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preprocessing.R
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#' @include generics.R
#'
NULL
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Functions
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Calculate the Barcode Distribution Inflection
#'
#' This function calculates an adaptive inflection point ("knee") of the barcode distribution
#' for each sample group. This is useful for determining a threshold for removing
#' low-quality samples.
#'
#' The function operates by calculating the slope of the barcode number vs. rank
#' distribution, and then finding the point at which the distribution changes most
#' steeply (the "knee"). Of note, this calculation often must be restricted as to the
#' range at which it performs, so `threshold` parameters are provided to restrict the
#' range of the calculation based on the rank of the barcodes. [BarcodeInflectionsPlot()]
#' is provided as a convenience function to visualize and test different thresholds and
#' thus provide more sensical end results.
#'
#' See [BarcodeInflectionsPlot()] to visualize the calculated inflection points and
#' [SubsetByBarcodeInflections()] to subsequently subset the Seurat object.
#'
#' @param object Seurat object
#' @param barcode.column Column to use as proxy for barcodes ("nCount_RNA" by default)
#' @param group.column Column to group by ("orig.ident" by default)
#' @param threshold.high Ignore barcodes of rank above thisf threshold in inflection calculation
#' @param threshold.low Ignore barcodes of rank below this threshold in inflection calculation
#'
#' @return Returns Seurat object with a new list in the `tools` slot, `CalculateBarcodeInflections` with values:
#'
#' * `barcode_distribution` - contains the full barcode distribution across the entire dataset
#' * `inflection_points` - the calculated inflection points within the thresholds
#' * `threshold_values` - the provided (or default) threshold values to search within for inflections
#' * `cells_pass` - the cells that pass the inflection point calculation
#'
#' @importFrom methods slot
#' @importFrom stats ave aggregate
#'
#' @export
#'
#' @author Robert A. Amezquita, \email{[email protected]}
#' @seealso \code{\link{BarcodeInflectionsPlot}} \code{\link{SubsetByBarcodeInflections}}
#'
#' @examples
#' CalculateBarcodeInflections(pbmc_small, group.column = 'groups')
#'
CalculateBarcodeInflections <- function(
object,
barcode.column = "nCount_RNA",
group.column = "orig.ident",
threshold.low = NULL,
threshold.high = NULL
) {
## Check that barcode.column exists in meta.data
if (!(barcode.column %in% colnames(x = object[[]]))) {
stop("`barcode.column` specified not present in Seurat object provided")
}
# Calculation of barcode distribution
## Append rank by grouping x umi column
# barcode_dist <- as.data.frame([email protected])[, c(group.column, barcode.column)]
barcode_dist <- object[[c(group.column, barcode.column)]]
barcode_dist <- barcode_dist[do.call(what = order, args = barcode_dist), ] # order by columns left to right
barcode_dist$rank <- ave(
x = barcode_dist[, barcode.column], barcode_dist[, group.column],
FUN = function(x) {
return(rev(x = order(x)))
}
)
barcode_dist <- barcode_dist[order(barcode_dist[, group.column], barcode_dist[, 'rank']), ]
## calculate rawdiff and append per group
top <- aggregate(
x = barcode_dist[, barcode.column],
by = list(barcode_dist[, group.column]),
FUN = function(x) {
return(c(0, diff(x = log10(x = x + 1))))
})$x
bot <- aggregate(
x = barcode_dist[, 'rank'],
by = list(barcode_dist[, group.column]),
FUN = function(x) {
return(c(0, diff(x = x)))
}
)$x
barcode_dist$rawdiff <- unlist(x = mapply(
FUN = function(x, y) {
return(ifelse(test = is.na(x = x / y), yes = 0, no = x / y))
},
x = top,
y = bot
))
# Calculation of inflection points
## Set thresholds for rank of barcodes to ignore
threshold.low <- threshold.low %||% 1
threshold.high <- threshold.high %||% max(barcode_dist$rank)
## Subset the barcode distribution by thresholds
barcode_dist_sub <- barcode_dist[barcode_dist$rank > threshold.low & barcode_dist$rank < threshold.high, ]
