRUE}, weights for shared cells are based on
#' the quantiles of their projection distances onto each curve. The distances
#' are ranked and converted into quantiles between \code{0} and \code{1},
#' which are then transformed by \code{1 - q^2}. Each cell's weight along a
#' given lineage is the ratio of this value to the maximum value for this cell
#' across all lineages.
#'
#' @references Hastie, T., and Stuetzle, W. (1989). "Principal Curves."
#' \emph{Journal of the American Statistical Association}, 84:502--516.
#'
#' @return An object of class \code{\link{SlingshotDataSet}} containing the
#' arguments provided to \code{slingshot} as well as the following output:
#' \itemize{ \item{\code{lineages}}{ a list of \code{L} items, where \code{L}
#' is the number of lineages identified. Each lineage is represented by a
#' character vector with the names of the clusters included in that lineage,
#' in order.} \item{\code{connectivity}}{ the inferred cluster connectivity
#' matrix.} \item{\code{slingParams}}{Additional parameters used for lineage
#' inference or fitting simultaneous principal curves. This may include the
#' elements \code{start.given} and \code{end.given}, logical values indicating
#' whether the starting and ending clusters were specified a priori.
#' Additionally, this will always include \code{dist}, the pairwise cluster
#' distance matrix.} \item{curves}{A list of
#' \code{\link[princurve]{principal_curve}} objects.}}
#'
#' @examples
#' data("slingshotExample")
#' rd <- slingshotExample$rd
#' cl <- slingshotExample$cl
#' sds <- slingshot(rd, cl, start.clus = '1')
#'
#' plot(rd, col = cl, asp = 1)
#' lines(sds, lwd = 3)
#'
#' @export
#'
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "character"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
sds <- getLineages(data, clusterLabels, reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale)
sds <- getCurves(sds,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
approx_points = approx_points,
stretch = stretch, smoother = smoother,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...)
return(sds)
}
)
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "matrix"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
sds <- getLineages(data, clusterLabels, reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale)
sds <- getCurves(sds,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
approx_points = approx_points,
stretch = stretch, smoother = smoother,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...)
return(sds)
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "SlingshotDataSet",
clusterLabels = "ANY"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
return(slingshot(data = reducedDim(data),
clusterLabels = .getClusterLabels(data),
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "data.frame",
clusterLabels = "ANY"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
RD <- as.matrix(data)
rownames(RD) <- rownames(data)
return(slingshot(data = RD,
clusterLabels = clusterLabels,
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "numeric"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
return(slingshot(data = data,
clusterLabels = as.character(clusterLabels),
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "factor"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
return(slingshot(data = data,
clusterLabels = as.character(clusterLabels),
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "ANY"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
if(missing(clusterLabels)){
message('No cluster labels provided.',
' Continuing with one cluster.')
clusterLabels <- rep('1', nrow(data))
}
if(! any(c(length(clusterLabels), nrow(clusterLabels)) ==
nrow(data))){
stop("clusterLabels must have length or number of rows equal',
'to nrow(data).")
}
return(slingshot(data = data, clusterLabels = clusterLabels,
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @importFrom SingleCellExperiment colData
#' @importFrom SummarizedExperiment colData<-
#' @importFrom SingleCellExperiment reducedDims
#' @importFrom SingleCellExperiment reducedDims<-
#' @export
setMethod(f = "slingshot",
signature = signature(data = "ClusterExperiment"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
# SETUP
# determine the cluster labels and reducedDim matrix
if(is.null(reducedDim)){
if(length(reducedDims(data))==0){
stop('No dimensionality reduction found.')
}else{
message('Dimensionality reduction not explicitly ',
'chosen. Continuing with ',
names(reducedDims(data))[1])
rd <- reducedDims(data)[[1]]
}
}
if(length(reducedDim)==1){
if(reducedDim %in% names(reducedDims(data))){
rd <- reducedDims(data)[[as.character(reducedDim)]]
}else{
stop(reducedDim,' not found in reducedDims(data).')
}
}else{
if(!is.null(dim(reducedDim))){
rd <- reducedDim
reducedDims(data)$slingReducedDim <- reducedDim
}
}
if(!is.matrix(rd)) {
stop("Slingshot currently works only with base matrices.")
