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integration.R
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#' @include generics.R
#'
NULL
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Functions
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Find integration anchors
#'
#' Find a set of anchors between a list of \code{\link{Seurat}} objects.
#' These anchors can later be used to integrate the objects using the
#' \code{\link{IntegrateData}} function.
#'
#' The main steps of this procedure are outlined below. For a more detailed
#' description of the methodology, please see Stuart, Butler, et al Cell 2019:
#' \doi{10.1016/j.cell.2019.05.031}; \doi{10.1101/460147}
#'
#' First, determine anchor.features if not explicitly specified using
#' \code{\link{SelectIntegrationFeatures}}. Then for all pairwise combinations
#' of reference and query datasets:
#'
#' \itemize{
#' \item{Perform dimensional reduction on the dataset pair as specified via
#' the \code{reduction} parameter. If \code{l2.norm} is set to \code{TRUE},
#' perform L2 normalization of the embedding vectors.}
#' \item{Identify anchors - pairs of cells from each dataset
#' that are contained within each other's neighborhoods (also known as mutual
#' nearest neighbors).}
#' \item{Filter low confidence anchors to ensure anchors in the low dimension
#' space are in broad agreement with the high dimensional measurements. This
#' is done by looking at the neighbors of each query cell in the reference
#' dataset using \code{max.features} to define this space. If the reference
#' cell isn't found within the first \code{k.filter} neighbors, remove the
#' anchor.}
#' \item{Assign each remaining anchor a score. For each anchor cell, determine
#' the nearest \code{k.score} anchors within its own dataset and within its
#' pair's dataset. Based on these neighborhoods, construct an overall neighbor
#' graph and then compute the shared neighbor overlap between anchor and query
#' cells (analogous to an SNN graph). We use the 0.01 and 0.90 quantiles on
#' these scores to dampen outlier effects and rescale to range between 0-1.}
#' }
#'
#' @param object.list A list of \code{\link{Seurat}} objects between which to
#' find anchors for downstream integration.
#' @param assay A vector of assay names specifying which assay to use when
#' constructing anchors. If NULL, the current default assay for each object is
#' used.
#' @param reference A vector specifying the object/s to be used as a reference
#' during integration. If NULL (default), all pairwise anchors are found (no
#' reference/s). If not NULL, the corresponding objects in \code{object.list}
#' will be used as references. When using a set of specified references, anchors
#' are first found between each query and each reference. The references are
#' then integrated through pairwise integration. Each query is then mapped to
#' the integrated reference.
#' @param anchor.features Can be either:
#' \itemize{
#' \item{A numeric value. This will call \code{\link{SelectIntegrationFeatures}}
#' to select the provided number of features to be used in anchor finding}
#' \item{A vector of features to be used as input to the anchor finding process}
#' }
#' @param scale Whether or not to scale the features provided. Only set to FALSE
#' if you have previously scaled the features you want to use for each object in
#' the object.list
#' @param normalization.method Name of normalization method used: LogNormalize
#' or SCT
#' @param sct.clip.range Numeric of length two specifying the min and max values
#' the Pearson residual will be clipped to
#' @param reduction Dimensional reduction to perform when finding anchors. Can
#' be one of:
#' \itemize{
#' \item{cca: Canonical correlation analysis}
#' \item{rpca: Reciprocal PCA}
#' }
#' @param l2.norm Perform L2 normalization on the CCA cell embeddings after
#' dimensional reduction
#' @param dims Which dimensions to use from the CCA to specify the neighbor
#' search space
#' @param k.anchor How many neighbors (k) to use when picking anchors
#' @param k.filter How many neighbors (k) to use when filtering anchors
#' @param k.score How many neighbors (k) to use when scoring anchors
#' @param max.features The maximum number of features to use when specifying the
#' neighborhood search space in the anchor filtering
#' @param nn.method Method for nearest neighbor finding. Options include: rann,
#' annoy
#' @param n.trees More trees gives higher precision when using annoy approximate
#' nearest neighbor search
#' @param eps Error bound on the neighbor finding algorithm (from RANN)
#' @param verbose Print progress bars and output
#'
#' @return Returns an \code{\link{AnchorSet}} object that can be used as input to
#' \code{\link{IntegrateData}}.
