R/scDblFinder.R
In plger/scDblFinder: scDblFinder
Defines functions
.defaultKnnKs
.scDblAddCD
.aggResultsTable
.xgbtrain
.defTrainFeatures
.scDblscore
.knnSmooth
.evaluateKNN
scDblFinder
Documented in
scDblFinder
#' scDblFinder
#'
#' Identification of heterotypic (or neotypic) doublets in single-cell RNAseq
#' using cluster-based generation of artificial doublets.
#'
#' @param sce A \code{\link[SummarizedExperiment]{SummarizedExperiment-class}},
#' \code{\link[SingleCellExperiment]{SingleCellExperiment-class}}, or array of
#' counts.
#' @param artificialDoublets The approximate number of artificial doublets to
#' create. If \code{NULL}, will be the maximum of the number of cells or
#' \code{5*nbClusters^2} (with a minimum of 1500).
#' @param clusters The optional cluster assignments. This is used to make
#' doublets more efficiently. \code{clusters} should either be a vector of
#' labels for each cell, or the name of a colData column of \code{sce}.
#' Alternatively, if `clusters=TRUE`, fast clustering will be performed. If
#' `clusters` is a single integer, it will determine how many clusters to
#' create (using k-means clustering). If `clusters` is NULL or FALSE, purely
#' random artificial doublets will be generated.
#' @param samples A vector of the same length as cells (or the name of a column
#' of \code{colData(x)}), indicating to which sample each cell belongs. Here, a
#' sample is understood as being processed independently. If omitted, doublets
#' will be searched for with all cells together. If given, doublets will be
#' searched for independently for each sample, which is preferable if they
#' represent different captures. If your samples were multiplexed using cell
#' hashes, what you want to give here are the different batches/wells (i.e.
#' independent captures, since doublets cannot arise across them) rather
#' than biological samples.
#' @param multiSampleMode Either "split" (recommended if there is
#' heterogeneity across samples), "singleModel", "singleModelSplitThres", or
#' "asOne" (see details below).
#' @param knownDoublets An optional logical vector of known doublets (e.g.
#' through cell barcodes), or the name of a colData column of `sce` containing
#' that information. The way these are used depends on the `knownUse` argument.
#' @param knownUse The way to use known doublets, either 'discard' (they are
#' discarded for the purpose of training, but counted as positive for
#' thresholding) or 'positive' (they are used as positive doublets for training
#' - usually leads to a mild decrease in accuracy due to the fact that known
#' doublets typically include a sizeable fraction of homotypic doublets). Note
#' that `scDblFinder` does *not* enforce that the knownDoublets be necessarily
#' called as doublets in the final classification, if they are not predicted as
#' such.
#' @param nfeatures The number of top features to use. Alternatively, a
#' character vectors of feature names (e.g. highly-variable genes) to use.
#' @param dims The number of dimensions used.
#' @param dbr The expected doublet rate. By default this is assumed to be 1\%
#' per thousand cells captured (so 4\% among 4000 thousand cells), which is
#' appropriate for 10x datasets. Corrections for homeotypic doublets will be
#' performed on the given rate.
#' @param dbr.sd The uncertainty range in the doublet rate, interpreted as
#' a +/- around `dbr`. During thresholding, deviation from the expected doublet
#' rate will be calculated from these boundaries, and will be considered null
#' within these boundaries. If NULL, will be 40\% of `dbr`. Set to `dbr.sd=0` to
#' disable the uncertainty around the doublet rate, or to `dbr.sd=1` to disable
#' any expectation of the number of doublets (thus letting the thresholding be
#' entirely driven by the misclassification of artificial doublets).
#' @param k Number of nearest neighbors (for KNN graph). If more than one value
#' is given, the doublet density will be calculated at each k (and other values
#' at the highest k), and all the information will be used by the classifier.
#' If omitted, a reasonable set of values is used.
#' @param clustCor Include Spearman correlations to cell type averages in the
#' predictors. If `clustCor` is a matrix of cell type marker expressions (with
#' features as rows and cell types as columns), the subset of these which are
#' present in the selected features will be correlated to each cell to produce
#' additional predictors (i.e. one per cell type). Alternatively, if `clustCor`
#' is a positive integer, this number of inter-cluster markers will be selected
#' and used for correlation (se `clustCor=Inf` to use all available genes).
#' @param removeUnidentifiable Logical; whether to remove artificial doublets of
#' a combination that is generally found to be unidentifiable.
#' @param includePCs The index of principal components to include in the
#' predictors (e.g. `includePCs=1:2`), or the number of top components to use
#' (e.g. `includePCs=10`, equivalent to 1:10).
#' @param propRandom The proportion of the artificial doublets which
#' should be made of random cells (as opposed to inter-cluster combinations).
#' If clusters is FALSE or NULL, this is ignored (and set to 1).
#' @param propMarkers The proportion of features to select based on marker
#' identification.
#' @param trainingFeatures The features to use for training (defaults to an
#' optimal pre-selection based on benchmark datasets). To exclude features
#' (rather than list those to be included), prefix them with a "-".
#' @param unident.th The score threshold below which artificial doublets will be
#' considered unidentifiable.
#' @param processing Counts (real and artificial) processing before KNN. Either
#' 'default' (normal \code{scater}-based normalization and PCA), "rawPCA" (PCA
#' without normalization), "rawFeatures" (no normalization/dimensional
#' reduction), "normFeatures" (uses normalized features, without PCA) or a
#' custom function with (at least) arguments `e` (the matrix of counts) and
#' `dims` (the desired number of dimensions), returning a named matrix with
#' cells as rows and components as columns.
#' @param returnType Either "sce" (default), "table" (to return the table of
#' cell attributes including artificial doublets), or "full" (returns an SCE
#' object containing both the real and artificial cells).
#' @param score Score to use for final classification.
#' @param metric Error metric to optimize during training (e.g. 'merror',
#' 'logloss', 'auc', 'aucpr').
#' @param nrounds Maximum rounds of boosting. If NULL, will be determined
#' through cross-validation. If a number <=1, will used the best
#' cross-validation round minus `nrounds` times the standard deviation of the
#' classification error.
#' @param max_depth Maximum depths of each tree.
#' @param iter A positive integer indicating the number of scoring iterations
#' (ignored if `score` isn't based on classifiers). At each iteration, real
#' cells that would be called as doublets are excluding from the training, and
#' new scores are calculated. Recommended values are 1 or 2.
#' @param threshold Logical; whether to threshold scores into binary doublet
#' calls
#' @param aggregateFeatures Whether to perform feature aggregation (recommended
#' for ATAC). Can also be a positive integer, in which case this will indicate
#' the number of components to use for feature aggregation (if TRUE, `dims`
#' will be used.)
#' @param verbose Logical; whether to print messages and the thresholding plot.
#' @param BPPARAM Used for multithreading when splitting by samples (i.e. when
#' `samples!=NULL`); otherwise passed to eventual PCA and K/SNN calculations.
#' @param ... further arguments passed to \code{\link{getArtificialDoublets}}.
#'
#' @return The \code{sce} object with several additional colData columns, in
#' particular `scDblFinder.score` (the final score used) and `scDblFinder.class`
#' (whether the cell is called as 'doublet' or 'singlet'). See
#' \code{vignette("scDblFinder")} for more details; for alternative return
#' values, see the `returnType` argument.
#'
#' @details
#' This function generates artificial doublets from real cells, evaluates their
#' prevalence in the neighborhood of each cells, and uses this along with
#' additional cell-level features to classify doublets. The approach is
#' complementary to doublets identified via cell hashes and SNPs in multiplexed
#' samples: the latter can identify doublets formed by cells of the same type
#' from two samples, which are nearly undistinguishable from real cells
#' transcriptionally, but cannot identify doublets made by cells of the
#' same sample. See \code{vignette("scDblFinder")} for more details on the
#' method.
#'
#' The `clusters` and `propRandom` argument determines whether the artificial
#' doublets are generated between clusters or randomly.
#'
#' When multiple samples/captures are present, they should be specified using
#' the \code{samples} argument. In this case, we recommend the use of
#' \code{BPPARAM} to perform several of the steps in parallel. Artificial
#' doublets and kNN networks will be computed separately; then the behavior will
#' then depend on the `multiSampleMode` argument:
#'
#' \itemize{
#' \item \emph{split}: the whole process is split by sample. This is the
#' default and recommended mode, because it is the most robust (e.g. to
#' heterogeneity between samples, also for instance in the number of cells),
#' and in practice we have not seen major gains in sharing information across
#' samples;
#' \item \emph{singleModel}: the doublets are generated on a per-sample basis,
#' but the classifier and thresholding will be trained globally;
#' \item \emph{singleModelSplitThres}: the doublets are generated on a
#' per-sample basis, the classifier is trained globally, but the final
#' thresholding is per-sample;
#' \item \emph{asOne}: the doublet rate (if not given) is calculated as the
#' weighted average of sample-specific doublet rates, and all samples are
#' otherwise run as if they were one sample. This can get computationally
#' more intensive, and can lead to biases if there are batch effects.
#' }
#'
#' When inter-sample doublets are available, they can be provided to
#' `scDblFinder` through the \code{knownDoublets} argument to improve the
#' identification of further doublets. How exactly these are used depends on the
#' `knownUse` argument: with 'discard' (default), the known doublets are
#' excluded from the training step, but counted as positives. With 'positive',
#' they are included and treated as positive doublets for the training step.
