add.ident = NULL, slot = "data", use.scale = FALSE, use.counts = FALSE,

verbose = TRUE, ...)

{

`%||%` <- function(lhs, rhs) {

if (!is.null(x = lhs)) {

return(lhs)

} else {

return(rhs)

}}

if (use.scale) {

.Deprecated(msg = "'use.scale' is a deprecated argument, please use the 'slot' argument instead")

slot <- "scale.data"

}

if (use.counts) {

.Deprecated(msg = "'use.counts' is a deprecated argument, please use the 'slot' argument instead")

if (use.scale) {

warning("Both 'use.scale' and 'use.counts' were set; using counts",

call. = FALSE, immediate. = TRUE)

}

slot <- "counts"

}

fxn.sum <- switch(EXPR = slot, data = function(x) {

return(sum(x = expm1(x = x)))

}, sum)

object.assays <- Seurat:::FilterObjects(object = object, classes.keep = "Assay")

assays <- assays %||% object.assays

ident.orig <- Idents(object = object)

orig.levels <- levels(x = Idents(object = object))

ident.new <- c()

if (!all(assays %in% object.assays)) {

assays <- assays[assays %in% object.assays]

if (length(assays) == 0) {

stop("None of the requested assays are present in the object")

}

else {

warning("Requested assays that do not exist in object. Proceeding with existing assays only.")

}

}

if (!is.null(x = add.ident)) {

new.data <- Seurat:::FetchData(object = object, vars = add.ident)

new.ident <- paste(Idents(object)[rownames(x = new.data)],

new.data[, 1], sep = "_")

Idents(object, cells = rownames(new.data)) <- new.ident

}

data.return <- list()

for (i in 1:length(x = assays)) {

data.use <- GetAssayData(object = object, assay = assays[i],

slot = slot)

features.assay <- features

if (length(x = intersect(x = features, y = rownames(x = data.use))) <

1) {

features.assay <- rownames(x = data.use)

}

data.all <- data.frame(row.names = features.assay)

for (j in levels(x = Idents(object))) {

temp.cells <- WhichCells(object = object, idents = j)

features.assay <- unique(x = intersect(x = features.assay,

y = rownames(x = data.use)))

if (length(x = temp.cells) == 1) {

data.temp <- (data.use[features.assay, temp.cells])

if (slot == "data") {

data.temp <- expm1(x = data.temp)

}

}

if (length(x = temp.cells) > 1) {

data.temp <- apply(X = data.use[features.assay,

temp.cells, drop = FALSE], MARGIN = 1, FUN = fxn.sum)

}

data.all <- cbind(data.all, data.temp)

colnames(x = data.all)[ncol(x = data.all)] <- j

if (verbose) {

message(paste("Finished summing", assays[i],

"for cluster", j))

}

if (i == 1) {

ident.new <- c(ident.new, as.character(x = ident.orig[temp.cells[1]]))

}

}

names(x = ident.new) <- levels(x = Idents(object))

data.return[[i]] <- data.all

names(x = data.return)[i] <- assays[[i]]

}

if (return.seurat) {

toRet <- CreateSeuratObject(counts = data.return[[1]],

project = "Average", assay = names(x = data.return)[1],

...)

if (length(x = data.return) > 1) {

for (i in 2:length(x = data.return)) {

toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = data.return[[i]])

}

}

if (DefaultAssay(object = object) %in% names(x = data.return)) {

DefaultAssay(object = toRet) <- DefaultAssay(object = object)

}

Idents(toRet, cells = colnames(x = toRet)) <- ident.new[colnames(x = toRet)]

Idents(object = toRet) <- factor(x = Idents(object = toRet),

levels = as.character(x = orig.levels), ordered = TRUE)

toRet <- NormalizeData(object = toRet, verbose = verbose)

toRet <- ScaleData(object = toRet, verbose = verbose)

return(toRet)

}

else {

return(data.return)

}