#'
#' @references Stuart T, Butler A, et al. Comprehensive Integration of
#' Single-Cell Data. Cell. 2019;177:1888-1902 \doi{10.1016/j.cell.2019.05.031}
#'
#' @importFrom pbapply pblapply
#' @importFrom future.apply future_lapply
#' @importFrom future nbrOfWorkers
#'
#' @export
#' @concept integration
#'
#' @examples
#' \dontrun{
#' # to install the SeuratData package see https://github.com/satijalab/seurat-data
#' library(SeuratData)
#' data("panc8")
#'
#' # panc8 is a merged Seurat object containing 8 separate pancreas datasets
#' # split the object by dataset
#' pancreas.list <- SplitObject(panc8, split.by = "tech")
#'
#' # perform standard preprocessing on each object
#' for (i in 1:length(pancreas.list)) {
#' pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE)
#' pancreas.list[[i]] <- FindVariableFeatures(
#' pancreas.list[[i]], selection.method = "vst",
#' nfeatures = 2000, verbose = FALSE
#' )
#' }
#'
#' # find anchors
#' anchors <- FindIntegrationAnchors(object.list = pancreas.list)
#'
#' # integrate data
#' integrated <- IntegrateData(anchorset = anchors)
#' }
#'
FindIntegrationAnchors <- function(
object.list = NULL,
assay = NULL,
reference = NULL,
anchor.features = 2000,
scale = TRUE,
normalization.method = c("LogNormalize", "SCT"),
sct.clip.range = NULL,
reduction = c("cca", "rpca"),
l2.norm = TRUE,
dims = 1:30,
k.anchor = 5,
k.filter = 200,
k.score = 30,
max.features = 200,
nn.method = "annoy",
n.trees = 50,
eps = 0,
verbose = TRUE
) {
normalization.method <- match.arg(arg = normalization.method)
reduction <- match.arg(arg = reduction)
if (reduction == "rpca") {
reduction <- "pca"
}
my.lapply <- ifelse(
test = verbose && nbrOfWorkers() == 1,
yes = pblapply,
no = future_lapply
)
object.ncells <- sapply(X = object.list, FUN = function(x) dim(x = x)[2])
if (any(object.ncells <= max(dims))) {
bad.obs <- which(x = object.ncells <= max(dims))
stop("Max dimension too large: objects ", paste(bad.obs, collapse = ", "),
" contain fewer than ", max(dims), " cells. \n Please specify a",
" maximum dimensions that is less than the number of cells in any ",
"object (", min(object.ncells), ").")
}
if (!is.null(x = assay)) {
if (length(x = assay) != length(x = object.list)) {
stop("If specifying the assay, please specify one assay per object in the object.list")
}
object.list <- sapply(
X = 1:length(x = object.list),
FUN = function(x) {
DefaultAssay(object = object.list[[x]]) <- assay[x]
return(object.list[[x]])
}
)
} else {
assay <- sapply(X = object.list, FUN = DefaultAssay)
}
object.list <- CheckDuplicateCellNames(object.list = object.list)
slot <- "data"
if (normalization.method == "SCT") {
slot <- "scale.data"
scale <- FALSE
if (is.numeric(x = anchor.features)) {
stop("Please specify the anchor.features to be used. The expected ",
"workflow for integratinge assays produced by SCTransform is ",
"SelectIntegrationFeatures -> PrepSCTIntegration -> ",
"FindIntegrationAnchors.")
}
sct.check <- sapply(
X = 1:length(x = object.list),
FUN = function(x) {
sct.cmd <- grep(
pattern = 'PrepSCTIntegration',
x = Command(object = object.list[[x]]),
value = TRUE
)
# check assay has gone through PrepSCTIntegration
if (!any(grepl(pattern = "PrepSCTIntegration", x = Command(object = object.list[[x]]))) ||
Command(object = object.list[[x]], command = sct.cmd, value = "assay") != assay[x]) {
stop("Object ", x, " assay - ", assay[x], " has not been processed ",
"by PrepSCTIntegration. Please run PrepSCTIntegration prior to ",
"FindIntegrationAnchors if using assays generated by SCTransform.", call. = FALSE)
}
# check that the correct features are being used
if (all(Command(object = object.list[[x]], command = sct.cmd, value = "anchor.features") != anchor.features)) {
stop("Object ", x, " assay - ", assay[x], " was processed using a ",
"different feature set than in PrepSCTIntegration. Please rerun ",
"PrepSCTIntegration with the same anchor.features for all objects in ",
"the object.list.", call. = FALSE)
}
}
)
}
if (is.numeric(x = anchor.features) && normalization.method != "SCT") {
if (verbose) {
message("Computing ", anchor.features, " integration features")
}
anchor.features <- SelectIntegrationFeatures(
object.list = object.list,
nfeatures = anchor.features,
assay = assay
)
}
if (scale) {
if (verbose) {
message("Scaling features for provided objects")
}
object.list <- my.lapply(
X = object.list,
FUN = function(object) {
ScaleData(object = object, features = anchor.features, verbose = FALSE)
}
)
}
nn.reduction <- reduction