#' Note that because known doublets can in practice include a lot of homotypic
#' doublets, this second approach can often lead to a slight decrease in the
#' accuracy of detecting heterotypic doublets.
#'
#' Finally, for some types of data, such as single-cell ATAC-seq, selecting a
#' number of top features is ineffective due to the high sparsity of the signal.
#' In such contexts, rather than _selecting_ features we recommend to use the
#' alternative approach of _aggregating_ similar features (with
#' `aggregateFeatures=TRUE`), which strongly improves accuracy. See the
#' vignette for more detail.
#'
#' @import SingleCellExperiment BiocParallel
#' @importFrom SummarizedExperiment colData<- assayNames
#' @importFrom scuttle normalizeCounts
#' @importFrom scater runPCA
#' @importFrom methods is
#' @importFrom DelayedArray as.matrix
#' @importFrom BiocNeighbors findKNN
#' @importFrom BiocSingular IrlbaParam
#'
#' @examples
#' library(SingleCellExperiment)
#' sce <- mockDoubletSCE()
#' sce <- scDblFinder(sce)
#' table(truth=sce$type, call=sce$scDblFinder.class)
#'
#' @export
#' @rdname scDblFinder
#' @import SingleCellExperiment
#' @importFrom SummarizedExperiment rowData<-
#' @importFrom BiocParallel SerialParam bpnworkers
scDblFinder <- function(
sce, clusters=NULL, samples=NULL, clustCor=NULL, artificialDoublets=NULL,
knownDoublets=NULL, knownUse=c("discard","positive"), dbr=NULL, dbr.sd=NULL,
nfeatures=1352, dims=20, k=NULL, removeUnidentifiable=TRUE, includePCs=19,
propRandom=0, propMarkers=0, aggregateFeatures=FALSE,
returnType=c("sce","table","full","counts"),
score=c("xgb","weighted","ratio"), processing="default", metric="logloss",
nrounds=0.25, max_depth=4, iter=3, trainingFeatures=NULL, unident.th=NULL,
multiSampleMode=c("split","singleModel","singleModelSplitThres","asOne"),
threshold=TRUE, verbose=is.null(samples), BPPARAM=SerialParam(), ...){
multiSampleMode <- match.arg(multiSampleMode)
## check arguments
if(!is(sce, "SingleCellExperiment") &&
( (!is.null(clusters) && is.character(clusters) && length(clusters)==1) ||
(!is.null(samples) && is.character(samples) && length(samples)==1) ) ){
stop("Passing a column name to the `samples` or `clusters` argument only",
" works if `sce` is a SingleCellExperiment.\n",
"Please pass the vector of labels.")
}
sce <- .checkSCE(sce)
score <- match.arg(score)
knownUse <- match.arg(knownUse)
if(!is.null(clustCor)){
if(is.null(dim(clustCor)) && (!is.numeric(clustCor) || clustCor<0))
stop("`clustCor` should be either a matrix of marker expression per cell",
" types, or a positive integer indicating the number of markers to use.")
}
returnType <- match.arg(returnType)
if(!is.null(clusters) && (!is.logical(clusters))){
if(length(clusters)>1 || !is.numeric(clusters))
clusters <- .checkColArg(sce, clusters)
if(is.factor(clusters)) clusters <- droplevels(clusters)
}
if(is.null(unident.th))
unident.th <- ifelse(is.null(clusters) || isFALSE(clusters), 0.2, 0)
knownDoublets <- .checkColArg(sce, knownDoublets)
samples <- .checkColArg(sce, samples)
if(!is.null(samples)) samples <- as.factor(samples)
.checkPropArg(propMarkers)
.checkPropArg(propRandom)
.checkPropArg(dbr.sd)
.checkPropArg(dbr, acceptNull=TRUE)
processing <- .checkProcArg(processing)
if(!bpisup(BPPARAM)){
## pre-start params for independent seeds between bplapply calls
bpstart(BPPARAM)
on.exit(bpstop(BPPARAM))
}
if(length(nfeatures)>1){
if(!all(nfeatures %in% row.names(sce)))
stop("'nfeatures' has a length >1, which is interpreted as feature (i.e.",
" row) names to use, but not all of the features specified are ",
"found in the object. ")
sel_features <- nfeatures
nfeatures <- length(sel_features)
}else{
stopifnot(nfeatures %% 1 == 0 && nfeatures>1)
## if clusters are given, it's more efficient to do feature selection before
## eventually splitting the dataset
if(!is.null(clusters) && length(clusters)>1 && !aggregateFeatures){
sel_features <- selFeatures(sce, clusters, nfeatures=nfeatures,
propMarkers=propMarkers)
}else{
sel_features <- row.names(sce)
}
}
if(!is.null(samples) && multiSampleMode=="asOne"){
if(is.null(dbr)){
tt <- as.numeric(table(samples))
dbr <- weighted.mean(tt/100000, tt)
}
samples <- NULL
}
if(!is.null(samples)){
## splitting by samples
if(!(isSplitMode <- multiSampleMode=="split")) includePCs <- c()
if(returnType=="full")
warning("`returnType='full'` ignored when splitting by samples")
cs <- split(seq_along(samples), samples, drop=TRUE)
names(nn) <- nn <- names(cs)
## run scDblFinder individually
d <- bplapply(nn, BPPARAM=BPPARAM, FUN=function(n){
x <- cs[[n]]
if(!is.null(clusters) && length(clusters)>1) clusters <- clusters[x]
if(!is.null(knownDoublets) && length(knownDoublets)>1){
knownDoublets <- knownDoublets[x]
if(!any(knownDoublets)) knownDoublets <- NULL
}
out <- tryCatch(
scDblFinder(sce[sel_features,x], clusters=clusters, dims=dims, dbr=dbr,
dbr.sd=dbr.sd, clustCor=clustCor, unident.th=unident.th,
knownDoublets=knownDoublets, knownUse=knownUse,
artificialDoublets=artificialDoublets, k=k,
processing=processing, nfeatures=nfeatures,
propRandom=propRandom, includePCs=includePCs,
propMarkers=propMarkers, trainingFeatures=trainingFeatures,
returnType=ifelse(returnType=="counts","counts","table"),
threshold=isSplitMode, score=ifelse(isSplitMode,score,"weighted"),
removeUnidentifiable=removeUnidentifiable, verbose=FALSE,
aggregateFeatures=aggregateFeatures, ...),
error=function(e){
stop("An error occured while processing sample '",n,"':\n", e)
})
if(!is.matrix(out)) out$sample <- n
out
})
if(returnType=="counts") return(do.call(cbind, d))
## aggregate the property tables
d <- .aggResultsTable(d)
if(multiSampleMode!="split"){
## score and thresholding
d <- .scDblscore(d, scoreType=score, threshold=threshold, dbr=dbr,
dbr.sd=dbr.sd, max_depth=max_depth, nrounds=nrounds,
iter=iter, BPPARAM=BPPARAM, verbose=verbose,
features=trainingFeatures, unident.th=unident.th,
metric=metric, filterUnidentifiable=removeUnidentifiable,
perSample=multiSampleMode=="singleModelSplitThres",
includeSamples=TRUE)
}
if(returnType=="table") return(d)
return(.scDblAddCD(sce, d))
}
## Handling a single sample
if(ncol(sce)<100)
warning("scDblFinder might not work well with very low numbers of cells.")
if(verbose && ncol(sce)>25000 && multiSampleMode!="asOne")
warning("You are trying to run scDblFinder on a very large number of ",
"cells. If these are from different captures, please specify this",
" using the `samples` argument.", immediate=TRUE)
k <- .defaultKnnKs(k, ncol(sce))
orig <- sce
wDbl <- c()
## if known doublets are given, we need to treat them separately
if(!is.null(knownDoublets) && length(wDbl <- which(knownDoublets))>0){
sce$knownDoublet <- knownDoublets
sce.dbl <- sce[,wDbl,drop=FALSE]
sce <- sce[,-wDbl,drop=FALSE]
if(!is.null(clusters) && length(clusters)>1){
clusters.dbl <- clusters[wDbl]
clusters <- clusters[-wDbl]
if(is.factor(clusters)) clusters <- droplevels(clusters)
}
}
if(aggregateFeatures){
if(verbose) message("Aggregating features...")
if(is.numeric(aggregateFeatures)){
fdims <- aggregateFeatures
}else{
fdims <- dims
}
if(length(fdims)==1) fdims <- seq_len(dims)[-1]
sce <- aggregateFeatures(sce, dims.use=fdims, k=nfeatures)
sel_features <- row.names(sce)
}
## clustering (if required)
if(isFALSE(clusters)) clusters <- NULL
if(!is.null(clusters)){
if(!is.null(clusters) && length(clusters)==1 && !isFALSE(clusters)){
if(verbose) message("Clustering cells...")