}
if(missing(clusterLabels)){
cl <- clusterExperiment::primaryClusterNamed(data)
if(is.null(cl)){
message('No primary clustering found. Continuing ',
'with one cluster.')
cl <- rep('1', nrow(rd))
}
colData(data)$slingClusters <- cl
}else{
if(length(clusterLabels)==1){
if(clusterLabels %in% colnames(colData(data))){
cl <- colData(data)[[as.character(clusterLabels)]]
}else{
if(clusterLabels %in% colnames(
clusterExperiment::clusterMatrix(data))){
cl <- clusterExperiment::clusterMatrixNamed(
data)[,as.character(clusterLabels)]
}else{
stop(clusterLabels,' not found in colData(data)',
' or clusterMatrix(data).')
}
}
}
if(length(clusterLabels)>1){
if(!is.null(dim(clusterLabels)) &&
length(dim(clusterLabels)) > 1 &&
all(dim(clusterLabels) > 1)){
cl <- as.matrix(clusterLabels)
colnames(cl) <- paste0('sling_c',seq_len(ncol(cl)))
colData(data) <- cbind(colData(data), cl)
}else{
cl <- as.character(clusterLabels)
colData(data)$slingClusters <- cl
}
}
}
# run slingshot
sds <- slingshot(data = rd, clusterLabels = cl,
reducedDim = NULL,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...)
# combine slingshot output with SCE
sce <- data
sce@int_metadata$slingshot <- sds
pst <- slingPseudotime(sds)
colnames(pst) <- paste0('slingPseudotime_',seq_len(ncol(pst)))
colData(sce) <- cbind(colData(sce), pst)
return(sce)
})
#' @rdname slingshot
#' @importFrom SingleCellExperiment colData
#' @importFrom SummarizedExperiment colData<-
#' @importFrom SingleCellExperiment reducedDims
#' @importFrom SingleCellExperiment reducedDims<-
#' @export
setMethod(f = "slingshot",
signature = signature(data = "SingleCellExperiment"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
# SETUP
# determine the cluster labels and reducedDim matrix
if(is.null(reducedDim)){
if(length(reducedDims(data))==0){
stop('No dimensionality reduction found.')
}else{
message('Dimensionality reduction not explicitly ',
'chosen. Continuing with ',
names(reducedDims(data))[1])
rd <- reducedDims(data)[[1]]
}
}
if(length(reducedDim)==1){
if(reducedDim %in% names(reducedDims(data))){
rd <- reducedDims(data)[[as.character(reducedDim)]]
}else{
stop(reducedDim,' not found in reducedDims(data).')
}
}else{
if(!is.null(dim(reducedDim))){
rd <- reducedDim
reducedDims(data)$slingReducedDim <- reducedDim
}
}
if(!is.matrix(rd)) {
stop("Slingshot currently works only with base matrices.")
}
if(missing(clusterLabels)){
message('No cluster labels provided. Continuing with ',
'one cluster.')
cl <- rep('1', nrow(rd))
colData(data)$slingClusters <- cl
}else{
if(length(clusterLabels)==1){
if(clusterLabels %in% colnames(colData(data))){
cl <- colData(data)[[as.character(clusterLabels)]]
}else{
stop(clusterLabels,' not found in colData(data).')
}
}
if(length(clusterLabels)>1){
if(!is.null(dim(clusterLabels)) &&
length(dim(clusterLabels)) > 1 &&
all(dim(clusterLabels) > 1)){
cl <- as.matrix(clusterLabels)
colnames(cl) <- paste0('sling_c',seq_len(ncol(cl)))
colData(data) <- cbind(colData(data), cl)
}else{
cl <- as.character(clusterLabels)
colData(data)$slingClusters <- cl
}
}
}
# run slingshot
sds <- slingshot(data = rd, clusterLabels = cl,
reducedDim = NULL,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...)
# combine slingshot output with SCE
sce <- data
sce@int_metadata$slingshot <- sds
pst <- slingPseudotime(sds)
colnames(pst) <- paste0('slingPseudotime_',seq_len(ncol(pst)))
colData(sce) <- cbind(colData(sce), pst)
return(sce)
})
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#' @rdname slingshot
#'
#' @description Given a reduced-dimensional data matrix \code{n} by \code{p} and
#' a vector of cluster labels (or matrix of soft cluster assignments,
#' potentially including a \code{-1} label for "unclustered"), this function
#' performs lineage inference using a cluster-based minimum spanning tree and
#' constructing simultaneous principal curves for branching paths through the
#' tree.