## Calculate inflection points
## note: if thresholds are s.t. it produces the same length across both groups,
## aggregate will create a data.frame with x.* columns, where * is the length
## using the same combine approach will yield non-symmetrical results!
whichmin_list <- aggregate(
x = barcode_dist_sub[, 'rawdiff'],
by = list(barcode_dist_sub[, group.column]),
FUN = function(x) {
return(x == min(x))
}
)$x
## workaround for aggregate behavior noted above
if (is.list(x = whichmin_list)) { # uneven lengths
is_inflection <- unlist(x = whichmin_list)
} else if (is.matrix(x = whichmin_list)) { # even lengths
is_inflection <- as.vector(x = t(x = whichmin_list))
}
tmp <- cbind(barcode_dist_sub, is_inflection)
# inflections <- tmp[tmp$is_inflection == TRUE, c(group.column, barcode.column, "rank")]
inflections <- tmp[which(x = tmp$is_inflection), c(group.column, barcode.column, 'rank')]
# Use inflection point for what cells to keep
## use the inflection points to cut the subsetted dist to what to keep
## keep only the barcodes above the inflection points
keep <- unlist(x = lapply(
X = whichmin_list,
FUN = function(x) {
keep <- !x
if (sum(keep) == length(x = keep)) {
return(keep) # prevents bug in case of keeping all cells
}
# toss <- which(keep == FALSE):length(x = keep) # the end cells below knee
toss <- which(x = !keep):length(x = keep)
keep[toss] <- FALSE
return(keep)
}
))
barcode_dist_sub_keep <- barcode_dist_sub[keep, ]
cells_keep <- rownames(x = barcode_dist_sub_keep)
# Bind thresholds to keep track of where they are placed
thresholds <- data.frame(
threshold = c('threshold.low', 'threshold.high'),
rank = c(threshold.low, threshold.high)
)
# Combine relevant info together
## Combine Barcode dist, inflection point, and cells to keep into list
info <- list(
barcode_distribution = barcode_dist,
inflection_points = inflections,
threshold_values = thresholds,
cells_pass = cells_keep
)
# save results into object
Tool(object = object) <- info
return(object)
}
#' Convert a peak matrix to a gene activity matrix
#'
#' This function will take in a peak matrix and an annotation file (gtf) and collapse the peak
#' matrix to a gene activity matrix. It makes the simplifying assumption that all counts in the gene
#' body plus X kb up and or downstream should be attributed to that gene.
#'
#' @param peak.matrix Matrix of peak counts
#' @param annotation.file Path to GTF annotation file
#' @param seq.levels Which seqlevels to keep (corresponds to chromosomes usually)
#' @param include.body Include the gene body?
#' @param upstream Number of bases upstream to consider
#' @param downstream Number of bases downstream to consider
#' @param keep.sparse Leave the matrix as a sparse matrix. Setting this option to
#' TRUE will take much longer but will use less memory. This can be useful if
#' you have a very large matrix that cannot fit into memory when converted to
#' a dense form.
#' @param verbose Print progress/messages
#'
#' @importFrom future nbrOfWorkers
#' @export
#'
CreateGeneActivityMatrix <- function(
peak.matrix,
annotation.file,
seq.levels = c(1:22, "X", "Y"),
include.body = TRUE,
upstream = 2000,
downstream = 0,
keep.sparse = FALSE,
verbose = TRUE
) {
if (!PackageCheck('GenomicRanges', error = FALSE)) {
stop("Please install GenomicRanges from Bioconductor.")
}
if (!PackageCheck('rtracklayer', error = FALSE)) {
stop("Please install rtracklayer from Bioconductor.")
}
# convert peak matrix to GRanges object
peak.df <- rownames(x = peak.matrix)
peak.df <- do.call(what = rbind, args = strsplit(x = gsub(peak.df, pattern = ":", replacement = "-"), split = "-"))
peak.df <- as.data.frame(x = peak.df)
colnames(x = peak.df) <- c("chromosome", 'start', 'end')
peaks.gr <- GenomicRanges::makeGRangesFromDataFrame(df = peak.df)
# if any peaks start at 0, change to 1
# otherwise GenomicRanges::distanceToNearest will not work
BiocGenerics::start(peaks.gr[BiocGenerics::start(peaks.gr) == 0, ]) <- 1
# get annotation file, select genes
gtf <- rtracklayer::import(con = annotation.file)
gtf <- GenomeInfoDb::keepSeqlevels(x = gtf, value = seq.levels, pruning.mode = 'coarse')
# change seqlevelsStyle if not the same
if (!any(GenomeInfoDb::seqlevelsStyle(x = gtf) == GenomeInfoDb::seqlevelsStyle(x = peaks.gr))) {
GenomeInfoDb::seqlevelsStyle(gtf) <- GenomeInfoDb::seqlevelsStyle(peaks.gr)
}
gtf.genes <- gtf[gtf$type == 'gene']
# Extend definition up/downstream
if (include.body) {
gtf.body_prom <- Extend(x = gtf.genes, upstream = upstream, downstream = downstream)
} else {
gtf.body_prom <- SummarizedExperiment::promoters(x = gtf.genes, upstream = upstream, downstream = downstream)
}
gene.distances <- GenomicRanges::distanceToNearest(x = peaks.gr, subject = gtf.body_prom)
keep.overlaps <- gene.distances[rtracklayer::mcols(x = gene.distances)$distance == 0]
peak.ids <- peaks.gr[S4Vectors::queryHits(x = keep.overlaps)]