if(isTRUE(clusters)) clusters <- NULL
if(!is.null(clusters)){
clusters <- fastcluster(sce, ndims=dims, k=clusters, nfeatures=nfeatures,
returnType="preclusters",
BPPARAM=BPPARAM, verbose=FALSE)
}else{
clusters <- fastcluster(sce, ndims=dims, nfeatures=nfeatures,
BPPARAM=BPPARAM, verbose=FALSE)
}
}
nc <- length(unique(clusters))
if(nc==1) stop("Only one cluster generated. Consider specifying `cluster` ",
"(e.g. `cluster=10`)")
if(verbose) message(nc, " clusters")
}else{
characterize <- FALSE
}
cl <- clusters
## feature selection
if(length(sel_features)>nfeatures)
sel_features <- selFeatures(sce[sel_features,], cl, nfeatures=nfeatures,
propMarkers=propMarkers)
sce <- sce[sel_features,]
if(length(wDbl)>0) sce.dbl <- sce.dbl[sel_features,]
## get the artificial doublets
if(is.null(artificialDoublets))
artificialDoublets <- min( 25000, max(1500,
ceiling(ncol(sce)*0.8),
10*length(unique(cl))^2 ) )
if(artificialDoublets<=2)
artificialDoublets <- min(ceiling(artificialDoublets*ncol(sce)),25000)
if(verbose)
message("Creating ~", artificialDoublets, " artificial doublets...")
ad <- getArtificialDoublets(counts(sce), n=artificialDoublets,
clusters=clusters, propRandom=propRandom, ...)
gc(verbose=FALSE)
ado <- ad$origins
ad <- ad$counts
no <- ncol(sce) + length(wDbl)
ado2 <- as.factor(c(rep(NA, no), as.character(ado)))
src <- factor( rep(1:2, c(no,ncol(ad))), labels = c("real","artificial"))
ctype <- factor( rep(c(1L,ifelse(knownUse=="positive",2L,1L),2L),
c(ncol(sce),length(wDbl),ncol(ad))),
labels=c("real","doublet") )
inclInTrain <- rep(c(TRUE,ifelse(knownUse=="positive",TRUE,FALSE),TRUE),
c(ncol(sce),length(wDbl),ncol(ad)))
e <- counts(sce)
if(!is.null(wDbl)) e <- cbind(e, counts(sce.dbl))
e <- cbind(e, ad[row.names(sce),])
# evaluate by library size and non-zero features
lsizes <- Matrix::colSums(e)
cxds_score <- cxds2(e, whichDbls=which(ctype=="doublet" | !inclInTrain))
nfeatures <- Matrix::colSums(e>0L)
nAbove2 <- Matrix::colSums(e>2L)
if(returnType=="counts"){
sce_out <- SingleCellExperiment(list(
counts=cbind(counts(sce), ad[row.names(sce),])))
sce_out$type <- ctype
sce_out$src <- src
sce_out$origin <- ado2
sce_out$cluster <- NA
if(!is.null(clusters)) colData(sce_out)[colnames(sce),"cluster"] <- clusters
sce_out$cxds_score <- cxds_score
return(sce_out)
}
if(verbose) message("Dimensional reduction")
if(!is.null(clustCor) && !is.null(clusters)){
if(!is.null(dim(clustCor))){
clustCor <- .clustSpearman(e, clustCor)
}else{
clustCor <- .clustSpearman(e, clusters, nMarkers=clustCor)
}
}
if(is.character(processing)){
pca <- switch(processing,
default=.defaultProcessing(e, dims=dims),
rawPCA=.defaultProcessing(e, dims=dims, doNorm=FALSE),
rawFeatures=t(e),
atac=.atacProcessing(e, dims=dims),
normFeatures=t(normalizeCounts(e)),
stop("Unknown processing function.")
)
}else{
pca <- processing(e, dims=dims)
stopifnot(identical(row.names(pca),colnames(e)))
}
ex <- NULL
if(!is.null(clusters)) ex <- getExpectedDoublets(clusters, dbr)
if(verbose) message("Evaluating kNN...")
d <- .evaluateKNN(pca, ctype, ado2, expected=ex, k=k)
#if(characterize) knn <- d$knn ## experimental
d <- d$d
if(!is.null(clusters)){
d$cluster <- NA
d[colnames(sce),"cluster"] <- clusters
}else{
d$cluster <- NULL
}
d$lsizes <- lsizes
d$nfeatures <- nfeatures
d$nAbove2 <- nAbove2
d$src <- src
d$cxds_score <- cxds_score
d$include.in.training <- inclInTrain
if(!is.null(clustCor)) d <- cbind(d, clustCor)
## classify
if(length(includePCs)==1) includePCs <- seq_len(includePCs)
includePCs <- includePCs[includePCs<ncol(pca)]
d <- .scDblscore(d, scoreType=score, addVals=pca[,includePCs,drop=FALSE],
threshold=threshold, dbr=dbr, dbr.sd=dbr.sd, nrounds=nrounds,
max_depth=max_depth, iter=iter, BPPARAM=BPPARAM,
features=trainingFeatures, verbose=verbose, metric=metric,
filterUnidentifiable=removeUnidentifiable,
unident.th=unident.th)
#if(characterize) d <- .callDblType(d, pca, knn=knn, origins=ado2)
if(returnType=="table") return(d)
if(returnType=="full"){
sce_out <- SingleCellExperiment(list(
counts=cbind(counts(sce), ad[row.names(sce),])), colData=d)
reducedDim(sce_out, "PCA") <- pca
if(is(d,"DataFrame") && !is.null(metadata(d)$scDblFinder.stats))
metadata(sce_out)$scDblFinder.stats <- metadata(d)$scDblFinder.stats
return(sce_out)
}
rowData(orig)$scDblFinder.selected <- row.names(orig) %in% sel_features
.scDblAddCD(orig, d)
}
#' @importFrom BiocNeighbors AnnoyParam
.evaluateKNN <- function(pca, ctype, origins, expected=NULL, k){
knn <- suppressWarnings(findKNN(pca, max(k), BNPARAM=AnnoyParam()))
hasOrigins <- length(unique(origins))>1
knn$type <- matrix(as.integer(ctype)[knn$index]-1L, nrow=nrow(knn$index))
if(hasOrigins) knn$orig <- matrix(origins[knn$index], nrow=nrow(knn[[1]]))
if(any(w <- knn$distance==0))
knn$distance[w] <- min(knn$distance[knn$distance[,1]>0,1])
md <- max(knn$distance[,1])
dr <- t(vapply(seq_len(nrow(knn$distance)), FUN.VALUE=numeric(2L),
FUN=function(x){
w <- knn$type[x,]==1
dA <- ifelse(length(wA <- which(w))==0, 2*md,
knn$distance[x,wA[1]])
dB <- ifelse(length(wB <- which(!w))==0, 2*md,
knn$distance[x,wB[1]])
c(dA,dB)
}))
dw <- sqrt(max(k)-seq_len(max(k))) * 1/knn$distance
dw <- dw/rowSums(dw)
d <- data.frame( row.names=row.names(pca), type=ctype, cluster=NA,
weighted=rowSums(knn$type*dw),
distanceToNearest=knn$distance[,1],
distanceToNearestDoublet=dr[,1],
distanceToNearestReal=dr[,2],
nearestClass=knn$type[,1] )
if(hasOrigins) d <- cbind(d, .getMostLikelyOrigins(knn, origins))
for(ki in k)
d[[paste0("ratio.k",ki)]] <- rowSums(knn$type[,seq_len(ki)])/ki
if(hasOrigins && !is.null(expected)){
w <- which(d$type=="doublet")
class.weighted <- vapply( split(d$weighted[w], d$mostLikelyOrigin[w]),
FUN.VALUE=numeric(1L), FUN=mean )
d$difficulty <- 1
w <- which(!is.na(d$mostLikelyOrigin))
d$difficulty[w] <- 1-class.weighted[d$mostLikelyOrigin[w]]
#d$difficulty <- .knnSmooth(knn, d$difficulty, use.distance=FALSE)
d$expected <- expected[d$mostLikelyOrigin]
ob <- table(d$mostLikelyOrigin)
d$observed <- ob[d$mostLikelyOrigin]
w <- which(is.na(d$mostLikelyOrigin))
d$observed[w] <- d$expected[w] <- 0
}
list(knn=knn, d=d)
}
#' @importFrom stats quantile weighted.mean
.knnSmooth <- function(knn, score, use.distance=TRUE, type=NULL){
w <- seq_len(ncol(knn$index))
if(use.distance){
mind <- quantile(knn$distance[,1], probs=0.1)
if(mind==0) mind <- 0.5
}
vapply(seq_len(nrow(knn$index)), FUN.VALUE=numeric(1L), FUN=function(i){
x <- knn$index[i,]
if(!is.null(type)){
w <- knn$type[i,]==type
}
if(sum(w)==0) return(score[i])
x <- x[w]
if(use.distance){
weights <- mind+c(0,knn$distance[i,][w])
weights <- 1/sqrt(weights)
}else{
weights <- 1/seq_len(1+length(x))
}
weighted.mean(c(score[i],score[x]),weights)
})
}
#' @importFrom S4Vectors DataFrame metadata
#' @importFrom stats predict quantile
.scDblscore <- function(d, scoreType="xgb", nrounds=NULL, max_depth=5, iter=2,
threshold=TRUE, verbose=TRUE, dbr=NULL, dbr.sd=NULL,
features=NULL, filterUnidentifiable=TRUE, addVals=NULL,
metric="logloss", eta=0.3, BPPARAM=SerialParam(),
includeSamples=FALSE, perSample=TRUE, unident.th=0.1, ...){
gdbr <- .gdbr(d, dbr)
if(!is.null(d$sample) && length(unique(d$sample))==1) d$sample <- NULL
if(is.null(dbr.sd)) dbr.sd <- 0.3*gdbr+0.025
if(scoreType=="xgb"){
if(verbose) message("Training model...")