#'
#' @description This wrapper function performs lineage inference in two steps:
#' (1) identify lineage structure with a cluster-based minimum spanning tree
#' with the \code{\link{getLineages}} function and (2) construct smooth
#' representations of each lineage using simultaneous principal curves from
#' the function \code{\link{getCurves}}.
#'
#' @param data a data object containing the matrix of coordinates to be used for
#' lineage inference. Supported types include \code{matrix},
#' \code{\link{SingleCellExperiment}}, and \code{\link{SlingshotDataSet}}.
#' @param clusterLabels character, a vector of length \code{n} denoting cluster
#' labels, optionally including \code{-1}'s for "unclustered." If
#' \code{reducedDim} is a \code{SlingshotDataSet}, cluster labels will be
#' taken from it.
#' @param reducedDim (optional) identifier to be used if \code{reducedDim(data)}
#' contains multiple elements. Otherwise, the first element will be used by
#' default.
#' @param start.clus (optional) character, indicates the cluster(s) of origin.
#' Lineages will be represented by paths coming out of this cluster.
#' @param end.clus (optional) character, indicates the cluster(s) which will be
#' forced leaf nodes. This introduces a constraint on the MST algorithm.
#' @param dist.fun (optional) function, method for calculating distances between
#' clusters. Must take two matrices as input, corresponding to subsets of
#' \code{reducedDim}. If the minimum cluster size is larger than the number
#' dimensions, the default is to use the joint covariance matrix to find
#' squared distance between cluster centers. If not, the default is to use the
#' diagonal of the joint covariance matrix.
#' @param omega (optional) numeric, this granularity parameter determines the
#' distance between every real cluster and the artificial cluster,
#' \code{.OMEGA}. In practice, this makes \code{omega} the maximum allowable
#' distance between two connected clusters. By default, \code{omega = Inf}. If
#' \code{omega = TRUE}, the maximum edge length will be set to the median edge
#' length of the unsupervised MST times a scaling factor (\code{omega_scale},
#' default \code{= 3}). This value is provided as a potentially useful rule of
#' thumb for datasets with outlying clusters or multiple, distinct
#' trajectories, but it is not otherwise recommended.
#' @param omega_scale (optional) numeric, scaling factor to use when \code{omega
#' = TRUE}. The maximum edge length will be set to the median edge length of
#' the unsupervised MST times \code{omega_scale} (default \code{= 3}).
#' @param lineages list generated by \code{\link{getLineages}}, denotes lineages
#' as ordered sets of clusters and contains the \code{K x K} connectivity
#' matrix constructed on the clusters by \code{\link{getLineages}}.
#' @param thresh numeric, determines the convergence criterion. Percent change
#' in the total distance from cells to their projections along curves must be
#' less than \code{thresh}. Default is \code{0.001}, similar to
#' \code{\link[princurve]{principal_curve}}.
#' @param maxit numeric, maximum number of iterations, see
#' \code{\link[princurve]{principal_curve}}.
#' @param stretch numeric factor by which curves can be extrapolated beyond
#' endpoints. Default is \code{2}, see
#' \code{\link[princurve]{principal_curve}}.
#' @param approx_points logical or numeric, whether curves should be
#' approximated by a fixed number of points. If \code{FALSE}, no approximation
#' will be performed and curves will contain as many points as the input data.
#' If numeric, curves will be approximated by this number of points;
#' preferably about 100 (see \code{\link[princurve]{principal_curve}}).
#' @param smoother, choice of scatter plot smoother. Same as
#' \code{\link[princurve]{principal_curve}}, but \code{"lowess"} option is
#' replaced with \code{"loess"} for additional flexibility.
#' @param shrink logical or numeric between 0 and 1, determines whether and how
#' much to shrink branching lineages toward their average prior to the split.
#' @param extend character, how to handle root and leaf clusters of lineages
#' when constructing the initial, piece-wise linear curve. Accepted values are
#' \code{'y'} (default), \code{'n'}, and \code{'pc1'}. See 'Details' for more.
#' @param reweight logical, whether to allow cells shared between lineages to be
#' reweighted during curve-fitting. If \code{TRUE}, cells shared between
#' lineages will be iteratively reweighted based on the quantiles of their
#' projection distances to each curve. See 'Details' for more.