gene.ids <- gtf.genes[S4Vectors::subjectHits(x = keep.overlaps)]
# Some GTF rows will not have gene_name attribute
# Replace it by gene_id attribute
gene.ids$gene_name[is.na(gene.ids$gene_name)] <- gene.ids$gene_id[is.na(gene.ids$gene_name)]
peak.ids$gene.name <- gene.ids$gene_name
peak.ids <- as.data.frame(x = peak.ids)
peak.ids$peak <- rownames(peak.matrix)[S4Vectors::queryHits(x = keep.overlaps)]
annotations <- peak.ids[, c('peak', 'gene.name')]
colnames(x = annotations) <- c('feature', 'new_feature')
# collapse into expression matrix
if (!keep.sparse) {
peak.matrix <- as(object = peak.matrix, Class = 'matrix')
}
all.features <- unique(x = annotations$new_feature)
if (nbrOfWorkers() > 1) {
mysapply <- future_sapply
} else {
mysapply <- ifelse(test = verbose, yes = pbsapply, no = sapply)
}
newmat.list <- mysapply(X = 1:length(x = all.features), FUN = function(x){
features.use <- annotations[annotations$new_feature == all.features[[x]], ]$feature
submat <- peak.matrix[features.use, ]
if (length(x = features.use) > 1) {
submat <- Matrix::colSums(submat)
}
if (keep.sparse) {
return(as(object = as.matrix(x = submat), Class = 'dgCMatrix'))
} else {
return(as.matrix(x = submat))
}
}, simplify = FALSE)
newmat = do.call(what = cbind, args = newmat.list)
newmat <- t(x = newmat)
rownames(x = newmat) <- all.features
return(as(object = newmat, Class = 'dgCMatrix'))
}
#' Demultiplex samples based on data from cell 'hashing'
#'
#' Assign sample-of-origin for each cell, annotate doublets.
#'
#' @param object Seurat object. Assumes that the hash tag oligo (HTO) data has been added and normalized.
#' @param assay Name of the Hashtag assay (HTO by default)
#' @param positive.quantile The quantile of inferred 'negative' distribution for each hashtag - over which the cell is considered 'positive'. Default is 0.99
#' @param init Initial number of clusters for hashtags. Default is the # of hashtag oligo names + 1 (to account for negatives)
#' @param kfunc Clustering function for initial hashtag grouping. Default is "clara" for fast k-medoids clustering on large applications, also support "kmeans" for kmeans clustering
#' @param nsamples Number of samples to be drawn from the dataset used for clustering, for kfunc = "clara"
#' @param nstarts nstarts value for k-means clustering (for kfunc = "kmeans"). 100 by default
#' @param seed Sets the random seed. If NULL, seed is not set
#' @param verbose Prints the output
#'
#' @return The Seurat object with the following demultiplexed information stored in the meta data:
#' \describe{
#' \item{hash.maxID}{Name of hashtag with the highest signal}
#' \item{hash.secondID}{Name of hashtag with the second highest signal}
#' \item{hash.margin}{The difference between signals for hash.maxID and hash.secondID}
#' \item{classification}{Classification result, with doublets/multiplets named by the top two highest hashtags}
#' \item{classification.global}{Global classification result (singlet, doublet or negative)}
#' \item{hash.ID}{Classification result where doublet IDs are collapsed}
#' }
#'
#' @importFrom cluster clara
#' @importFrom Matrix colSums
#' @importFrom fitdistrplus fitdist
#' @importFrom stats pnbinom kmeans
#'
#' @export
#'
#' @seealso \code{\link{HTOHeatmap}}
#'
#' @examples
#' \dontrun{
#' object <- HTODemux(object)
#' }
#'
HTODemux <- function(
object,
assay = "HTO",
positive.quantile = 0.99,
init = NULL,
nstarts = 100,
kfunc = "clara",
nsamples = 100,
seed = 42,
verbose = TRUE
) {
if (!is.null(x = seed)) {
set.seed(seed = seed)
}
#initial clustering
assay <- assay %||% DefaultAssay(object = object)
data <- GetAssayData(object = object, assay = assay)
counts <- GetAssayData(
object = object,
assay = assay,
slot = 'counts'
)[, colnames(x = object)]
counts <- as.matrix(x = counts)
ncenters <- init %||% (nrow(x = data) + 1)
switch(
EXPR = kfunc,
'kmeans' = {
init.clusters <- kmeans(
x = t(x = GetAssayData(object = object, assay = assay)),
centers = ncenters,
nstart = nstarts
)
#identify positive and negative signals for all HTO
Idents(object = object, cells = names(x = init.clusters$cluster)) <- init.clusters$cluster
},
'clara' = {
#use fast k-medoid clustering
init.clusters <- clara(
x = t(x = GetAssayData(object = object, assay = assay)),
k = ncenters,
samples = nsamples
)
#identify positive and negative signals for all HTO
Idents(object = object, cells = names(x = init.clusters$clustering), drop = TRUE) <- init.clusters$clustering
},
stop("Unknown k-means function ", kfunc, ", please choose from 'kmeans' or 'clara'")
)
#average hto signals per cluster
#work around so we don't average all the RNA levels which takes time
average.expression <- AverageExpression(
object = object,
assays = assay,
verbose = FALSE
)[[assay]]
#checking for any cluster with all zero counts for any barcode
if (sum(average.expression == 0) > 0) {
stop("Cells with zero counts exist as a cluster.")
