mfranke-2
commented
9 months ago Hi, I have also come across this error - it's a result of the seurat v5 updates ("counts" are now stored in "layers"). My current workaround is to edit some of the DoubletFinder functions. Redefining the functions by running the below worked for me (lines that I changed are marked by a comment):
paramSweep_v3 <- function (seu, PCs = 1:10, sct = FALSE, num.cores = 1)
{
require(Seurat)
require(fields)
pK <- c(5e-04, 0.001, 0.005, seq(0.01, 0.3, by = 0.01))
pN <- seq(0.05, 0.3, by = 0.05)
min.cells <- round(nrow(seu@meta.data)/(1 - 0.05) - nrow(seu@meta.data))
pK.test <- round(pK * min.cells)
pK <- pK[which(pK.test >= 1)]
orig.commands <- seu@commands
if (nrow(seu@meta.data) > 10000) {
real.cells <- rownames(seu@meta.data)[sample(1:nrow(seu@meta.data),
10000, replace = FALSE)]
data <- seu@assays$RNA@layers$counts ## M
colnames(data) <- colnames(seu) ## M
rownames(data) <- rownames(seu) ## M
data <- data[ ,real.cells] ## M
n.real.cells <- ncol(data)
}
if (nrow(seu@meta.data) <= 10000) {
real.cells <- rownames(seu@meta.data)
data <- seu@assays$RNA@layers$counts ## M
colnames(data) <- colnames(seu) ## M
rownames(data) <- rownames(seu) ## M
n.real.cells <- ncol(data)
}
if (num.cores > 1) {
require(parallel)
cl <- makeCluster(num.cores)
output2 <- mclapply(as.list(1:length(pN)), FUN = parallel_paramSweep_v3,
n.real.cells, real.cells, pK, pN, data, orig.commands,
PCs, sct, mc.cores = num.cores)
stopCluster(cl)
}
else {
output2 <- lapply(as.list(1:length(pN)), FUN = parallel_paramSweep_v3,
n.real.cells, real.cells, pK, pN, data, orig.commands,
PCs, sct)
}
sweep.res.list <- list()
list.ind <- 0
for (i in 1:length(output2)) {
for (j in 1:length(output2[[i]])) {
list.ind <- list.ind + 1
sweep.res.list[[list.ind]] <- output2[[i]][[j]]
}
}
name.vec <- NULL
for (j in 1:length(pN)) {
name.vec <- c(name.vec, paste("pN", pN[j], "pK", pK,
sep = "_"))
}
names(sweep.res.list) <- name.vec
return(sweep.res.list)
}
parallel_paramSweep_v3 <- function (n, n.real.cells, real.cells, pK, pN, data, orig.commands,
PCs, sct)
{
sweep.res.list = list()
list.ind = 0
print(paste("Creating artificial doublets for pN = ", pN[n] *
100, "%", sep = ""))
n_doublets <- round(n.real.cells/(1 - pN[n]) - n.real.cells)
real.cells1 <- sample(real.cells, n_doublets, replace = TRUE)
real.cells2 <- sample(real.cells, n_doublets, replace = TRUE)
doublets <- (data[, real.cells1] + data[, real.cells2])/2
colnames(doublets) <- paste("X", 1:n_doublets, sep = "")
data_wdoublets <- cbind(data, doublets)
if (sct == FALSE) {
print("Creating Seurat object...")
seu_wdoublets <- CreateSeuratObject(counts = data_wdoublets)
print("Normalizing Seurat object...")
seu_wdoublets <- NormalizeData(seu_wdoublets, normalization.method = orig.commands$NormalizeData.RNA@params$normalization.method,
scale.factor = orig.commands$NormalizeData.RNA@params$scale.factor,
margin = orig.commands$NormalizeData.RNA@params$margin)
print("Finding variable genes...")
seu_wdoublets <- FindVariableFeatures(seu_wdoublets,
selection.method = orig.commands$FindVariableFeatures.RNA$selection.method,
loess.span = orig.commands$FindVariableFeatures.RNA$loess.span,
clip.max = orig.commands$FindVariableFeatures.RNA$clip.max,
mean.function = orig.commands$FindVariableFeatures.RNA$mean.function,
dispersion.function = orig.commands$FindVariableFeatures.RNA$dispersion.function,
num.bin = orig.commands$FindVariableFeatures.RNA$num.bin,
binning.method = orig.commands$FindVariableFeatures.RNA$binning.method,
nfeatures = orig.commands$FindVariableFeatures.RNA$nfeatures,
mean.cutoff = orig.commands$FindVariableFeatures.RNA$mean.cutoff,
dispersion.cutoff = orig.commands$FindVariableFeatures.RNA$dispersion.cutoff)
print("Scaling data...")