d$score <- NULL
if(is.null(features)){
prds <- .defTrainFeatures(d)
}else{
if("ratio.k*" %in% features)
features <- c(features[features!="ratio.k*"],
grep("^ratio\\.k",colnames(d),value=TRUE))
toExclude <- grep("^-",features)
if(length(toExclude)==0){
if(length(mis <- setdiff(features, colnames(d)))>0)
warning("The following features were not found: ",
paste(mis,collapse=", "))
prds <- intersect(features, colnames(d))
}else{
if(length(toExclude)!=length(features))
stop("Mixture of included/excluded features - use only either.")
prds <- setdiff(.defTrainFeatures(d), gsub("^-","",features))
}
prds <- setdiff(prds,c("type","src","class","cluster"))
}
if(!is.null(features)) message(paste("Features used for training:\n",
paste(prds,collapse=", ")))
preds <- as(as.matrix(d[,prds,drop=FALSE]), "CsparseMatrix")
if(includeSamples && !is.null(d$sample))
preds <- cbind(preds, as(stats::model.matrix(~d$sample)[,-1,drop=FALSE],
"CsparseMatrix"))
if(!is.null(addVals)){
stopifnot(nrow(addVals)==nrow(preds))
preds <- cbind(preds, as(addVals, "CsparseMatrix"))
rm(addVals)
}
w <- which(d$type=="real")
ratio <- rev(grep("^ratio\\.k",colnames(d)))[1]
if(!is.null(d$sample) && !perSample){
tt <- table(d$type, d$sample)
expected.ratio <- (1+tt["doublet",])/(1+tt["real",])
d$adjusted.ratio <- d[[ratio]]/expected.ratio[d$sample]
d <- .rescaleSampleScores(d, TRUE, what="cxds_score",
newName="adjusted.cxds")
d$score <- (d$adjusted.ratio + d$adjusted.cxds)/2
}else{
d$score <- (d$cxds_score + d[[ratio]]/max(d[[ratio]]))/2
}
max.iter <- iter
while(iter>0){
# remove cells with a high chance of being doublets from the training,
# as well as unidentifiable artificial doublets
w <- which( (d$type=="real" &
doubletThresholding(d, dbr=dbr, dbr.sd=dbr.sd, stringency=0.7,
perSample=perSample,
returnType="call")=="doublet") |
(d$type=="doublet" & d$score<unident.th & filterUnidentifiable) |
!d$include.in.training )
if(verbose) message("iter=",max.iter-iter,", ", length(w),
" cells excluded from training.")
d$score <- tryCatch({
fit <- .xgbtrain(preds[-w,], d$type[-w], nrounds, metric=metric,
max_depth=max_depth, eta=eta, #base_score=gdbr,
nthreads=BiocParallel::bpnworkers(BPPARAM))
predict(fit, as.matrix(preds))
}, error=function(e) d$score)
if(!is.null(d$mostLikelyOrigin)){
wO <- which(d$type!="real" & !is.na(d$mostLikelyOrigin))
class.diff <- vapply( split(d$score[wO], d$mostLikelyOrigin[wO]),
FUN.VALUE=numeric(1L), FUN=mean )
d$difficulty <- mean(class.diff)
wO <- which(!is.na(d$mostLikelyOrigin))
d$difficulty[wO] <- 1-class.diff[d$mostLikelyOrigin[wO]]
if(filterUnidentifiable && iter==max.iter)
d <- .filterUnrecognizableDoublets(d)
}
iter <- iter-1
}
d$include.in.training[w] <- FALSE
########################
# Uncomment and use with scDblFinder(..., returnType="table") to extract
# variable importance
# return(xgb.importance(model=fit))
#######################
}else{
if(scoreType=="ratio"){
d$score <- d$ratio
}else{
d$score <- d$weighted
}
}
d <- DataFrame(d)
if(threshold){
th <- doubletThresholding( d, dbr=dbr, dbr.sd=dbr.sd, perSample=perSample,
... )
if(!is.null(d$sample) && length(th)>1){
d$class <- ifelse(d$score >= th[d$sample], "doublet", "singlet")
}else{
d$class <- ifelse(d$score >= th, "doublet", "singlet")
}
if(verbose) message("Threshold found:", paste(round(th,3), collapse=" "))
## set class of known (i.e. inputted) doublets:
d$class[d$src=="real" & d$type=="doublet"] <- "doublet"
if(!is.null(d$mostLikelyOrigin)){
th.stats <- .getDoubletStats(d, th, dbr, dbr.sd)
metadata(d)$scDblFinder.stats <- th.stats
}
metadata(d)$scDblFinder.threshold <- th
d$nearestClass <- factor(d$nearestClass, levels = 0:1,
labels=c("cell","artificialDoublet"))
dbr <- sum(d$class=="doublet" & d$src=="real")/sum(d$src=="real")
if(verbose) message(sum(d$class=="doublet" & d$src=="real"), " (",
round(100*dbr,1),"%) doublets called")
}
d
}
.defTrainFeatures <- function(d){
setdiff(colnames(d), c("mostLikelyOrigin","originAmbiguous",
"distanceToNearestDoublet", "type",
"src","distanceToNearest","class",
"nearestClass","cluster","sample","expected",
"include.in.training","observed"))
}
#' @importFrom xgboost xgb.cv xgboost
.xgbtrain <- function(d2, ctype, nrounds=NULL, max_depth=6, nfold=5,
tree_method="exact", subsample=0.75, nthreads=1,
metric="logloss", ...){
if(!is.integer(ctype)) ctype <- as.integer(ctype)-1
d2 <- as.matrix(d2)
if(is.null(nrounds)) nrounds <- 0L
if(!is.numeric(nrounds) || nrounds<0)
stop("If given, `nrounds` must be a positive number!")
if(nrounds<=1){
# use cross-validation
res <- xgb.cv(data=d2, label=ctype, nrounds=200, max_depth=max_depth,
objective="binary:logistic", eval_metric=metric,
early_stopping_rounds=2, tree_method=tree_method, nfold=nfold,
subsample=subsample, nthread=nthreads, verbose=FALSE, ...)
best <- res$best_iteration
if(nrounds==0){
nrounds <- best
}else{
e <- res$evaluation_log
ac <- e[[grep("test.+mean",colnames(e))]][best] +
nrounds * e[[grep("test.+std",colnames(e))]][best]
nrounds <- min(which(e[[grep("test.+mean",colnames(e))]] <= ac))
}
#message("Best iteration: ", best, "; selected nrounds: ", nrounds)
}
xgboost( d2, ctype, nrounds=nrounds, eval_metric=metric,
objective="binary:logistic", tree_method=tree_method,
max_depth=max_depth, early_stopping_rounds=2, verbose=FALSE,
nthread=nthreads, ... )
}
.aggResultsTable <- function(d, keep.col=NULL){
ths <- sapply(d, FUN=function(x){
if(!is(x,"DFrame") || is.null(th <- metadata(x)$scDblFinder.threshold))
return(NULL)
th
})
cn <- table(unlist(lapply(d, colnames)))
cn <- c(names(cn)[cn==length(d)], "total.prop.real")
if(!is.null(keep.col)) cn <- intersect(cn, keep.col)
d <- do.call(rbind, lapply(d, FUN=function(x){
x$total.prop.real <- sum(x$type=="real",na.rm=TRUE)/nrow(x)
if(!is.null(x$cluster)) x$cluster <- as.character(x$cluster)
x[,cn]
}))
if(!is.null(d$cluster)) d$cluster <- as.factor(d$cluster)
if(!is.null(d$sample)) d$sample <- as.factor(d$sample)
metadata(d)$scDblFinder.threshold <- ths
d
}
# add the relevant fields of the scDblFinder results table to the SCE
#' @importFrom stats relevel
.scDblAddCD <- function(sce, d){
fields <- c("sample","cluster","class","score","ratio","weighted",
"difficulty","cxds_score","mostLikelyOrigin","originAmbiguous",
"origin.prob", "origin.call", "origin.2ndBest")
if(!is.data.frame(d) && is.list(d)) d <- .aggResultsTable(d, fields)
d <- d[colnames(sce),]
for(f in fields){
if(!is.null(d[[f]])) sce[[paste0("scDblFinder.",f)]] <- d[[f]]
}
if(!is.null(sce$scDblFinder.class)) sce$scDblFinder.class <-
relevel(as.factor(sce$scDblFinder.class),"singlet")
if(is(d,"DataFrame")){
if(!is.null(metadata(d)$scDblFinder.stats))
metadata(sce)$scDblFinder.stats <- metadata(d)$scDblFinder.stats
metadata(sce)$scDblFinder.threshold <- metadata(d)$scDblFinder.threshold
}
sce
}
## sets a reasonable set of ks (for KNN)
.defaultKnnKs <- function(k=NULL, n){
if(!is.null(dim(n))) n <- ncol(n)
if(!is.null(k)) return(k[k<=ceiling(n/2)])
kmax <- max(ceiling(sqrt(n/2)),25)
k <- c(3,10,15,20,25,50,kmax)
unique(k[k<=kmax])
}
plger/scDblFinder documentation built on March 20, 2024, 9:46 p.m.