}
#create a matrix to store classification result
discrete <- GetAssayData(object = object, assay = assay)
discrete[discrete > 0] <- 0
# for each HTO, we will use the minimum cluster for fitting
for (iter in rownames(x = data)) {
values <- counts[iter, colnames(object)]
#commented out if we take all but the top cluster as background
#values_negative=values[setdiff([email protected],WhichCells(object,which.max(average.expression[iter,])))]
values.use <- values[WhichCells(
object = object,
idents = levels(x = Idents(object = object))[[which.min(x = average.expression[iter, ])]]
)]
fit <- suppressWarnings(expr = fitdist(data = values.use, distr = "nbinom"))
cutoff <- as.numeric(x = quantile(x = fit, probs = positive.quantile)$quantiles[1])
discrete[iter, names(x = which(x = values > cutoff))] <- 1
if (verbose) {
message(paste0("Cutoff for ", iter, " : ", cutoff, " reads"))
}
}
# now assign cells to HTO based on discretized values
npositive <- colSums(x = discrete)
classification.global <- npositive
classification.global[npositive == 0] <- "Negative"
classification.global[npositive == 1] <- "Singlet"
classification.global[npositive > 1] <- "Doublet"
donor.id = rownames(x = data)
hash.max <- apply(X = data, MARGIN = 2, FUN = max)
hash.maxID <- apply(X = data, MARGIN = 2, FUN = which.max)
hash.second <- apply(X = data, MARGIN = 2, FUN = MaxN, N = 2)
hash.maxID <- as.character(x = donor.id[sapply(
X = 1:ncol(x = data),
FUN = function(x) {
return(which(x = data[, x] == hash.max[x])[1])
}
)])
hash.secondID <- as.character(x = donor.id[sapply(
X = 1:ncol(x = data),
FUN = function(x) {
return(which(x = data[, x] == hash.second[x])[1])
}
)])
hash.margin <- hash.max - hash.second
doublet_id <- sapply(
X = 1:length(x = hash.maxID),
FUN = function(x) {
return(paste(sort(x = c(hash.maxID[x], hash.secondID[x])), collapse = "_"))
}
)
# doublet_names <- names(x = table(doublet_id))[-1] # Not used
classification <- classification.global
classification[classification.global == "Negative"] <- "Negative"
classification[classification.global == "Singlet"] <- hash.maxID[which(x = classification.global == "Singlet")]
classification[classification.global == "Doublet"] <- doublet_id[which(x = classification.global == "Doublet")]
classification.metadata <- data.frame(
hash.maxID,
hash.secondID,
hash.margin,
classification,
classification.global
)
colnames(x = classification.metadata) <- paste(
assay,
c('maxID', 'secondID', 'margin', 'classification', 'classification.global'),
sep = '_'
)
object <- AddMetaData(object = object, metadata = classification.metadata)
Idents(object) <- paste0(assay, '_classification')
# Idents(object, cells = rownames([email protected][[email protected]$classification.global == "Doublet", ])) <- "Doublet"
doublets <- rownames(x = object[[]])[which(object[[paste0(assay, "_classification.global")]] == "Doublet")]
Idents(object = object, cells = doublets) <- 'Doublet'
# [email protected]$hash.ID <- Idents(object)
object$hash.ID <- Idents(object = object)
return(object)
}
#' Calculate pearson residuals of features not in the scale.data
#'
#' This function calls sctransform::get_residuals.
#'
#' @param object A seurat object
#' @param features Name of features to add into the scale.data
#' @param assay Name of the assay of the seurat object generated by SCTransform
#' @param umi.assay Name of the assay of the seurat object containing UMI matrix and the default is
#' RNA
#' @param clip.range Numeric of length two specifying the min and max values the Pearson residual
#' will be clipped to
#' @param replace.value Recalculate residuals for all features, even if they are already present.
#' Useful if you want to change the clip.range.