#' @param reassign logical, whether to reassign cells to lineages at each
#' iteration. If \code{TRUE}, cells will be added to a lineage when their
#' projection distance to the curve is less than the median distance for all
#' cells currently assigned to the lineage. Additionally, shared cells will be
#' removed from a lineage if their projection distance to the curve is above
#' the 90th percentile and their weight along the curve is less than
#' \code{0.1}.
#' @param shrink.method character denoting how to determine the appropriate
#' amount of shrinkage for a branching lineage. Accepted values are the same
#' as for \code{kernel} in \code{\link{density}} (default is \code{"cosine"}),
#' as well as \code{"tricube"} and \code{"density"}. See 'Details' for more.
#' @param allow.breaks logical, determines whether curves that branch very close
#' to the origin should be allowed to have different starting points.
#' @param ... Additional parameters to pass to scatter plot smoothing function,
#' \code{smoother}.
#'
#' @details The \code{connectivity} matrix is learned by fitting a (possibly
#' constrained) minimum-spanning tree on the clusters and the artificial
#' cluster, \code{.OMEGA}, which is a fixed distance away from every real
#' cluster. This effectively limits the maximum branch length in the MST to
#' the chosen distance, meaning that the output may contain multiple trees.
#'
#' @details Once the \code{connectivity} is known, lineages are identified in
#' any tree with at least two clusters. For a given tree, if there is an
#' annotated starting cluster, every possible path out of a starting cluster
#' and ending in a leaf that isn't another starting cluster will be returned.
#' If no starting cluster is annotated, every leaf will be considered as a
#' potential starting cluster and whichever configuration produces the longest
#' average lineage length (in terms of number of clusters included) will be
#' returned.
#'
#' @details When there is only a single lineage, the curve-fitting algorithm is
#' nearly identical to that of \code{\link[princurve]{principal_curve}}. When
#' there are multiple lineages and \code{shrink == TRUE}, an additional step
#' is added to the iterative procedure, forcing curves to be similar in the
#' neighborhood of shared points (ie., before they branch).
#'
#' @details The \code{extend} argument determines how to construct the
#' piece-wise linear curve used to initiate the recursive algorithm. The
#' initial curve is always based on the lines between cluster centers and if
#' \code{extend = 'n'}, this curve will terminate at the center of the
#' endpoint clusters. Setting \code{extend = 'y'} will allow the first and
#' last segments to extend beyond the cluster center to the orthogonal
#' projection of the furthest point. Setting \code{extend = 'pc1'} is similar
#' to \code{'y'}, but uses the first principal component of the cluster to
#' determine the direction of the curve beyond the cluster center. These
#' options typically have little to no impact on the final curve, but can
#' occasionally help with stability issues.
#'
#' @details When \code{shink == TRUE}, we compute a shrinkage curve,
#' \eqn{w_l(t)}, for each lineage, a non-increasing function of pseudotime
#' that determines how much that lineage should be shrunk toward a shared
#' average curve. We set \eqn{w_l(0) = 1}, so that the curves will perfectly
#' overlap the average curve at pseudotime \code{0}. The weighting curve
#' decreases from \code{1} to \code{0} over the non-outlying pseudotime values
#' of shared cells (where outliers are defined by the \code{1.5*IQR} rule).
#' The exact shape of the curve in this region is controlled by
#' \code{shrink.method}, and can follow the shape of any standard kernel
#' function's cumulative density curve (or more precisely, survival curve,
#' since we require a decreasing function). Different choices of
#' \code{shrink.method} seem to have little impact on the final curves, in
#' most cases.
#'
#' @details When \code{reweight = TRUE}, weights for shared cells are based on
#' the quantiles of their projection distances onto each curve. The distances
#' are ranked and converted into quantiles between \code{0} and \code{1},
#' which are then transformed by \code{1 - q^2}. Each cell's weight along a
#' given lineage is the ratio of this value to the maximum value for this cell
#' across all lineages.
#'
#' @references Hastie, T., and Stuetzle, W. (1989). "Principal Curves."
#' \emph{Journal of the American Statistical Association}, 84:502--516.