R Package Documentation
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Defines functions .defaultKnnKs .scDblAddCD .aggResultsTable .xgbtrain .defTrainFeatures .scDblscore .knnSmooth .evaluateKNN scDblFinder
Documented in scDblFinder
#' scDblFinder
#'
#' Identification of heterotypic (or neotypic) doublets in single-cell RNAseq
#' using cluster-based generation of artificial doublets.
#'
#' @param sce A \code{\link[SummarizedExperiment]{SummarizedExperiment-class}},
#' \code{\link[SingleCellExperiment]{SingleCellExperiment-class}}, or array of
#' counts.
#' @param artificialDoublets The approximate number of artificial doublets to
#' create. If \code{NULL}, will be the maximum of the number of cells or
#' \code{5*nbClusters^2} (with a minimum of 1500).
#' @param clusters The optional cluster assignments. This is used to make
#' doublets more efficiently. \code{clusters} should either be a vector of
#' labels for each cell, or the name of a colData column of \code{sce}.
#' Alternatively, if `clusters=TRUE`, fast clustering will be performed. If
#' `clusters` is a single integer, it will determine how many clusters to
#' create (using k-means clustering). If `clusters` is NULL or FALSE, purely
#' random artificial doublets will be generated.
#' @param samples A vector of the same length as cells (or the name of a column
#' of \code{colData(x)}), indicating to which sample each cell belongs. Here, a
#' sample is understood as being processed independently. If omitted, doublets
#' will be searched for with all cells together. If given, doublets will be
#' searched for independently for each sample, which is preferable if they
#' represent different captures. If your samples were multiplexed using cell
#' hashes, what you want to give here are the different batches/wells (i.e.
#' independent captures, since doublets cannot arise across them) rather
#' than biological samples.
#' @param multiSampleMode Either "split" (recommended if there is
#' heterogeneity across samples), "singleModel", "singleModelSplitThres", or
#' "asOne" (see details below).
#' @param knownDoublets An optional logical vector of known doublets (e.g.
#' through cell barcodes), or the name of a colData column of `sce` containing
#' that information. The way these are used depends on the `knownUse` argument.
#' @param knownUse The way to use known doublets, either 'discard' (they are
#' discarded for the purpose of training, but counted as positive for
#' thresholding) or 'positive' (they are used as positive doublets for training
#' - usually leads to a mild decrease in accuracy due to the fact that known
#' doublets typically include a sizeable fraction of homotypic doublets). Note
#' that `scDblFinder` does *not* enforce that the knownDoublets be necessarily
#' called as doublets in the final classification, if they are not predicted as
#' such.
#' @param nfeatures The number of top features to use. Alternatively, a
#' character vectors of feature names (e.g. highly-variable genes) to use.
#' @param dims The number of dimensions used.
#' @param dbr The expected doublet rate. By default this is assumed to be 1\%
#' per thousand cells captured (so 4\% among 4000 thousand cells), which is
#' appropriate for 10x datasets. Corrections for homeotypic doublets will be
#' performed on the given rate.
#' @param dbr.sd The uncertainty range in the doublet rate, interpreted as
#' a +/- around `dbr`. During thresholding, deviation from the expected doublet
#' rate will be calculated from these boundaries, and will be considered null
#' within these boundaries. If NULL, will be 40\% of `dbr`. Set to `dbr.sd=0` to
#' disable the uncertainty around the doublet rate, or to `dbr.sd=1` to disable
#' any expectation of the number of doublets (thus letting the thresholding be
#' entirely driven by the misclassification of artificial doublets).
#' @param k Number of nearest neighbors (for KNN graph). If more than one value
#' is given, the doublet density will be calculated at each k (and other values
#' at the highest k), and all the information will be used by the classifier.
#' If omitted, a reasonable set of values is used.
#' @param clustCor Include Spearman correlations to cell type averages in the
#' predictors. If `clustCor` is a matrix of cell type marker expressions (with
#' features as rows and cell types as columns), the subset of these which are
#' present in the selected features will be correlated to each cell to produce
#' additional predictors (i.e. one per cell type). Alternatively, if `clustCor`
#' is a positive integer, this number of inter-cluster markers will be selected
#' and used for correlation (se `clustCor=Inf` to use all available genes).
#' @param removeUnidentifiable Logical; whether to remove artificial doublets of
#' a combination that is generally found to be unidentifiable.
#' @param includePCs The index of principal components to include in the
#' predictors (e.g. `includePCs=1:2`), or the number of top components to use
#' (e.g. `includePCs=10`, equivalent to 1:10).
#' @param propRandom The proportion of the artificial doublets which
#' should be made of random cells (as opposed to inter-cluster combinations).
#' If clusters is FALSE or NULL, this is ignored (and set to 1).
#' @param propMarkers The proportion of features to select based on marker
#' identification.
#' @param trainingFeatures The features to use for training (defaults to an
#' optimal pre-selection based on benchmark datasets). To exclude features
#' (rather than list those to be included), prefix them with a "-".
#' @param unident.th The score threshold below which artificial doublets will be
#' considered unidentifiable.
#' @param processing Counts (real and artificial) processing before KNN. Either
#' 'default' (normal \code{scater}-based normalization and PCA), "rawPCA" (PCA
#' without normalization), "rawFeatures" (no normalization/dimensional
#' reduction), "normFeatures" (uses normalized features, without PCA) or a
#' custom function with (at least) arguments `e` (the matrix of counts) and
#' `dims` (the desired number of dimensions), returning a named matrix with
#' cells as rows and components as columns.
#' @param returnType Either "sce" (default), "table" (to return the table of
#' cell attributes including artificial doublets), or "full" (returns an SCE
#' object containing both the real and artificial cells).
#' @param score Score to use for final classification.
#' @param metric Error metric to optimize during training (e.g. 'merror',
#' 'logloss', 'auc', 'aucpr').
#' @param nrounds Maximum rounds of boosting. If NULL, will be determined
#' through cross-validation. If a number <=1, will used the best
#' cross-validation round minus `nrounds` times the standard deviation of the
#' classification error.
#' @param max_depth Maximum depths of each tree.
#' @param iter A positive integer indicating the number of scoring iterations
#' (ignored if `score` isn't based on classifiers). At each iteration, real
#' cells that would be called as doublets are excluding from the training, and
#' new scores are calculated. Recommended values are 1 or 2.
#' @param threshold Logical; whether to threshold scores into binary doublet
#' calls
#' @param aggregateFeatures Whether to perform feature aggregation (recommended
#' for ATAC). Can also be a positive integer, in which case this will indicate
#' the number of components to use for feature aggregation (if TRUE, `dims`
#' will be used.)
#' @param verbose Logical; whether to print messages and the thresholding plot.
#' @param BPPARAM Used for multithreading when splitting by samples (i.e. when
#' `samples!=NULL`); otherwise passed to eventual PCA and K/SNN calculations.
#' @param ... further arguments passed to \code{\link{getArtificialDoublets}}.
#'
#' @return The \code{sce} object with several additional colData columns, in
#' particular `scDblFinder.score` (the final score used) and `scDblFinder.class`
#' (whether the cell is called as 'doublet' or 'singlet'). See
#' \code{vignette("scDblFinder")} for more details; for alternative return
#' values, see the `returnType` argument.
#'
#' @details
#' This function generates artificial doublets from real cells, evaluates their
#' prevalence in the neighborhood of each cells, and uses this along with
#' additional cell-level features to classify doublets. The approach is
#' complementary to doublets identified via cell hashes and SNPs in multiplexed
#' samples: the latter can identify doublets formed by cells of the same type
#' from two samples, which are nearly undistinguishable from real cells
#' transcriptionally, but cannot identify doublets made by cells of the
#' same sample. See \code{vignette("scDblFinder")} for more details on the
#' method.
#'
#' The `clusters` and `propRandom` argument determines whether the artificial
#' doublets are generated between clusters or randomly.
#'
#' When multiple samples/captures are present, they should be specified using
#' the \code{samples} argument. In this case, we recommend the use of
#' \code{BPPARAM} to perform several of the steps in parallel. Artificial
#' doublets and kNN networks will be computed separately; then the behavior will
#' then depend on the `multiSampleMode` argument:
#'
#' \itemize{
#' \item \emph{split}: the whole process is split by sample. This is the
#' default and recommended mode, because it is the most robust (e.g. to
#' heterogeneity between samples, also for instance in the number of cells),
#' and in practice we have not seen major gains in sharing information across
#' samples;
#' \item \emph{singleModel}: the doublets are generated on a per-sample basis,
#' but the classifier and thresholding will be trained globally;
#' \item \emph{singleModelSplitThres}: the doublets are generated on a
#' per-sample basis, the classifier is trained globally, but the final
#' thresholding is per-sample;
#' \item \emph{asOne}: the doublet rate (if not given) is calculated as the
#' weighted average of sample-specific doublet rates, and all samples are
#' otherwise run as if they were one sample. This can get computationally
#' more intensive, and can lead to biases if there are batch effects.
#' }
#'
#' When inter-sample doublets are available, they can be provided to
#' `scDblFinder` through the \code{knownDoublets} argument to improve the
#' identification of further doublets. How exactly these are used depends on the
#' `knownUse` argument: with 'discard' (default), the known doublets are
#' excluded from the training step, but counted as positives. With 'positive',
#' they are included and treated as positive doublets for the training step.