#' @param verbose Whether to print messages and progress bars
#'
#' @return Returns a Seurat object containing pearson residuals of added features in its scale.data
#'
#' @importFrom sctransform get_residuals
#'
#' @export
#'
#' @seealso \code{\link[sctransform]{get_residuals}}
#'
#' @examples
#' pbmc_small <- SCTransform(object = pbmc_small, variable.features.n = 20)
#' pbmc_small <- GetResidual(object = pbmc_small, features = c('MS4A1', 'TCL1A'))
#'
GetResidual <- function(
object,
features,
assay = "SCT",
umi.assay = NULL,
clip.range = NULL,
replace.value = FALSE,
verbose = TRUE
) {
if (!IsSCT(assay = object[[assay]])) {
stop(assay, " assay was not generated by SCTransform")
}
umi.assay <- umi.assay %||% Misc(object = object[[assay]], slot = "umi.assay")
umi.assay <- umi.assay %||% "RNA" # for object created in 3.1.1 or earlier, default to RNA
if (replace.value) {
new_features <- features
} else {
new_features <- setdiff(
x = features,
y = rownames(x = GetAssayData(object = object, assay = assay, slot = "scale.data"))
)
}
if (length(x = new_features) == 0) {
if (verbose) {
message("Pearson residuals of input features exist already")
}
} else {
if (is.null(x = Misc(object = object[[assay]], slot = 'vst.set'))) {
vst_out <- Misc(object = object[[assay]], slot = 'vst.out')
# filter cells not in the object but in the SCT model
vst_out$cell_attr <- vst_out$cell_attr[Cells(x = object), ]
vst_out$cells_step1 <- intersect(x = vst_out$cells_step1, y = Cells(x = object))
object <- GetResidualVstOut(
object = object,
assay = assay,
umi.assay = umi.assay,
new_features = new_features,
vst_out = vst_out,
clip.range = clip.range,
verbose = verbose
)
} else {
# Calculate Pearson Residual from integrated object SCT assay
vst.set <- Misc(object = object[[assay]], slot = 'vst.set')
scale.data <- GetAssayData(
object = object,
assay = assay,
slot = "scale.data"
)
vst_set_genes <- sapply(1:length(vst.set), function(x) rownames(vst.set[[x]]$model_pars_fit))
vst_set_genes <- Reduce(intersect, vst_set_genes)
diff_features <- setdiff(
x = new_features,
y = vst_set_genes
)
if (length(x = diff_features) !=0) {
warning(
"The following ", length(x = diff_features),
" features do not exist in all SCT models: ",
paste(diff_features, collapse = " ")
)
}
new_features <- intersect(
x = new_features,
y = vst_set_genes
)
if (length(new_features) != 0){
object <- SetAssayData(
object = object,
assay = assay,
slot = "scale.data",
new.data = scale.data[!rownames(x = scale.data) %in% new_features, , drop = FALSE]
)
new.scale.data <- matrix(nrow = length(new_features), ncol = 0)
rownames(x = new.scale.data) <- new_features
for (v in 1:length(x = vst.set)) {
vst_out <- vst.set[[v]]
# confirm that cells from SCT model also exist in the integrated object
cells.v <- intersect(x = rownames(x = vst_out$cell_attr), y = Cells(x = object))
vst_out$cell_attr <- vst_out$cell_attr[cells.v, ]
vst_out$cells_step1 <- intersect(x = vst_out$cells_step1, y = cells.v)
object.v <- subset(x = object, cells = cells.v)
object.v <- GetResidualVstOut(
object = object.v,
assay = assay,
umi.assay = umi.assay[[v]],
new_features = new_features,
vst_out = vst_out,
clip.range = clip.range,
verbose = verbose
)
new.scale.data <- cbind(
new.scale.data,
GetAssayData(object = object.v, assay = assay, slot ="scale.data" )[new_features, , drop = FALSE]
)
}
object <- SetAssayData(
object = object,
assay = assay,
slot = "scale.data",
new.data = rbind(
GetAssayData(object = object, slot = 'scale.data', assay = assay),
new.scale.data
)
)
}
}
}
return(object)
}
#' Normalize raw data
#'
#' Normalize count data per cell and transform to log scale
#'
#' @param data Matrix with the raw count data
#' @param scale.factor Scale the data. Default is 1e4
#' @param verbose Print progress
#'
#' @return Returns a matrix with the normalize and log transformed data
#'
#' @import Matrix
#' @importFrom methods as
#'
#' @export
#'
#' @examples
#' mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
#' mat
#' mat_norm <- LogNormalize(data = mat)
#' mat_norm
#'
LogNormalize <- function(data, scale.factor = 1e4, verbose = TRUE) {
if (is.data.frame(x = data)) {
data <- as.matrix(x = data)
}
if (!inherits(x = data, what = 'dgCMatrix')) {
data <- as(object = data, Class = "dgCMatrix")
}
# call Rcpp function to normalize
if (verbose) {
cat("Performing log-normalization\n", file = stderr())
}
norm.data <- LogNorm(data, scale_factor = scale.factor, display_progress = verbose)
colnames(x = norm.data) <- colnames(x = data)
rownames(x = norm.data) <- rownames(x = data)
return(norm.data)
}
#' Demultiplex samples based on classification method from MULTI-seq (McGinnis et al., bioRxiv 2018)
#'
#' Identify singlets, doublets and negative cells from multiplexing experiments. Annotate singlets by tags.