# if using pca, only need to compute the internal neighborhood structure once
# for each dataset
internal.neighbors <- list()
if (nn.reduction == "pca") {
k.filter <- NA
if (verbose) {
message("Computing within dataset neighborhoods")
}
k.neighbor <- max(k.anchor, k.score)
internal.neighbors <- my.lapply(
X = 1:length(x = object.list),
FUN = function(x) {
NNHelper(
data = Embeddings(object = object.list[[x]][[nn.reduction]])[, dims],
k = k.neighbor + 1,
method = nn.method,
n.trees = n.trees,
eps = eps
)
}
)
}
# determine pairwise combinations
combinations <- expand.grid(1:length(x = object.list), 1:length(x = object.list))
combinations <- combinations[combinations$Var1 < combinations$Var2, , drop = FALSE]
# determine the proper offsets for indexing anchors
objects.ncell <- sapply(X = object.list, FUN = ncol)
offsets <- as.vector(x = cumsum(x = c(0, objects.ncell)))[1:length(x = object.list)]
if (is.null(x = reference)) {
# case for all pairwise, leave the combinations matrix the same
if (verbose) {
message("Finding all pairwise anchors")
}
} else {
reference <- unique(x = sort(x = reference))
if (max(reference) > length(x = object.list)) {
stop('Error: requested reference object ', max(reference), " but only ",
length(x = object.list), " objects provided")
}
# modify the combinations matrix to retain only R-R and R-Q comparisons
if (verbose) {
message("Finding anchors between all query and reference datasets")
ok.rows <- (combinations$Var1 %in% reference) | (combinations$Var2 %in% reference)
combinations <- combinations[ok.rows, ]
}
}
# determine all anchors
all.anchors <- my.lapply(
X = 1:nrow(x = combinations),
FUN = function(row) {
i <- combinations[row, 1]
j <- combinations[row, 2]
object.1 <- DietSeurat(
object = object.list[[i]],
assays = assay[i],
features = anchor.features,
counts = FALSE,
scale.data = TRUE,
dimreducs = reduction
)
object.2 <- DietSeurat(
object = object.list[[j]],
assays = assay[j],
features = anchor.features,
counts = FALSE,
scale.data = TRUE,
dimreducs = reduction
)
# suppress key duplication warning
suppressWarnings(object.1[["ToIntegrate"]] <- object.1[[assay[i]]])
DefaultAssay(object = object.1) <- "ToIntegrate"
if (reduction %in% Reductions(object = object.1)) {
slot(object = object.1[[reduction]], name = "assay.used") <- "ToIntegrate"
}
object.1 <- DietSeurat(object = object.1, assays = "ToIntegrate", scale.data = TRUE, dimreducs = reduction)
suppressWarnings(object.2[["ToIntegrate"]] <- object.2[[assay[j]]])
DefaultAssay(object = object.2) <- "ToIntegrate"
if (reduction %in% Reductions(object = object.2)) {
slot(object = object.2[[reduction]], name = "assay.used") <- "ToIntegrate"
}
object.2 <- DietSeurat(object = object.2, assays = "ToIntegrate", scale.data = TRUE, dimreducs = reduction)
object.pair <- switch(
EXPR = reduction,
'cca' = {
object.pair <- RunCCA(
object1 = object.1,
object2 = object.2,
assay1 = "ToIntegrate",
assay2 = "ToIntegrate",
features = anchor.features,
num.cc = max(dims),
renormalize = FALSE,
rescale = FALSE,
verbose = verbose
)
if (l2.norm){
object.pair <- L2Dim(object = object.pair, reduction = reduction)
reduction <- paste0(reduction, ".l2")
nn.reduction <- reduction
}
reduction.2 <- character()
object.pair
},
'pca' = {
common.features <- intersect(
x = rownames(x = Loadings(object = object.1[["pca"]])),
y = rownames(x = Loadings(object = object.2[["pca"]]))
)
common.features <- intersect(
x = common.features,
y = anchor.features
)
object.pair <- merge(x = object.1, y = object.2, merge.data = TRUE)
projected.embeddings.1<- t(x = GetAssayData(object = object.1, slot = "scale.data")[common.features, ]) %*%
Loadings(object = object.2[["pca"]])[common.features, ]
object.pair[['projectedpca.1']] <- CreateDimReducObject(
embeddings = rbind(projected.embeddings.1, Embeddings(object = object.2[["pca"]])),
assay = DefaultAssay(object = object.1),
key = "projectedpca1_"
)
projected.embeddings.2 <- t(x = GetAssayData(object = object.2, slot = "scale.data")[common.features, ]) %*%
Loadings(object = object.1[["pca"]])[common.features, ]
object.pair[['projectedpca.2']] <- CreateDimReducObject(
embeddings = rbind(projected.embeddings.2, Embeddings(object = object.1[["pca"]])),
assay = DefaultAssay(object = object.2),
key = "projectedpca2_"
)
object.pair[["pca"]] <- CreateDimReducObject(
embeddings = rbind(
Embeddings(object = object.1[["pca"]]),
Embeddings(object = object.2[["pca"]])),
assay = DefaultAssay(object = object.1),
key = "pca_"
)
reduction <- "projectedpca.1"
reduction.2 <- "projectedpca.2"