#' Note that because known doublets can in practice include a lot of homotypic
#' doublets, this second approach can often lead to a slight decrease in the
#' accuracy of detecting heterotypic doublets.
#'
#' Finally, for some types of data, such as single-cell ATAC-seq, selecting a
#' number of top features is ineffective due to the high sparsity of the signal.
#' In such contexts, rather than _selecting_ features we recommend to use the
#' alternative approach of _aggregating_ similar features (with
#' `aggregateFeatures=TRUE`), which strongly improves accuracy. See the
#' vignette for more detail.
#'
#' @import SingleCellExperiment BiocParallel
#' @importFrom SummarizedExperiment colData<- assayNames
#' @importFrom scuttle normalizeCounts
#' @importFrom scater runPCA
#' @importFrom methods is
#' @importFrom DelayedArray as.matrix
#' @importFrom BiocNeighbors findKNN
#' @importFrom BiocSingular IrlbaParam
#'
#' @examples
#' library(SingleCellExperiment)
#' sce <- mockDoubletSCE()
#' sce <- scDblFinder(sce)
#' table(truth=sce$type, call=sce$scDblFinder.class)
#'
#' @export
#' @rdname scDblFinder
#' @import SingleCellExperiment
#' @importFrom SummarizedExperiment rowData<-
#' @importFrom BiocParallel SerialParam bpnworkers
scDblFinder <- function(
sce, clusters=NULL, samples=NULL, clustCor=NULL, artificialDoublets=NULL,
knownDoublets=NULL, knownUse=c("discard","positive"), dbr=NULL, dbr.sd=NULL,
nfeatures=1352, dims=20, k=NULL, removeUnidentifiable=TRUE, includePCs=19,
propRandom=0, propMarkers=0, aggregateFeatures=FALSE,
returnType=c("sce","table","full","counts"),
score=c("xgb","weighted","ratio"), processing="default", metric="logloss",
nrounds=0.25, max_depth=4, iter=3, trainingFeatures=NULL, unident.th=NULL,
multiSampleMode=c("split","singleModel","singleModelSplitThres","asOne"),
threshold=TRUE, verbose=is.null(samples), BPPARAM=SerialParam(), ...){
multiSampleMode <- match.arg(multiSampleMode)
## check arguments
if(!is(sce, "SingleCellExperiment") &&
( (!is.null(clusters) && is.character(clusters) && length(clusters)==1) ||
(!is.null(samples) && is.character(samples) && length(samples)==1) ) ){
stop("Passing a column name to the `samples` or `clusters` argument only",
" works if `sce` is a SingleCellExperiment.\n",
"Please pass the vector of labels.")
}
sce <- .checkSCE(sce)
score <- match.arg(score)
knownUse <- match.arg(knownUse)
if(!is.null(clustCor)){
if(is.null(dim(clustCor)) && (!is.numeric(clustCor) || clustCor<0))
stop("`clustCor` should be either a matrix of marker expression per cell",
" types, or a positive integer indicating the number of markers to use.")
}
returnType <- match.arg(returnType)
if(!is.null(clusters) && (!is.logical(clusters))){
if(length(clusters)>1 || !is.numeric(clusters))
clusters <- .checkColArg(sce, clusters)
if(is.factor(clusters)) clusters <- droplevels(clusters)
}
if(is.null(unident.th))
unident.th <- ifelse(is.null(clusters) || isFALSE(clusters), 0.2, 0)
knownDoublets <- .checkColArg(sce, knownDoublets)
samples <- .checkColArg(sce, samples)
if(!is.null(samples)) samples <- as.factor(samples)
.checkPropArg(propMarkers)
.checkPropArg(propRandom)
.checkPropArg(dbr.sd)
.checkPropArg(dbr, acceptNull=TRUE)
processing <- .checkProcArg(processing)
if(!bpisup(BPPARAM)){
## pre-start params for independent seeds between bplapply calls
bpstart(BPPARAM)
on.exit(bpstop(BPPARAM))
}
if(length(nfeatures)>1){
if(!all(nfeatures %in% row.names(sce)))
stop("'nfeatures' has a length >1, which is interpreted as feature (i.e.",
" row) names to use, but not all of the features specified are ",
"found in the object. ")
sel_features <- nfeatures
nfeatures <- length(sel_features)
}else{
stopifnot(nfeatures %% 1 == 0 && nfeatures>1)
## if clusters are given, it's more efficient to do feature selection before
## eventually splitting the dataset
if(!is.null(clusters) && length(clusters)>1 && !aggregateFeatures){
sel_features <- selFeatures(sce, clusters, nfeatures=nfeatures,
propMarkers=propMarkers)
}else{
sel_features <- row.names(sce)
}
}
if(!is.null(samples) && multiSampleMode=="asOne"){
if(is.null(dbr)){
tt <- as.numeric(table(samples))
dbr <- weighted.mean(tt/100000, tt)
}
samples <- NULL
}
if(!is.null(samples)){
## splitting by samples
if(!(isSplitMode <- multiSampleMode=="split")) includePCs <- c()
if(returnType=="full")
warning("`returnType='full'` ignored when splitting by samples")
cs <- split(seq_along(samples), samples, drop=TRUE)
names(nn) <- nn <- names(cs)
## run scDblFinder individually
d <- bplapply(nn, BPPARAM=BPPARAM, FUN=function(n){
x <- cs[[n]]
if(!is.null(clusters) && length(clusters)>1) clusters <- clusters[x]
if(!is.null(knownDoublets) && length(knownDoublets)>1){
knownDoublets <- knownDoublets[x]
if(!any(knownDoublets)) knownDoublets <- NULL
}
out <- tryCatch(
scDblFinder(sce[sel_features,x], clusters=clusters, dims=dims, dbr=dbr,
dbr.sd=dbr.sd, clustCor=clustCor, unident.th=unident.th,
knownDoublets=knownDoublets, knownUse=knownUse,
artificialDoublets=artificialDoublets, k=k,
processing=processing, nfeatures=nfeatures,
propRandom=propRandom, includePCs=includePCs,
propMarkers=propMarkers, trainingFeatures=trainingFeatures,
returnType=ifelse(returnType=="counts","counts","table"),
threshold=isSplitMode, score=ifelse(isSplitMode,score,"weighted"),
removeUnidentifiable=removeUnidentifiable, verbose=FALSE,
aggregateFeatures=aggregateFeatures, ...),
error=function(e){
stop("An error occured while processing sample '",n,"':\n", e)
})
if(!is.matrix(out)) out$sample <- n
out
})
if(returnType=="counts") return(do.call(cbind, d))
## aggregate the property tables
d <- .aggResultsTable(d)
if(multiSampleMode!="split"){
## score and thresholding
d <- .scDblscore(d, scoreType=score, threshold=threshold, dbr=dbr,
dbr.sd=dbr.sd, max_depth=max_depth, nrounds=nrounds,
iter=iter, BPPARAM=BPPARAM, verbose=verbose,
features=trainingFeatures, unident.th=unident.th,
metric=metric, filterUnidentifiable=removeUnidentifiable,
perSample=multiSampleMode=="singleModelSplitThres",
includeSamples=TRUE)
}
if(returnType=="table") return(d)
return(.scDblAddCD(sce, d))
}
## Handling a single sample
if(ncol(sce)<100)
warning("scDblFinder might not work well with very low numbers of cells.")
if(verbose && ncol(sce)>25000 && multiSampleMode!="asOne")
warning("You are trying to run scDblFinder on a very large number of ",
"cells. If these are from different captures, please specify this",
" using the `samples` argument.", immediate=TRUE)
k <- .defaultKnnKs(k, ncol(sce))
orig <- sce
wDbl <- c()
## if known doublets are given, we need to treat them separately
if(!is.null(knownDoublets) && length(wDbl <- which(knownDoublets))>0){
sce$knownDoublet <- knownDoublets
sce.dbl <- sce[,wDbl,drop=FALSE]
sce <- sce[,-wDbl,drop=FALSE]
if(!is.null(clusters) && length(clusters)>1){
clusters.dbl <- clusters[wDbl]
clusters <- clusters[-wDbl]
if(is.factor(clusters)) clusters <- droplevels(clusters)
}
}
if(aggregateFeatures){
if(verbose) message("Aggregating features...")
if(is.numeric(aggregateFeatures)){
fdims <- aggregateFeatures
}else{
fdims <- dims
}
if(length(fdims)==1) fdims <- seq_len(dims)[-1]
sce <- aggregateFeatures(sce, dims.use=fdims, k=nfeatures)
sel_features <- row.names(sce)
}
## clustering (if required)
if(isFALSE(clusters)) clusters <- NULL
if(!is.null(clusters)){
if(!is.null(clusters) && length(clusters)==1 && !isFALSE(clusters)){
if(verbose) message("Clustering cells...")