#'
#' @param object Seurat object. Assumes that the specified assay data has been added
#' @param assay Name of the multiplexing assay (HTO by default)
#' @param quantile The quantile to use for classification
#' @param autoThresh Whether to perform automated threshold finding to define the best quantile. Default is FALSE
#' @param maxiter Maximum number of iterations if autoThresh = TRUE. Default is 5
#' @param qrange A range of possible quantile values to try if autoThresh = TRUE
#' @param verbose Prints the output
#'
#' @return A Seurat object with demultiplexing results stored at \code{object$MULTI_ID}
#'
#' @import Matrix
#'
#' @export
#'
#' @references \url{https://www.biorxiv.org/content/early/2018/08/08/387241}
#'
#' @examples
#' \dontrun{
#' object <- MULTIseqDemux(object)
#' }
#'
MULTIseqDemux <- function(
object,
assay = "HTO",
quantile = 0.7,
autoThresh = FALSE,
maxiter = 5,
qrange = seq(from = 0.1, to = 0.9, by = 0.05),
verbose = TRUE
) {
assay <- assay %||% DefaultAssay(object = object)
multi_data_norm <- t(x = GetAssayData(
object = object,
slot = "data",
assay = assay
))
if (autoThresh) {
iter <- 1
negatives <- c()
neg.vector <- c()
while (iter <= maxiter) {
# Iterate over q values to find ideal barcode thresholding results by maximizing singlet classifications
bar.table_sweep.list <- list()
n <- 0
for (q in qrange) {
n <- n + 1
# Generate list of singlet/doublet/negative classifications across q sweep
bar.table_sweep.list[[n]] <- ClassifyCells(data = multi_data_norm, q = q)
names(x = bar.table_sweep.list)[n] <- paste0("q=" , q)
}
# Determine which q values results in the highest pSinglet
res_round <- FindThresh(call.list = bar.table_sweep.list)$res
res.use <- res_round[res_round$Subset == "pSinglet", ]
q.use <- res.use[which.max(res.use$Proportion),"q"]
if (verbose) {
message("Iteration ", iter)
message("Using quantile ", q.use)
}
round.calls <- ClassifyCells(data = multi_data_norm, q = q.use)
#remove negative cells
neg.cells <- names(x = round.calls)[which(x = round.calls == "Negative")]
neg.vector <- c(neg.vector, rep(x = "Negative", length(x = neg.cells)))
negatives <- c(negatives, neg.cells)
if (length(x = neg.cells) == 0) {
break
}
multi_data_norm <- multi_data_norm[-which(x = rownames(x = multi_data_norm) %in% neg.cells), ]
iter <- iter + 1
}
names(x = neg.vector) <- negatives
demux_result <- c(round.calls,neg.vector)
demux_result <- demux_result[rownames(x = object[[]])]
} else{
demux_result <- ClassifyCells(data = multi_data_norm, q = quantile)
}
demux_result <- demux_result[rownames(x = object[[]])]
object[['MULTI_ID']] <- factor(x = demux_result)
Idents(object = object) <- "MULTI_ID"
bcs <- colnames(x = multi_data_norm)
bc.max <- bcs[apply(X = multi_data_norm, MARGIN = 1, FUN = which.max)]
bc.second <- bcs[unlist(x = apply(
X = multi_data_norm,
MARGIN = 1,
FUN = function(x) {
return(which(x == MaxN(x)))
}
))]
doublet.names <- unlist(x = lapply(
X = 1:length(x = bc.max),
FUN = function(x) {
return(paste(sort(x = c(bc.max[x], bc.second[x])), collapse = "_"))
}
))
doublet.id <- which(x = demux_result == "Doublet")
MULTI_classification <- as.character(object$MULTI_ID)
MULTI_classification[doublet.id] <- doublet.names[doublet.id]
object$MULTI_classification <- factor(x = MULTI_classification)
return(object)
}
#' Load in data from Alevin pipeline
#'
#' Enables easy loading of csv format matrix provided by Alevin
#' ran with `--dumpCsvCounts` flags.
#'
#' @param base.path Directory containing the alevin/quant_mat*
#' files provided by Alevin.