if (l2.norm){
slot(object = object.pair[["projectedpca.1"]], name = "cell.embeddings") <- Sweep(
x = Embeddings(object = object.pair[["projectedpca.1"]]),
MARGIN = 2,
STATS = apply(X = Embeddings(object = object.pair[["projectedpca.1"]]), MARGIN = 2, FUN = sd),
FUN = "/"
)
slot(object = object.pair[["projectedpca.2"]], name = "cell.embeddings") <- Sweep(
x = Embeddings(object = object.pair[["projectedpca.2"]]),
MARGIN = 2,
STATS = apply(X = Embeddings(object = object.pair[["projectedpca.2"]]), MARGIN = 2, FUN = sd),
FUN = "/"
)
object.pair <- L2Dim(object = object.pair, reduction = "projectedpca.1")
object.pair <- L2Dim(object = object.pair, reduction = "projectedpca.2")
reduction <- paste0(reduction, ".l2")
reduction.2 <- paste0(reduction.2, ".l2")
}
object.pair
},
stop("Invalid reduction parameter. Please choose either cca or rpca")
)
internal.neighbors <- internal.neighbors[c(i, j)]
anchors <- FindAnchors(
object.pair = object.pair,
assay = c("ToIntegrate", "ToIntegrate"),
slot = slot,
cells1 = colnames(x = object.1),
cells2 = colnames(x = object.2),
internal.neighbors = internal.neighbors,
reduction = reduction,
reduction.2 = reduction.2,
nn.reduction = nn.reduction,
dims = dims,
k.anchor = k.anchor,
k.filter = k.filter,
k.score = k.score,
max.features = max.features,
nn.method = nn.method,
n.trees = n.trees,
eps = eps,
verbose = verbose
)
anchors[, 1] <- anchors[, 1] + offsets[i]
anchors[, 2] <- anchors[, 2] + offsets[j]
return(anchors)
}
)
all.anchors <- do.call(what = 'rbind', args = all.anchors)
all.anchors <- rbind(all.anchors, all.anchors[, c(2, 1, 3)])
all.anchors <- AddDatasetID(anchor.df = all.anchors, offsets = offsets, obj.lengths = objects.ncell)
command <- LogSeuratCommand(object = object.list[[1]], return.command = TRUE)
anchor.set <- new(Class = "IntegrationAnchorSet",
object.list = object.list,
reference.objects = reference %||% seq_along(object.list),
anchors = all.anchors,
offsets = offsets,
anchor.features = anchor.features,
command = command
)
return(anchor.set)
}
#' Find transfer anchors
#'
#' Find a set of anchors between a reference and query object. These
#' anchors can later be used to transfer data from the reference to
#' query object using the \code{\link{TransferData}} object.
#'
#' The main steps of this procedure are outlined below. For a more detailed
#' description of the methodology, please see Stuart, Butler, et al Cell 2019.
#' \doi{10.1016/j.cell.2019.05.031}; \doi{10.1101/460147}
#'
#' \itemize{
#'
#' \item{Perform dimensional reduction. Exactly what is done here depends on
#' the values set for the \code{reduction} and \code{project.query}
#' parameters. If \code{reduction = "pcaproject"}, a PCA is performed on
#' either the reference (if \code{project.query = FALSE}) or the query (if
#' \code{project.query = TRUE}), using the \code{features} specified. The data
#' from the other dataset is then projected onto this learned PCA structure.
#' If \code{reduction = "cca"}, then CCA is performed on the reference and
#' query for this dimensional reduction step. If \code{l2.norm} is set to
#' \code{TRUE}, perform L2 normalization of the embedding vectors.}
#' \item{Identify anchors between the reference and query - pairs of cells
#' from each dataset that are contained within each other's neighborhoods
#' (also known as mutual nearest neighbors).}
#' \item{Filter low confidence anchors to ensure anchors in the low dimension
#' space are in broad agreement with the high dimensional measurements. This
#' is done by looking at the neighbors of each query cell in the reference
#' dataset using \code{max.features} to define this space. If the reference
#' cell isn't found within the first \code{k.filter} neighbors, remove the
#' anchor.}
#' \item{Assign each remaining anchor a score. For each anchor cell, determine
#' the nearest \code{k.score} anchors within its own dataset and within its
#' pair's dataset. Based on these neighborhoods, construct an overall neighbor
#' graph and then compute the shared neighbor overlap between anchor and query
#' cells (analogous to an SNN graph). We use the 0.01 and 0.90 quantiles on
#' these scores to dampen outlier effects and rescale to range between 0-1.}
#' }
#'
#' @param reference \code{\link{Seurat}} object to use as the reference
#' @param query \code{\link{Seurat}} object to use as the query
#' @param reference.assay Name of the Assay to use from reference
#' @param reference.neighbors Name of the Neighbor to use from the reference.