#'
#' @return An object of class \code{\link{SlingshotDataSet}} containing the
#' arguments provided to \code{slingshot} as well as the following output:
#' \itemize{ \item{\code{lineages}}{ a list of \code{L} items, where \code{L}
#' is the number of lineages identified. Each lineage is represented by a
#' character vector with the names of the clusters included in that lineage,
#' in order.} \item{\code{connectivity}}{ the inferred cluster connectivity
#' matrix.} \item{\code{slingParams}}{Additional parameters used for lineage
#' inference or fitting simultaneous principal curves. This may include the
#' elements \code{start.given} and \code{end.given}, logical values indicating
#' whether the starting and ending clusters were specified a priori.
#' Additionally, this will always include \code{dist}, the pairwise cluster
#' distance matrix.} \item{curves}{A list of
#' \code{\link[princurve]{principal_curve}} objects.}}
#'
#' @examples
#' data("slingshotExample")
#' rd <- slingshotExample$rd
#' cl <- slingshotExample$cl
#' sds <- slingshot(rd, cl, start.clus = '1')
#'
#' plot(rd, col = cl, asp = 1)
#' lines(sds, lwd = 3)
#'
#' @export
#'
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "character"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
sds <- getLineages(data, clusterLabels, reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale)
sds <- getCurves(sds,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
approx_points = approx_points,
stretch = stretch, smoother = smoother,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...)
return(sds)
}
)
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "matrix"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
sds <- getLineages(data, clusterLabels, reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale)
sds <- getCurves(sds,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
approx_points = approx_points,
stretch = stretch, smoother = smoother,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...)
return(sds)
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "SlingshotDataSet",
clusterLabels = "ANY"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
return(slingshot(data = reducedDim(data),
clusterLabels = .getClusterLabels(data),
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "data.frame",
clusterLabels = "ANY"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
RD <- as.matrix(data)
rownames(RD) <- rownames(data)
return(slingshot(data = RD,
clusterLabels = clusterLabels,
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "numeric"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
return(slingshot(data = data,
clusterLabels = as.character(clusterLabels),
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "factor"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
return(slingshot(data = data,
clusterLabels = as.character(clusterLabels),
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @export
setMethod(f = "slingshot",
signature = signature(data = "matrix",
clusterLabels = "ANY"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
if(missing(clusterLabels)){
message('No cluster labels provided.',
' Continuing with one cluster.')
clusterLabels <- rep('1', nrow(data))
}
if(! any(c(length(clusterLabels), nrow(clusterLabels)) ==
nrow(data))){
stop("clusterLabels must have length or number of rows equal',
'to nrow(data).")
}
return(slingshot(data = data, clusterLabels = clusterLabels,
reducedDim = reducedDim,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...))
})
#' @rdname slingshot
#' @importFrom SingleCellExperiment colData
#' @importFrom SummarizedExperiment colData<-
#' @importFrom SingleCellExperiment reducedDims
#' @importFrom SingleCellExperiment reducedDims<-
#' @export
setMethod(f = "slingshot",
signature = signature(data = "ClusterExperiment"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
# SETUP
# determine the cluster labels and reducedDim matrix
if(is.null(reducedDim)){
if(length(reducedDims(data))==0){
stop('No dimensionality reduction found.')
}else{
message('Dimensionality reduction not explicitly ',
'chosen. Continuing with ',
names(reducedDims(data))[1])
rd <- reducedDims(data)[[1]]
}
}
if(length(reducedDim)==1){
if(reducedDim %in% names(reducedDims(data))){
rd <- reducedDims(data)[[as.character(reducedDim)]]
}else{
stop(reducedDim,' not found in reducedDims(data).')
}
}else{
if(!is.null(dim(reducedDim))){
rd <- reducedDim
reducedDims(data)$slingReducedDim <- reducedDim
}
}
if(!is.matrix(rd)) {
stop("Slingshot currently works only with base matrices.")
}
if(missing(clusterLabels)){
cl <- clusterExperiment::primaryClusterNamed(data)
if(is.null(cl)){
message('No primary clustering found. Continuing ',
'with one cluster.')
cl <- rep('1', nrow(rd))
}
colData(data)$slingClusters <- cl
}else{
if(length(clusterLabels)==1){
if(clusterLabels %in% colnames(colData(data))){
cl <- colData(data)[[as.character(clusterLabels)]]
}else{
if(clusterLabels %in% colnames(
clusterExperiment::clusterMatrix(data))){
cl <- clusterExperiment::clusterMatrixNamed(
data)[,as.character(clusterLabels)]
}else{
stop(clusterLabels,' not found in colData(data)',
' or clusterMatrix(data).')