#'
#' @return Returns a matrix with rows and columns labeled
#'
#' @importFrom utils read.csv read.delim
#' @export
#'
#' @author Avi Srivastava
#'
#' @examples
#' \dontrun{
#' data_dir <- 'path/to/output/directory'
#' list.files(data_dir) # Should show alevin/quants_mat* files
#' expression_matrix <- ReadAlevinCsv(base.path = data_dir)
#' seurat_object = CreateSeuratObject(counts = expression_matrix)
#' }
#'
ReadAlevinCsv <- function(base.path) {
if (!dir.exists(base.path)) {
stop("Directory provided does not exist")
}
barcode.loc <- file.path(base.path, "alevin", "quants_mat_rows.txt")
gene.loc <- file.path(base.path, "alevin", "quants_mat_cols.txt")
matrix.loc <- file.path( base.path, "alevin", "quants_mat.csv" )
if (!file.exists(barcode.loc)) {
stop("Barcode file missing")
}
if (!file.exists(gene.loc)) {
stop("Gene name file missing")
}
if (!file.exists(matrix.loc)) {
stop("Expression matrix file missing")
}
matrix <- as.matrix(x = read.csv(file = matrix.loc, header = FALSE))
matrix <- t(x = matrix[, 1:ncol(x = matrix) - 1])
cell.names <- readLines(con = barcode.loc)
gene.names <- readLines(con = gene.loc)
colnames(x = matrix) <- cell.names
rownames(x = matrix) <- gene.names
matrix[is.na(x = matrix)] <- 0
return(matrix)
}
#' Load in data from Alevin pipeline
#'
#' Enables easy loading of binary format matrix provided by Alevin
#'
#' @param base.path Directory containing the alevin/quant_mat*
#' files provided by Alevin.
#'
#' @return Returns a matrix with rows and columns labeled
#'
#' @export
#'
#' @author Avi Srivastava
#'
#' @examples
#' \dontrun{
#' data_dir <- 'path/to/output/directory'
#' list.files(data_dir) # Should show alevin/quants_mat* files
#' expression_matrix <- ReadAlevin(base.path = data_dir)
#' seurat_object = CreateSeuratObject(counts = expression_matrix)
#' }
#'
ReadAlevin <- function(base.path) {
if (!dir.exists(base.path)) {
stop("Directory provided does not exist")
}
barcode.loc <- file.path(base.path, "alevin", "quants_mat_rows.txt")
gene.loc <- file.path(base.path, "alevin", "quants_mat_cols.txt")
matrix.loc <- file.path(base.path, "alevin", "quants_mat.gz")
if (!file.exists(barcode.loc)) {
stop("Barcode file missing")
}
if (!file.exists(gene.loc)) {
stop("Gene name file missing")
}
if (!file.exists(matrix.loc)) {
stop("Expression matrix file missing")
}
cell.names <- readLines(con = barcode.loc)
gene.names <- readLines(con = gene.loc)
num.cells <- length(x = cell.names)
num.genes <- length(x = gene.names)
out.matrix <- matrix(data = NA, nrow = num.genes, ncol = num.cells)
con <- gzcon(con = file(description = matrix.loc, open = "rb"))
total.molecules <- 0.0
for (n in seq_len(length.out = num.cells)) {
out.matrix[, n] <- readBin(
con = con,
what = double(),
endian = "little",
n = num.genes
)
total.molecules <- total.molecules + sum(out.matrix[, n])
}
colnames(x = out.matrix) <- cell.names
rownames(x = out.matrix) <- gene.names
message("Found total ", total.molecules, " molecules")
return(out.matrix)
}
#' Load in data from 10X
#'
#' Enables easy loading of sparse data matrices provided by 10X genomics.
#'
#' @param data.dir Directory containing the matrix.mtx, genes.tsv (or features.tsv), and barcodes.tsv
#' files provided by 10X. A vector or named vector can be given in order to load
#' several data directories. If a named vector is given, the cell barcode names
#' will be prefixed with the name.
#' @param gene.column Specify which column of genes.tsv or features.tsv to use for gene names; default is 2
#' @param unique.features Make feature names unique (default TRUE)
#' @param strip.suffix Remove trailing "-1" if present in all cell barcodes.
#'
#' @return If features.csv indicates the data has multiple data types, a list
#' containing a sparse matrix of the data from each type will be returned.
#' Otherwise a sparse matrix containing the expression data will be returned.