#' Optionally enables reuse of precomputed neighbors.
#' @param query.assay Name of the Assay to use from query
#' @param reduction Dimensional reduction to perform when finding anchors.
#' Options are:
#' \itemize{
#' \item{pcaproject: Project the PCA from the reference onto the query. We
#' recommend using PCA when reference and query datasets are from scRNA-seq}
#' \item{cca: Run a CCA on the reference and query }
#' }
#' @param reference.reduction Name of dimensional reduction to use from the
#' reference if running the pcaproject workflow. Optionally enables reuse of
#' precomputed reference dimensional reduction. If NULL (default), use a PCA
#' computed on the reference object.
#' @param project.query Project the PCA from the query dataset onto the
#' reference. Use only in rare cases where the query dataset has a much larger
#' cell number, but the reference dataset has a unique assay for transfer. In
#' this case, the default features will be set to the variable features of the
#' query object that are alos present in the reference.
#' @param features Features to use for dimensional reduction. If not specified,
#' set as variable features of the reference object which are also present in
#' the query.
#' @param normalization.method Name of normalization method used: LogNormalize
#' or SCT.
#' @param recompute.residuals If using SCT as a normalization method, compute
#' query Pearson residuals using the reference SCT model parameters.
#' @param npcs Number of PCs to compute on reference if reference.reduction is
#' not provided.
#' @param l2.norm Perform L2 normalization on the cell embeddings after
#' dimensional reduction
#' @param dims Which dimensions to use from the reduction to specify the
#' neighbor search space
#' @param k.anchor How many neighbors (k) to use when finding anchors
#' @param k.filter How many neighbors (k) to use when filtering anchors. Set to
#' NA to turn off filtering.
#' @param k.score How many neighbors (k) to use when scoring anchors
#' @param max.features The maximum number of features to use when specifying the
#' neighborhood search space in the anchor filtering
#' @param nn.method Method for nearest neighbor finding. Options include: rann,
#' annoy
#' @param n.trees More trees gives higher precision when using annoy approximate
#' nearest neighbor search
#' @param eps Error bound on the neighbor finding algorithm (from
#' \code{\link{RANN}})
#' @param approx.pca Use truncated singular value decomposition to approximate
#' PCA
#' @param mapping.score.k Compute and store nearest k query neighbors in the
#' AnchorSet object that is returned. You can optionally set this if you plan
#' on computing the mapping score and want to enable reuse of some downstream
#' neighbor calculations to make the mapping score function more efficient.
#' @param verbose Print progress bars and output
#'
#' @return Returns an \code{AnchorSet} object that can be used as input to
#' \code{\link{TransferData}}
#'
#' @references Stuart T, Butler A, et al. Comprehensive Integration of
#' Single-Cell Data. Cell. 2019;177:1888-1902 \doi{10.1016/j.cell.2019.05.031};
#'
#' @export
#' @concept integration
#' @examples
#' \dontrun{
#' # to install the SeuratData package see https://github.com/satijalab/seurat-data
#' library(SeuratData)
#' data("pbmc3k")
#'
#' # for demonstration, split the object into reference and query
#' pbmc.reference <- pbmc3k[, 1:1350]
#' pbmc.query <- pbmc3k[, 1351:2700]
#'
#' # perform standard preprocessing on each object
#' pbmc.reference <- NormalizeData(pbmc.reference)
#' pbmc.reference <- FindVariableFeatures(pbmc.reference)
#' pbmc.reference <- ScaleData(pbmc.reference)
#'
#' pbmc.query <- NormalizeData(pbmc.query)
#' pbmc.query <- FindVariableFeatures(pbmc.query)
#' pbmc.query <- ScaleData(pbmc.query)
#'
#' # find anchors
#' anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.query)
#'
#' # transfer labels
#' predictions <- TransferData(
#' anchorset = anchors,
#' refdata = pbmc.reference$seurat_annotations
#' )
#' pbmc.query <- AddMetaData(object = pbmc.query, metadata = predictions)
#' }
#'
FindTransferAnchors <- function(
reference,