if(isTRUE(clusters)) clusters <- NULL
if(!is.null(clusters)){
clusters <- fastcluster(sce, ndims=dims, k=clusters, nfeatures=nfeatures,
returnType="preclusters",
BPPARAM=BPPARAM, verbose=FALSE)
}else{
clusters <- fastcluster(sce, ndims=dims, nfeatures=nfeatures,
BPPARAM=BPPARAM, verbose=FALSE)
}
}
nc <- length(unique(clusters))
if(nc==1) stop("Only one cluster generated. Consider specifying `cluster` ",
"(e.g. `cluster=10`)")
if(verbose) message(nc, " clusters")
}else{
characterize <- FALSE
}
cl <- clusters
## feature selection
if(length(sel_features)>nfeatures)
sel_features <- selFeatures(sce[sel_features,], cl, nfeatures=nfeatures,
propMarkers=propMarkers)
sce <- sce[sel_features,]
if(length(wDbl)>0) sce.dbl <- sce.dbl[sel_features,]
## get the artificial doublets
if(is.null(artificialDoublets))
artificialDoublets <- min( 25000, max(1500,
ceiling(ncol(sce)*0.8),
10*length(unique(cl))^2 ) )
if(artificialDoublets<=2)
artificialDoublets <- min(ceiling(artificialDoublets*ncol(sce)),25000)
if(verbose)
message("Creating ~", artificialDoublets, " artificial doublets...")
ad <- getArtificialDoublets(counts(sce), n=artificialDoublets,
clusters=clusters, propRandom=propRandom, ...)
gc(verbose=FALSE)
ado <- ad$origins
ad <- ad$counts
no <- ncol(sce) + length(wDbl)
ado2 <- as.factor(c(rep(NA, no), as.character(ado)))
src <- factor( rep(1:2, c(no,ncol(ad))), labels = c("real","artificial"))
ctype <- factor( rep(c(1L,ifelse(knownUse=="positive",2L,1L),2L),
c(ncol(sce),length(wDbl),ncol(ad))),
labels=c("real","doublet") )
inclInTrain <- rep(c(TRUE,ifelse(knownUse=="positive",TRUE,FALSE),TRUE),
c(ncol(sce),length(wDbl),ncol(ad)))
e <- counts(sce)
if(!is.null(wDbl)) e <- cbind(e, counts(sce.dbl))
e <- cbind(e, ad[row.names(sce),])
# evaluate by library size and non-zero features
lsizes <- Matrix::colSums(e)
cxds_score <- cxds2(e, whichDbls=which(ctype=="doublet" | !inclInTrain))
nfeatures <- Matrix::colSums(e>0L)
nAbove2 <- Matrix::colSums(e>2L)
if(returnType=="counts"){
sce_out <- SingleCellExperiment(list(
counts=cbind(counts(sce), ad[row.names(sce),])))
sce_out$type <- ctype
sce_out$src <- src
sce_out$origin <- ado2
sce_out$cluster <- NA
if(!is.null(clusters)) colData(sce_out)[colnames(sce),"cluster"] <- clusters
sce_out$cxds_score <- cxds_score
return(sce_out)
}
if(verbose) message("Dimensional reduction")
if(!is.null(clustCor) && !is.null(clusters)){
if(!is.null(dim(clustCor))){
clustCor <- .clustSpearman(e, clustCor)
}else{
clustCor <- .clustSpearman(e, clusters, nMarkers=clustCor)
}
}
if(is.character(processing)){
pca <- switch(processing,
default=.defaultProcessing(e, dims=dims),
rawPCA=.defaultProcessing(e, dims=dims, doNorm=FALSE),
rawFeatures=t(e),
atac=.atacProcessing(e, dims=dims),
normFeatures=t(normalizeCounts(e)),
stop("Unknown processing function.")
)
}else{
pca <- processing(e, dims=dims)
stopifnot(identical(row.names(pca),colnames(e)))
}
ex <- NULL
if(!is.null(clusters)) ex <- getExpectedDoublets(clusters, dbr)
if(verbose) message("Evaluating kNN...")
d <- .evaluateKNN(pca, ctype, ado2, expected=ex, k=k)
#if(characterize) knn <- d$knn ## experimental
d <- d$d
if(!is.null(clusters)){
d$cluster <- NA
d[colnames(sce),"cluster"] <- clusters
}else{
d$cluster <- NULL
}
d$lsizes <- lsizes
d$nfeatures <- nfeatures
d$nAbove2 <- nAbove2
d$src <- src
d$cxds_score <- cxds_score
d$include.in.training <- inclInTrain
if(!is.null(clustCor)) d <- cbind(d, clustCor)
## classify
if(length(includePCs)==1) includePCs <- seq_len(includePCs)
includePCs <- includePCs[includePCs<ncol(pca)]
d <- .scDblscore(d, scoreType=score, addVals=pca[,includePCs,drop=FALSE],
threshold=threshold, dbr=dbr, dbr.sd=dbr.sd, nrounds=nrounds,
max_depth=max_depth, iter=iter, BPPARAM=BPPARAM,
features=trainingFeatures, verbose=verbose, metric=metric,
filterUnidentifiable=removeUnidentifiable,
unident.th=unident.th)
#if(characterize) d <- .callDblType(d, pca, knn=knn, origins=ado2)
if(returnType=="table") return(d)
if(returnType=="full"){
sce_out <- SingleCellExperiment(list(
counts=cbind(counts(sce), ad[row.names(sce),])), colData=d)
reducedDim(sce_out, "PCA") <- pca
if(is(d,"DataFrame") && !is.null(metadata(d)$scDblFinder.stats))
metadata(sce_out)$scDblFinder.stats <- metadata(d)$scDblFinder.stats
return(sce_out)
}
rowData(orig)$scDblFinder.selected <- row.names(orig) %in% sel_features
.scDblAddCD(orig, d)
}
#' @importFrom BiocNeighbors AnnoyParam
.evaluateKNN <- function(pca, ctype, origins, expected=NULL, k){
knn <- suppressWarnings(findKNN(pca, max(k), BNPARAM=AnnoyParam()))
hasOrigins <- length(unique(origins))>1
knn$type <- matrix(as.integer(ctype)[knn$index]-1L, nrow=nrow(knn$index))
if(hasOrigins) knn$orig <- matrix(origins[knn$index], nrow=nrow(knn[[1]]))
if(any(w <- knn$distance==0))
knn$distance[w] <- min(knn$distance[knn$distance[,1]>0,1])
md <- max(knn$distance[,1])
dr <- t(vapply(seq_len(nrow(knn$distance)), FUN.VALUE=numeric(2L),
FUN=function(x){
w <- knn$type[x,]==1
dA <- ifelse(length(wA <- which(w))==0, 2*md,
knn$distance[x,wA[1]])
dB <- ifelse(length(wB <- which(!w))==0, 2*md,
knn$distance[x,wB[1]])
c(dA,dB)
}))
dw <- sqrt(max(k)-seq_len(max(k))) * 1/knn$distance
dw <- dw/rowSums(dw)
d <- data.frame( row.names=row.names(pca), type=ctype, cluster=NA,
weighted=rowSums(knn$type*dw),
distanceToNearest=knn$distance[,1],
distanceToNearestDoublet=dr[,1],
distanceToNearestReal=dr[,2],
nearestClass=knn$type[,1] )
if(hasOrigins) d <- cbind(d, .getMostLikelyOrigins(knn, origins))
for(ki in k)
d[[paste0("ratio.k",ki)]] <- rowSums(knn$type[,seq_len(ki)])/ki
if(hasOrigins && !is.null(expected)){
w <- which(d$type=="doublet")
class.weighted <- vapply( split(d$weighted[w], d$mostLikelyOrigin[w]),
FUN.VALUE=numeric(1L), FUN=mean )
d$difficulty <- 1
w <- which(!is.na(d$mostLikelyOrigin))
d$difficulty[w] <- 1-class.weighted[d$mostLikelyOrigin[w]]
#d$difficulty <- .knnSmooth(knn, d$difficulty, use.distance=FALSE)
d$expected <- expected[d$mostLikelyOrigin]
ob <- table(d$mostLikelyOrigin)
d$observed <- ob[d$mostLikelyOrigin]
w <- which(is.na(d$mostLikelyOrigin))
d$observed[w] <- d$expected[w] <- 0
}
list(knn=knn, d=d)
}
#' @importFrom stats quantile weighted.mean
.knnSmooth <- function(knn, score, use.distance=TRUE, type=NULL){
w <- seq_len(ncol(knn$index))
if(use.distance){
mind <- quantile(knn$distance[,1], probs=0.1)
if(mind==0) mind <- 0.5
}
vapply(seq_len(nrow(knn$index)), FUN.VALUE=numeric(1L), FUN=function(i){
x <- knn$index[i,]
if(!is.null(type)){
w <- knn$type[i,]==type
}
if(sum(w)==0) return(score[i])
x <- x[w]
if(use.distance){
weights <- mind+c(0,knn$distance[i,][w])
weights <- 1/sqrt(weights)
}else{
weights <- 1/seq_len(1+length(x))
}
weighted.mean(c(score[i],score[x]),weights)
})
}
#' @importFrom S4Vectors DataFrame metadata
#' @importFrom stats predict quantile
.scDblscore <- function(d, scoreType="xgb", nrounds=NULL, max_depth=5, iter=2,
threshold=TRUE, verbose=TRUE, dbr=NULL, dbr.sd=NULL,
features=NULL, filterUnidentifiable=TRUE, addVals=NULL,
metric="logloss", eta=0.3, BPPARAM=SerialParam(),
includeSamples=FALSE, perSample=TRUE, unident.th=0.1, ...){
gdbr <- .gdbr(d, dbr)
if(!is.null(d$sample) && length(unique(d$sample))==1) d$sample <- NULL
if(is.null(dbr.sd)) dbr.sd <- 0.3*gdbr+0.025
if(scoreType=="xgb"){
if(verbose) message("Training model...")