seu_wdoublets <- ScaleData(seu_wdoublets, features = orig.commands$ScaleData.RNA$features,
model.use = orig.commands$ScaleData.RNA$model.use,
do.scale = orig.commands$ScaleData.RNA$do.scale,
do.center = orig.commands$ScaleData.RNA$do.center,
scale.max = orig.commands$ScaleData.RNA$scale.max,
block.size = orig.commands$ScaleData.RNA$block.size,
min.cells.to.block = orig.commands$ScaleData.RNA$min.cells.to.block)
print("Running PCA...")
seu_wdoublets <- RunPCA(seu_wdoublets, features = orig.commands$ScaleData.RNA$features,
npcs = length(PCs), rev.pca = orig.commands$RunPCA.RNA$rev.pca,
weight.by.var = orig.commands$RunPCA.RNA$weight.by.var,
verbose = FALSE)
}
if (sct == TRUE) {
require(sctransform)
print("Creating Seurat object...")
seu_wdoublets <- CreateSeuratObject(counts = data_wdoublets)
print("Running SCTransform...")
seu_wdoublets <- SCTransform(seu_wdoublets)
print("Running PCA...")
seu_wdoublets <- RunPCA(seu_wdoublets, npcs = length(PCs))
}
print("Calculating PC distance matrix...")
nCells <- nrow(seu_wdoublets@meta.data)
pca.coord <- seu_wdoublets@reductions$pca@cell.embeddings[,
PCs]
rm(seu_wdoublets)
gc()
dist.mat <- fields::rdist(pca.coord)[, 1:n.real.cells]
print("Defining neighborhoods...")
for (i in 1:n.real.cells) {
dist.mat[, i] <- order(dist.mat[, i])
}
ind <- round(nCells * max(pK)) + 5
dist.mat <- dist.mat[1:ind, ]
print("Computing pANN across all pK...")
for (k in 1:length(pK)) {
print(paste("pK = ", pK[k], "...", sep = ""))
pk.temp <- round(nCells * pK[k])
pANN <- as.data.frame(matrix(0L, nrow = n.real.cells,
ncol = 1))
colnames(pANN) <- "pANN"
rownames(pANN) <- real.cells
list.ind <- list.ind + 1
for (i in 1:n.real.cells) {
neighbors <- dist.mat[2:(pk.temp + 1), i]
pANN$pANN[i] <- length(which(neighbors > n.real.cells))/pk.temp
}
sweep.res.list[[list.ind]] <- pANN
}
return(sweep.res.list)
}
doubletFinder_v3 <- function (seu, PCs, pN = 0.25, pK, nExp, reuse.pANN = FALSE,
sct = FALSE, annotations = NULL)
{
require(Seurat)
require(fields)
require(KernSmooth)
if (reuse.pANN != FALSE) {
pANN.old <- seu@meta.data[, reuse.pANN]
classifications <- rep("Singlet", length(pANN.old))
classifications[order(pANN.old, decreasing = TRUE)[1:nExp]] <- "Doublet"
seu@meta.data[, paste("DF.classifications", pN, pK, nExp,
sep = "_")] <- classifications
return(seu)
}
if (reuse.pANN == FALSE) {
real.cells <- rownames(seu@meta.data)
data <- seu@assays$RNA@layers$counts ## M
colnames(data) <- colnames(seu) ## M
rownames(data) <- rownames(seu) ## M
data <- data[, real.cells] ## M
n_real.cells <- length(real.cells)
n_doublets <- round(n_real.cells/(1 - pN) - n_real.cells)
print(paste("Creating", n_doublets, "artificial doublets...",
sep = " "))
real.cells1 <- sample(real.cells, n_doublets, replace = TRUE)
real.cells2 <- sample(real.cells, n_doublets, replace = TRUE)
doublets <- (data[, real.cells1] + data[, real.cells2])/2
colnames(doublets) <- paste("X", 1:n_doublets, sep = "")
data_wdoublets <- cbind(data, doublets)
if (!is.null(annotations)) {
stopifnot(typeof(annotations) == "character")
stopifnot(length(annotations) == length(Cells(seu)))
stopifnot(!any(is.na(annotations)))
annotations <- factor(annotations)
names(annotations) <- Cells(seu)
doublet_types1 <- annotations[real.cells1]
doublet_types2 <- annotations[real.cells2]
}
orig.commands <- seu@commands
if (sct == FALSE) {
print("Creating Seurat object...")
seu_wdoublets <- CreateSeuratObject(counts = data_wdoublets)
print("Normalizing Seurat object...")
seu_wdoublets <- NormalizeData(seu_wdoublets, normalization.method = orig.commands$NormalizeData.RNA@params$normalization.method,
scale.factor = orig.commands$NormalizeData.RNA@params$scale.factor,
margin = orig.commands$NormalizeData.RNA@params$margin)
print("Finding variable genes...")