}
}
}
if(length(clusterLabels)>1){
if(!is.null(dim(clusterLabels)) &&
length(dim(clusterLabels)) > 1 &&
all(dim(clusterLabels) > 1)){
cl <- as.matrix(clusterLabels)
colnames(cl) <- paste0('sling_c',seq_len(ncol(cl)))
colData(data) <- cbind(colData(data), cl)
}else{
cl <- as.character(clusterLabels)
colData(data)$slingClusters <- cl
}
}
}
# run slingshot
sds <- slingshot(data = rd, clusterLabels = cl,
reducedDim = NULL,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...)
# combine slingshot output with SCE
sce <- data
sce@int_metadata$slingshot <- sds
pst <- slingPseudotime(sds)
colnames(pst) <- paste0('slingPseudotime_',seq_len(ncol(pst)))
colData(sce) <- cbind(colData(sce), pst)
return(sce)
})
#' @rdname slingshot
#' @importFrom SingleCellExperiment colData
#' @importFrom SummarizedExperiment colData<-
#' @importFrom SingleCellExperiment reducedDims
#' @importFrom SingleCellExperiment reducedDims<-
#' @export
setMethod(f = "slingshot",
signature = signature(data = "SingleCellExperiment"),
definition = function(data, clusterLabels,
reducedDim = NULL,
start.clus = NULL, end.clus = NULL,
dist.fun = NULL, omega = NULL, omega_scale = 3,
lineages = list(),
shrink = TRUE,
extend = 'y',
reweight = TRUE,
reassign = TRUE,
thresh = 0.001, maxit = 15, stretch = 2,
approx_points = FALSE,
smoother = 'smooth.spline',
shrink.method = 'cosine',
allow.breaks = TRUE, ...){
# SETUP
# determine the cluster labels and reducedDim matrix
if(is.null(reducedDim)){
if(length(reducedDims(data))==0){
stop('No dimensionality reduction found.')
}else{
message('Dimensionality reduction not explicitly ',
'chosen. Continuing with ',
names(reducedDims(data))[1])
rd <- reducedDims(data)[[1]]
}
}
if(length(reducedDim)==1){
if(reducedDim %in% names(reducedDims(data))){
rd <- reducedDims(data)[[as.character(reducedDim)]]
}else{
stop(reducedDim,' not found in reducedDims(data).')
}
}else{
if(!is.null(dim(reducedDim))){
rd <- reducedDim
reducedDims(data)$slingReducedDim <- reducedDim
}
}
if(!is.matrix(rd)) {
stop("Slingshot currently works only with base matrices.")
}
if(missing(clusterLabels)){
message('No cluster labels provided. Continuing with ',
'one cluster.')
cl <- rep('1', nrow(rd))
colData(data)$slingClusters <- cl
}else{
if(length(clusterLabels)==1){
if(clusterLabels %in% colnames(colData(data))){
cl <- colData(data)[[as.character(clusterLabels)]]
}else{
stop(clusterLabels,' not found in colData(data).')
}
}
if(length(clusterLabels)>1){
if(!is.null(dim(clusterLabels)) &&
length(dim(clusterLabels)) > 1 &&
all(dim(clusterLabels) > 1)){
cl <- as.matrix(clusterLabels)
colnames(cl) <- paste0('sling_c',seq_len(ncol(cl)))
colData(data) <- cbind(colData(data), cl)
}else{
cl <- as.character(clusterLabels)
colData(data)$slingClusters <- cl
}
}
}
# run slingshot
sds <- slingshot(data = rd, clusterLabels = cl,
reducedDim = NULL,
start.clus = start.clus, end.clus = end.clus,
dist.fun = dist.fun, omega = omega,
omega_scale = omega_scale,
shrink = shrink, extend = extend,
reweight = reweight, reassign = reassign,
thresh = thresh, maxit = maxit,
stretch = stretch, smoother = smoother,
approx_points = approx_points,
shrink.method = shrink.method,
allow.breaks = allow.breaks, ...)
# combine slingshot output with SCE
sce <- data
sce@int_metadata$slingshot <- sds
pst <- slingPseudotime(sds)
colnames(pst) <- paste0('slingPseudotime_',seq_len(ncol(pst)))
colData(sce) <- cbind(colData(sce), pst)
return(sce)
})
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