query,
normalization.method = "LogNormalize",
recompute.residuals = TRUE,
reference.assay = NULL,
reference.neighbors = NULL,
query.assay = NULL,
reduction = "pcaproject",
reference.reduction = NULL,
project.query = FALSE,
features = NULL,
npcs = 30,
l2.norm = TRUE,
dims = 1:30,
k.anchor = 5,
k.filter = 200,
k.score = 30,
max.features = 200,
nn.method = "annoy",
n.trees = 50,
eps = 0,
approx.pca = TRUE,
mapping.score.k = NULL,
verbose = TRUE
) {
# input validation
ValidateParams_FindTransferAnchors(
reference = reference,
query = query,
normalization.method = normalization.method,
recompute.residuals = recompute.residuals,
reference.assay = reference.assay,
reference.neighbors = reference.neighbors,
query.assay = query.assay,
reduction = reduction,
reference.reduction = reference.reduction,
project.query = project.query,
features = features,
npcs = npcs,
l2.norm = l2.norm,
dims = dims,
k.anchor = k.anchor,
k.filter = k.filter,
k.score = k.score,
max.features = max.features,
nn.method = nn.method,
n.trees = n.trees,
eps = eps,
approx.pca = approx.pca,
mapping.score.k = mapping.score.k,
verbose = verbose
)
projected <- ifelse(test = reduction == "pcaproject", yes = TRUE, no = FALSE)
feature.mean <- NULL
if (normalization.method == "SCT") {
# ensure all residuals required are computed
query <- suppressWarnings(expr = GetResidual(object = query, assay = query.assay, features = features, verbose = FALSE))
if (is.null(x = reference.reduction)) {
reference <- suppressWarnings(expr = GetResidual(object = reference, assay = reference.assay, features = features, verbose = FALSE))
features <- intersect(
x = features,
y = intersect(
x = rownames(x = GetAssayData(object = query[[query.assay]], slot = "scale.data")),
y = rownames(x = GetAssayData(object = reference[[reference.assay]], slot = "scale.data"))
)
)
reference[[reference.assay]] <- as(
object = CreateAssayObject(
data = GetAssayData(object = reference[[reference.assay]], slot = "scale.data")[features, ]),
Class = "SCTAssay"
)
reference <- SetAssayData(
object = reference,
slot = "scale.data",
assay = reference.assay,
new.data = as.matrix(x = GetAssayData(object = reference[[reference.assay]], slot = "data"))
)
}
query[[query.assay]] <- as(
object = CreateAssayObject(
data = GetAssayData(object = query[[query.assay]], slot = "scale.data")[features, ]),
Class = "SCTAssay"
)
query <- SetAssayData(
object = query,
slot = "scale.data",
assay = query.assay,
new.data = as.matrix(x = GetAssayData(object = query[[query.assay]], slot = "data"))
)
feature.mean <- "SCT"
}
# only keep necessary info from objects
query <- DietSeurat(
object = query,
assays = query.assay,
dimreducs = reference.reduction,
features = features,
scale.data = TRUE
)
reference <- DietSeurat(
object = reference,
assays = reference.assay,
dimreducs = reference.reduction,
features = features,
scale.data = TRUE
)
# append query and reference to cell names - mainly to avoid name conflicts
query <- RenameCells(
object = query,
new.names = paste0(Cells(x = query), "_", "query")
)
reference <- RenameCells(
object = reference,
new.names = paste0(Cells(x = reference), "_", "reference")
)
# Perform PCA projection
if (reduction == 'pcaproject') {
if (project.query) {
if (is.null(x = reference.reduction)) {
reference.reduction <- "pca"
if (verbose) {
message("Performing PCA on the provided query using ", length(x = features), " features as input.")
}
if (normalization.method == "LogNormalize") {
query <- ScaleData(object = query, features = features, verbose = FALSE)
}
query <- RunPCA(
object = query,
npcs = npcs,
reduction.name = reference.reduction,
verbose = FALSE,
features = features,
approx = approx.pca
)
}
projected.pca <- ProjectCellEmbeddings(
reference = query,
reduction = reference.reduction,
query = reference,
dims = dims,
verbose = verbose
)
orig.embeddings <- Embeddings(object = query[[reference.reduction]])[, dims]
orig.loadings <- Loadings(object = query[[reference.reduction]])
} else {
if (is.null(x = reference.reduction)) {
reference.reduction <- "pca"
if (verbose) {
message("Performing PCA on the provided reference using ", length(x = features), " features as input.")