d$score <- NULL
if(is.null(features)){
prds <- .defTrainFeatures(d)
}else{
if("ratio.k*" %in% features)
features <- c(features[features!="ratio.k*"],
grep("^ratio\\.k",colnames(d),value=TRUE))
toExclude <- grep("^-",features)
if(length(toExclude)==0){
if(length(mis <- setdiff(features, colnames(d)))>0)
warning("The following features were not found: ",
paste(mis,collapse=", "))
prds <- intersect(features, colnames(d))
}else{
if(length(toExclude)!=length(features))
stop("Mixture of included/excluded features - use only either.")
prds <- setdiff(.defTrainFeatures(d), gsub("^-","",features))
}
prds <- setdiff(prds,c("type","src","class","cluster"))
}
if(!is.null(features)) message(paste("Features used for training:\n",
paste(prds,collapse=", ")))
preds <- as(as.matrix(d[,prds,drop=FALSE]), "CsparseMatrix")
if(includeSamples && !is.null(d$sample))
preds <- cbind(preds, as(stats::model.matrix(~d$sample)[,-1,drop=FALSE],
"CsparseMatrix"))
if(!is.null(addVals)){
stopifnot(nrow(addVals)==nrow(preds))
preds <- cbind(preds, as(addVals, "CsparseMatrix"))
rm(addVals)
}
w <- which(d$type=="real")
ratio <- rev(grep("^ratio\\.k",colnames(d)))[1]
if(!is.null(d$sample) && !perSample){
tt <- table(d$type, d$sample)
expected.ratio <- (1+tt["doublet",])/(1+tt["real",])
d$adjusted.ratio <- d[[ratio]]/expected.ratio[d$sample]
d <- .rescaleSampleScores(d, TRUE, what="cxds_score",
newName="adjusted.cxds")
d$score <- (d$adjusted.ratio + d$adjusted.cxds)/2
}else{
d$score <- (d$cxds_score + d[[ratio]]/max(d[[ratio]]))/2
}
max.iter <- iter
while(iter>0){
# remove cells with a high chance of being doublets from the training,
# as well as unidentifiable artificial doublets
w <- which( (d$type=="real" &
doubletThresholding(d, dbr=dbr, dbr.sd=dbr.sd, stringency=0.7,
perSample=perSample,
returnType="call")=="doublet") |
(d$type=="doublet" & d$score<unident.th & filterUnidentifiable) |
!d$include.in.training )
if(verbose) message("iter=",max.iter-iter,", ", length(w),
" cells excluded from training.")
d$score <- tryCatch({
fit <- .xgbtrain(preds[-w,], d$type[-w], nrounds, metric=metric,
max_depth=max_depth, eta=eta, #base_score=gdbr,
nthreads=BiocParallel::bpnworkers(BPPARAM))
predict(fit, as.matrix(preds))
}, error=function(e) d$score)
if(!is.null(d$mostLikelyOrigin)){
wO <- which(d$type!="real" & !is.na(d$mostLikelyOrigin))
class.diff <- vapply( split(d$score[wO], d$mostLikelyOrigin[wO]),
FUN.VALUE=numeric(1L), FUN=mean )
d$difficulty <- mean(class.diff)
wO <- which(!is.na(d$mostLikelyOrigin))
d$difficulty[wO] <- 1-class.diff[d$mostLikelyOrigin[wO]]
if(filterUnidentifiable && iter==max.iter)
d <- .filterUnrecognizableDoublets(d)
}
iter <- iter-1
}
d$include.in.training[w] <- FALSE
########################
# Uncomment and use with scDblFinder(..., returnType="table") to extract
# variable importance
# return(xgb.importance(model=fit))
#######################
}else{
if(scoreType=="ratio"){
d$score <- d$ratio
}else{
d$score <- d$weighted
}
}
d <- DataFrame(d)
if(threshold){
th <- doubletThresholding( d, dbr=dbr, dbr.sd=dbr.sd, perSample=perSample,
... )
if(!is.null(d$sample) && length(th)>1){
d$class <- ifelse(d$score >= th[d$sample], "doublet", "singlet")
}else{
d$class <- ifelse(d$score >= th, "doublet", "singlet")
}
if(verbose) message("Threshold found:", paste(round(th,3), collapse=" "))
## set class of known (i.e. inputted) doublets:
d$class[d$src=="real" & d$type=="doublet"] <- "doublet"
if(!is.null(d$mostLikelyOrigin)){
th.stats <- .getDoubletStats(d, th, dbr, dbr.sd)
metadata(d)$scDblFinder.stats <- th.stats
}
metadata(d)$scDblFinder.threshold <- th
d$nearestClass <- factor(d$nearestClass, levels = 0:1,
labels=c("cell","artificialDoublet"))
dbr <- sum(d$class=="doublet" & d$src=="real")/sum(d$src=="real")
if(verbose) message(sum(d$class=="doublet" & d$src=="real"), " (",
round(100*dbr,1),"%) doublets called")
}
d
}
.defTrainFeatures <- function(d){
setdiff(colnames(d), c("mostLikelyOrigin","originAmbiguous",
"distanceToNearestDoublet", "type",
"src","distanceToNearest","class",
"nearestClass","cluster","sample","expected",
"include.in.training","observed"))
}
#' @importFrom xgboost xgb.cv xgboost
.xgbtrain <- function(d2, ctype, nrounds=NULL, max_depth=6, nfold=5,
tree_method="exact", subsample=0.75, nthreads=1,
metric="logloss", ...){
if(!is.integer(ctype)) ctype <- as.integer(ctype)-1
d2 <- as.matrix(d2)
if(is.null(nrounds)) nrounds <- 0L
if(!is.numeric(nrounds) || nrounds<0)
stop("If given, `nrounds` must be a positive number!")
if(nrounds<=1){
# use cross-validation
res <- xgb.cv(data=d2, label=ctype, nrounds=200, max_depth=max_depth,
objective="binary:logistic", eval_metric=metric,
early_stopping_rounds=2, tree_method=tree_method, nfold=nfold,
subsample=subsample, nthread=nthreads, verbose=FALSE, ...)
best <- res$best_iteration
if(nrounds==0){
nrounds <- best
}else{
e <- res$evaluation_log
ac <- e[[grep("test.+mean",colnames(e))]][best] +
nrounds * e[[grep("test.+std",colnames(e))]][best]
nrounds <- min(which(e[[grep("test.+mean",colnames(e))]] <= ac))
}
#message("Best iteration: ", best, "; selected nrounds: ", nrounds)
}
xgboost( d2, ctype, nrounds=nrounds, eval_metric=metric,
objective="binary:logistic", tree_method=tree_method,
max_depth=max_depth, early_stopping_rounds=2, verbose=FALSE,
nthread=nthreads, ... )
}
.aggResultsTable <- function(d, keep.col=NULL){
ths <- sapply(d, FUN=function(x){
if(!is(x,"DFrame") || is.null(th <- metadata(x)$scDblFinder.threshold))
return(NULL)
th
})
cn <- table(unlist(lapply(d, colnames)))
cn <- c(names(cn)[cn==length(d)], "total.prop.real")
if(!is.null(keep.col)) cn <- intersect(cn, keep.col)
d <- do.call(rbind, lapply(d, FUN=function(x){
x$total.prop.real <- sum(x$type=="real",na.rm=TRUE)/nrow(x)
if(!is.null(x$cluster)) x$cluster <- as.character(x$cluster)
x[,cn]
}))
if(!is.null(d$cluster)) d$cluster <- as.factor(d$cluster)
if(!is.null(d$sample)) d$sample <- as.factor(d$sample)
metadata(d)$scDblFinder.threshold <- ths
d
}
# add the relevant fields of the scDblFinder results table to the SCE
#' @importFrom stats relevel
.scDblAddCD <- function(sce, d){
fields <- c("sample","cluster","class","score","ratio","weighted",
"difficulty","cxds_score","mostLikelyOrigin","originAmbiguous",
"origin.prob", "origin.call", "origin.2ndBest")
if(!is.data.frame(d) && is.list(d)) d <- .aggResultsTable(d, fields)
d <- d[colnames(sce),]
for(f in fields){
if(!is.null(d[[f]])) sce[[paste0("scDblFinder.",f)]] <- d[[f]]
}
if(!is.null(sce$scDblFinder.class)) sce$scDblFinder.class <-
relevel(as.factor(sce$scDblFinder.class),"singlet")
if(is(d,"DataFrame")){
if(!is.null(metadata(d)$scDblFinder.stats))
metadata(sce)$scDblFinder.stats <- metadata(d)$scDblFinder.stats
metadata(sce)$scDblFinder.threshold <- metadata(d)$scDblFinder.threshold
}
sce
}
## sets a reasonable set of ks (for KNN)
.defaultKnnKs <- function(k=NULL, n){
if(!is.null(dim(n))) n <- ncol(n)
if(!is.null(k)) return(k[k<=ceiling(n/2)])
kmax <- max(ceiling(sqrt(n/2)),25)
k <- c(3,10,15,20,25,50,kmax)
unique(k[k<=kmax])
}
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