seu_wdoublets <- FindVariableFeatures(seu_wdoublets,
selection.method = orig.commands$FindVariableFeatures.RNA$selection.method,
loess.span = orig.commands$FindVariableFeatures.RNA$loess.span,
clip.max = orig.commands$FindVariableFeatures.RNA$clip.max,
mean.function = orig.commands$FindVariableFeatures.RNA$mean.function,
dispersion.function = orig.commands$FindVariableFeatures.RNA$dispersion.function,
num.bin = orig.commands$FindVariableFeatures.RNA$num.bin,
binning.method = orig.commands$FindVariableFeatures.RNA$binning.method,
nfeatures = orig.commands$FindVariableFeatures.RNA$nfeatures,
mean.cutoff = orig.commands$FindVariableFeatures.RNA$mean.cutoff,
dispersion.cutoff = orig.commands$FindVariableFeatures.RNA$dispersion.cutoff)
print("Scaling data...")
seu_wdoublets <- ScaleData(seu_wdoublets, features = orig.commands$ScaleData.RNA$features,
model.use = orig.commands$ScaleData.RNA$model.use,
do.scale = orig.commands$ScaleData.RNA$do.scale,
do.center = orig.commands$ScaleData.RNA$do.center,
scale.max = orig.commands$ScaleData.RNA$scale.max,
block.size = orig.commands$ScaleData.RNA$block.size,
min.cells.to.block = orig.commands$ScaleData.RNA$min.cells.to.block)
print("Running PCA...")
seu_wdoublets <- RunPCA(seu_wdoublets, features = orig.commands$ScaleData.RNA$features,
npcs = length(PCs), rev.pca = orig.commands$RunPCA.RNA$rev.pca,
weight.by.var = orig.commands$RunPCA.RNA$weight.by.var,
verbose = FALSE)
pca.coord <- seu_wdoublets@reductions$pca@cell.embeddings[,
PCs]
cell.names <- rownames(seu_wdoublets@meta.data)
nCells <- length(cell.names)
rm(seu_wdoublets)
gc()
}
if (sct == TRUE) {
require(sctransform)
print("Creating Seurat object...")
seu_wdoublets <- CreateSeuratObject(counts = data_wdoublets)
print("Running SCTransform...")
seu_wdoublets <- SCTransform(seu_wdoublets)
print("Running PCA...")
seu_wdoublets <- RunPCA(seu_wdoublets, npcs = length(PCs))
pca.coord <- seu_wdoublets@reductions$pca@cell.embeddings[,
PCs]
cell.names <- rownames(seu_wdoublets@meta.data)
nCells <- length(cell.names)
rm(seu_wdoublets)
gc()
}
print("Calculating PC distance matrix...")
dist.mat <- fields::rdist(pca.coord)
print("Computing pANN...")
pANN <- as.data.frame(matrix(0L, nrow = n_real.cells,
ncol = 1))
if (!is.null(annotations)) {
neighbor_types <- as.data.frame(matrix(0L, nrow = n_real.cells,
ncol = length(levels(doublet_types1))))
}
rownames(pANN) <- real.cells
colnames(pANN) <- "pANN"
k <- round(nCells * pK)
for (i in 1:n_real.cells) {
neighbors <- order(dist.mat[, i])
neighbors <- neighbors[2:(k + 1)]
pANN$pANN[i] <- length(which(neighbors > n_real.cells))/k
if (!is.null(annotations)) {
for (ct in unique(annotations)) {
neighbors_that_are_doublets = neighbors[neighbors >
n_real.cells]
if (length(neighbors_that_are_doublets) > 0) {
neighbor_types[i, ] <- table(doublet_types1[neighbors_that_are_doublets -
n_real.cells]) + table(doublet_types2[neighbors_that_are_doublets -
n_real.cells])
neighbor_types[i, ] <- neighbor_types[i,
]/sum(neighbor_types[i, ])
}
else {
neighbor_types[i, ] <- NA
}
}
}
}
print("Classifying doublets..")
classifications <- rep("Singlet", n_real.cells)
classifications[order(pANN$pANN[1:n_real.cells], decreasing = TRUE)[1:nExp]] <- "Doublet"
seu@meta.data[, paste("pANN", pN, pK, nExp, sep = "_")] <- pANN[rownames(seu@meta.data),
1]
seu@meta.data[, paste("DF.classifications", pN, pK, nExp,
sep = "_")] <- classifications
if (!is.null(annotations)) {
colnames(neighbor_types) = levels(doublet_types1)
for (ct in levels(doublet_types1)) {
seu@meta.data[, paste("DF.doublet.contributors",
pN, pK, nExp, ct, sep = "_")] <- neighbor_types[,
ct]
}
}
return(seu)
}
}
12231366ostun
genejockey33000
Hello, sorry for bother,
when I run
I encounter the following error:
and the package versions of Seurat and doubletfinder are :
Do you have any insights on how I could address this issue?
Thank you so much.