}
if (normalization.method == "LogNormalize") {
reference <- ScaleData(object = reference, features = features, verbose = FALSE)
}
reference <- RunPCA(
object = reference,
npcs = npcs,
verbose = FALSE,
features = features,
approx = approx.pca
)
}
projected.pca <- ProjectCellEmbeddings(
reference = reference,
reduction = reference.reduction,
query = query,
dims = dims,
feature.mean = feature.mean,
verbose = verbose
)
orig.embeddings <- Embeddings(object = reference[[reference.reduction]])[, dims]
orig.loadings <- Loadings(object = reference[[reference.reduction]])
}
combined.pca <- CreateDimReducObject(
embeddings = as.matrix(x = rbind(orig.embeddings, projected.pca)),
key = "ProjectPC_",
assay = reference.assay
)
combined.ob <- suppressWarnings(expr = merge(
x = DietSeurat(object = reference, counts = FALSE),
y = DietSeurat(object = query, counts = FALSE),
))
combined.ob[["pcaproject"]] <- combined.pca
colnames(x = orig.loadings) <- paste0("ProjectPC_", 1:ncol(x = orig.loadings))
Loadings(object = combined.ob[["pcaproject"]]) <- orig.loadings[, dims]
}
# Run CCA as dimension reduction to be used in anchor finding
if (reduction == 'cca') {
if (normalization.method == "LogNormalize") {
reference <- ScaleData(object = reference, features = features, verbose = FALSE)
query <- ScaleData(object = query, features = features, verbose = FALSE)
}
combined.ob <- RunCCA(
object1 = reference,
object2 = query,
features = features,
num.cc = max(dims),
renormalize = FALSE,
rescale = FALSE,
verbose = verbose
)
}
if (l2.norm) {
combined.ob <- L2Dim(object = combined.ob, reduction = reduction)
reduction <- paste0(reduction, ".l2")
}
precomputed.neighbors <- list(ref.neighbors = NULL, query.neighbors = NULL)
nn.idx1 <- NULL
nn.idx2 <- NULL
# if computing the mapping score later, compute large enough query
# neighborhood here to reuse
if (!is.null(x = mapping.score.k)) {
if (verbose) {
message("Finding query neighbors")
}
k.nn <- max(k.score, k.anchor)
query.neighbors <- NNHelper(
data = Embeddings(object = combined.ob[[reduction]])[Cells(x = query), ],
k = max(mapping.score.k, k.nn + 1),
method = nn.method,
n.trees = n.trees,
cache.index = TRUE
)
query.neighbors.sub <- query.neighbors
slot(object = query.neighbors.sub, name = "nn.idx") <- slot(
object = query.neighbors.sub,
name = "nn.idx")[, 1:(k.nn + 1)]
slot(object = query.neighbors.sub, name = "nn.dist") <- slot(
object = query.neighbors.sub,
name = "nn.dist")[, 1:(k.nn + 1)]
precomputed.neighbors[["query.neighbors"]] <- query.neighbors.sub
nn.idx2 <- Index(object = query.neighbors.sub)
}
if (!is.null(x = reference.neighbors)) {
precomputed.neighbors[["ref.neighbors"]] <- reference[[reference.neighbors]]
nn.idx1 <- Index(object = reference[[reference.neighbors]])
}
anchors <- FindAnchors(
object.pair = combined.ob,
assay = c(reference.assay, query.assay),
slot = "data",
cells1 = colnames(x = reference),
cells2 = colnames(x = query),
reduction = reduction,
internal.neighbors = precomputed.neighbors,
dims = 1:length(x = dims),
k.anchor = k.anchor,
k.filter = k.filter,
k.score = k.score,
max.features = max.features,
nn.method = nn.method,
n.trees = n.trees,
nn.idx1 = nn.idx1,
nn.idx2 = nn.idx2,
eps = eps,
projected = projected,
verbose = verbose
)
reductions <- slot(object = combined.ob, name = "reductions")
for (i in unique(x = c(reference.assay, query.assay))) {
dummy.assay <- paste0(i, "DUMMY")
suppressWarnings(
expr = combined.ob[[dummy.assay]] <- CreateDummyAssay(assay = combined.ob[[i]])
)
DefaultAssay(combined.ob) <- dummy.assay
combined.ob[[i]] <- NULL
suppressWarnings(
expr = combined.ob[[i]] <- combined.ob[[dummy.assay]]
)
DefaultAssay(combined.ob) <- i
combined.ob[[dummy.assay]] <- NULL
}
slot(object = combined.ob, name = "reductions") <- reductions
command <- LogSeuratCommand(object = combined.ob, return.command = TRUE)
anchor.set <- new(
Class = "TransferAnchorSet",
object.list = list(combined.ob),
reference.cells = colnames(x = reference),
query.cells = colnames(x = query),
anchors = anchors,
anchor.features = features,
command = command
)
if (!is.null(x = precomputed.neighbors[["query.neighbors"]])) {
slot(object = anchor.set, name = "neighbors") <- list(
query.neighbors = query.neighbors)
}
return(anchor.set)
}
#' Get the predicted identity
#'
#' Utility function to easily pull out the name of the class with the maximum
#' prediction. This is useful if you've set \code{prediction.assay = TRUE} in
#' \code{\link{TransferData}} and want to have a vector with the predicted class.
#'
#' @param object Seurat object
#' @param assay Name of the assay holding the predictions
#' @param slot Slot of the assay in which the prediction scores are stored
#' @param score.filter Return "Unassigned" for any cell with a score less than
#' this value
#'
#' @return Returns a vector of predicted class names
#'
#' @examples
#' \dontrun{
#' prediction.assay <- TransferData(anchorset = anchors, refdata = reference$class)
#' query[["predictions"]] <- prediction.assay
#' query$predicted.id <- GetTransferPredictions(query)
#' }
#' @export
#' @concept integration
#'
GetTransferPredictions <- function(object, assay = "predictions", slot = "data", score.filter = 0.75) {
dat <- GetAssayData(object[[assay]], slot = slot)
predictions <- apply(
X = dat,
MARGIN = 2,
FUN = function(x){
if (x['max'] < score.filter) {
"Unassigned"
} else {
x <- x[-which(x = names(x = x) == "max")]
names(x = which.max(x = x))
}
}
)
return(predictions)
}
#' Integrate data
#'
#' Perform dataset integration using a pre-computed \code{\link{AnchorSet}}.