#'
#' @importFrom Matrix readMM
#'
#' @export
#'
#' @examples
#' \dontrun{
#' # For output from CellRanger < 3.0
#' data_dir <- 'path/to/data/directory'
#' list.files(data_dir) # Should show barcodes.tsv, genes.tsv, and matrix.mtx
#' expression_matrix <- Read10X(data.dir = data_dir)
#' seurat_object = CreateSeuratObject(counts = expression_matrix)
#'
#' # For output from CellRanger >= 3.0 with multiple data types
#' data_dir <- 'path/to/data/directory'
#' list.files(data_dir) # Should show barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz
#' data <- Read10X(data.dir = data_dir)
#' seurat_object = CreateSeuratObject(counts = data$`Gene Expression`)
#' seurat_object[['Protein']] = CreateAssayObject(counts = data$`Antibody Capture`)
#' }
#'
Read10X <- function(
data.dir = NULL,
gene.column = 2,
unique.features = TRUE,
strip.suffix = FALSE
) {
full.data <- list()
for (i in seq_along(along.with = data.dir)) {
run <- data.dir[i]
if (!dir.exists(paths = run)) {
stop("Directory provided does not exist")
}
barcode.loc <- file.path(run, 'barcodes.tsv')
gene.loc <- file.path(run, 'genes.tsv')
features.loc <- file.path(run, 'features.tsv.gz')
matrix.loc <- file.path(run, 'matrix.mtx')
# Flag to indicate if this data is from CellRanger >= 3.0
pre_ver_3 <- file.exists(gene.loc)
if (!pre_ver_3) {
addgz <- function(s) {
return(paste0(s, ".gz"))
}
barcode.loc <- addgz(s = barcode.loc)
matrix.loc <- addgz(s = matrix.loc)
}
if (!file.exists(barcode.loc)) {
stop("Barcode file missing. Expecting ", basename(path = barcode.loc))
}
if (!pre_ver_3 && !file.exists(features.loc) ) {
stop("Gene name or features file missing. Expecting ", basename(path = features.loc))
}
if (!file.exists(matrix.loc)) {
stop("Expression matrix file missing. Expecting ", basename(path = matrix.loc))
}
data <- readMM(file = matrix.loc)
cell.names <- readLines(barcode.loc)
if (all(grepl(pattern = "\\-1$", x = cell.names)) & strip.suffix) {
cell.names <- as.vector(x = as.character(x = sapply(
X = cell.names,
FUN = ExtractField,
field = 1,
delim = "-"
)))
}
if (is.null(x = names(x = data.dir))) {
if (i < 2) {
colnames(x = data) <- cell.names
} else {
colnames(x = data) <- paste0(i, "_", cell.names)
}
} else {
colnames(x = data) <- paste0(names(x = data.dir)[i], "_", cell.names)
}
feature.names <- read.delim(
file = ifelse(test = pre_ver_3, yes = gene.loc, no = features.loc),
header = FALSE,
stringsAsFactors = FALSE
)
if (any(is.na(x = feature.names[, gene.column]))) {
warning(
'Some features names are NA. Replacing NA names with ID from the opposite column requested',
call. = FALSE,
immediate. = TRUE
)
na.features <- which(x = is.na(x = feature.names[, gene.column]))
replacement.column <- ifelse(test = gene.column == 2, yes = 1, no = 2)
feature.names[na.features, gene.column] <- feature.names[na.features, replacement.column]
}
if (unique.features) {
fcols = ncol(x = feature.names)
if (fcols < gene.column) {
stop(paste0("gene.column was set to ", gene.column,
" but feature.tsv.gz (or genes.tsv) only has ", fcols, " columns.",
" Try setting the gene.column argument to a value <= to ", fcols, "."))
}
rownames(x = data) <- make.unique(names = feature.names[, gene.column])
}
# In cell ranger 3.0, a third column specifying the type of data was added
# and we will return each type of data as a separate matrix
if (ncol(x = feature.names) > 2) {
data_types <- factor(x = feature.names$V3)
lvls <- levels(x = data_types)
if (length(x = lvls) > 1 && length(x = full.data) == 0) {
message("10X data contains more than one type and is being returned as a list containing matrices of each type.")
}
expr_name <- "Gene Expression"
if (expr_name %in% lvls) { # Return Gene Expression first
lvls <- c(expr_name, lvls[-which(x = lvls == expr_name)])
}
data <- lapply(
X = lvls,
FUN = function(l) {
return(data[data_types == l, , drop = FALSE])
}
)
names(x = data) <- lvls
} else{
data <- list(data)
}
full.data[[length(x = full.data) + 1]] <- data
}
# Combine all the data from different directories into one big matrix, note this
# assumes that all data directories essentially have the same features files
list_of_data <- list()
for (j in 1:length(x = full.data[[1]])) {
list_of_data[[j]] <- do.call(cbind, lapply(X = full.data, FUN = `[[`, j))
# Fix for Issue #913
list_of_data[[j]] <- as(object = list_of_data[[j]], Class = "dgCMatrix")
}
names(x = list_of_data) <- names(x = full.data[[1]])
# If multiple features, will return a list, otherwise
# a matrix.
if (length(x = list_of_data) == 1) {
return(list_of_data[[1]])
} else {
return(list_of_data)
}
}
#' Read 10X hdf5 file
#'
#' Read count matrix from 10X CellRanger hdf5 file.
#' This can be used to read both scATAC-seq and scRNA-seq matrices.
#'
#' @param filename Path to h5 file
#' @param use.names Label row names with feature names rather than ID numbers.
#' @param unique.features Make feature names unique (default TRUE)
#'
#' @return Returns a sparse matrix with rows and columns labeled. If multiple
#' genomes are present, returns a list of sparse matrices (one per genome).
#'
#' @export
#'
Read10X_h5 <- function(filename, use.names = TRUE, unique.features = TRUE) {
if (!requireNamespace('hdf5r', quietly = TRUE)) {
stop("Please install hdf5r to read HDF5 files")
}
if (!file.exists(filename)) {
stop("File not found")
}