#'
#' The main steps of this procedure are outlined below. For a more detailed
#' description of the methodology, please see Stuart, Butler, et al Cell 2019.
#' \doi{10.1016/j.cell.2019.05.031}; \doi{10.1101/460147}
#'
#' For pairwise integration:
#'
#' \itemize{
#' \item{Construct a weights matrix that defines the association between each
#' query cell and each anchor. These weights are computed as 1 - the distance
#' between the query cell and the anchor divided by the distance of the query
#' cell to the \code{k.weight}th anchor multiplied by the anchor score
#' computed in \code{\link{FindIntegrationAnchors}}. We then apply a Gaussian
#' kernel width a bandwidth defined by \code{sd.weight} and normalize across
#' all \code{k.weight} anchors.}
#' \item{Compute the anchor integration matrix as the difference between the
#' two expression matrices for every pair of anchor cells}
#' \item{Compute the transformation matrix as the product of the integration
#' matrix and the weights matrix.}
#' \item{Subtract the transformation matrix from the original expression
#' matrix.}
#' }
#'
#' For multiple dataset integration, we perform iterative pairwise integration.
#' To determine the order of integration (if not specified via
#' \code{sample.tree}), we
#' \itemize{
#' \item{Define a distance between datasets as the total number of cells in
#' the smaller dataset divided by the total number of anchors between the two
#' datasets.}
#' \item{Compute all pairwise distances between datasets}
#' \item{Cluster this distance matrix to determine a guide tree}
#' }
#'
#'
#' @param anchorset An \code{\link{AnchorSet}} object generated by
#' \code{\link{FindIntegrationAnchors}}
#' @param new.assay.name Name for the new assay containing the integrated data
#' @param normalization.method Name of normalization method used: LogNormalize
#' or SCT
#' @param features Vector of features to use when computing the PCA to determine
#' the weights. Only set if you want a different set from those used in the
#' anchor finding process
#' @param features.to.integrate Vector of features to integrate. By default,
#' will use the features used in anchor finding.
#' @param dims Number of dimensions to use in the anchor weighting procedure
#' @param k.weight Number of neighbors to consider when weighting anchors
#' @param weight.reduction Dimension reduction to use when calculating anchor
#' weights. This can be one of:
#' \itemize{
#' \item{A string, specifying the name of a dimension reduction present in
#' all objects to be integrated}
#' \item{A vector of strings, specifying the name of a dimension reduction to
#' use for each object to be integrated}
#' \item{A vector of \code{\link{DimReduc}} objects, specifying the object to
#' use for each object in the integration}
#' \item{NULL, in which case a new PCA will be calculated and used to
#' calculate anchor weights}
#' }
#' Note that, if specified, the requested dimension reduction will only be used
#' for calculating anchor weights in the first merge between reference and
#' query, as the merged object will subsequently contain more cells than was in
#' query, and weights will need to be calculated for all cells in the object.
#' @param sd.weight Controls the bandwidth of the Gaussian kernel for weighting
#' @param sample.tree Specify the order of integration. If NULL, will compute
#' automatically.
#' @param preserve.order Do not reorder objects based on size for each pairwise
#' integration.
#' @param eps Error bound on the neighbor finding algorithm (from
#' \code{\link{RANN}})
#' @param verbose Print progress bars and output
#'
#' @return Returns a \code{\link{Seurat}} object with a new integrated
#' \code{\link{Assay}}. If \code{normalization.method = "LogNormalize"}, the
#' integrated data is returned to the \code{data} slot and can be treated as
#' log-normalized, corrected data. If \code{normalization.method = "SCT"}, the
#' integrated data is returned to the \code{scale.data} slot and can be treated
#' as centered, corrected Pearson residuals.
#'
#' @references Stuart T, Butler A, et al. Comprehensive Integration of
#' Single-Cell Data. Cell. 2019;177:1888-1902 \doi{10.1016/j.cell.2019.05.031}
#'
#' @export
#' @concept integration
#' @examples
#' \dontrun{
#' # to install the SeuratData package see https://github.com/satijalab/seurat-data
#' library(SeuratData)
#' data("panc8")
#'
#' # panc8 is a merged Seurat object containing 8 separate pancreas datasets
#' # split the object by dataset
#' pancreas.list <- SplitObject(panc8, split.by = "tech")
#'
#' # perform standard preprocessing on each object