diff --git a/R/lun-simulate.R b/R/lun-simulate.R
index abeafcb3d851a0d972bdc09c8ff2fce39de1b0f3..8ced43ea7aa7cbc1026282d84f43633961b5e21d 100644
--- a/R/lun-simulate.R
+++ b/R/lun-simulate.R
@@ -63,6 +63,9 @@ lunSimulate <- function(params = newLunParams(), verbose = TRUE, ...) {
de.upFC <- getParam(params, "de.upFC")
de.downFC <- getParam(params, "de.downFC")
+ cell.names <- paste0("Cell", seq_len(nCells))
+ gene.names <- paste0("Gene", seq_len(nGenes))
+
if (verbose) {message("Simulating means...")}
gene.means <- rgamma(nGenes, shape = mean.shape, rate = mean.rate)
@@ -99,6 +102,8 @@ lunSimulate <- function(params = newLunParams(), verbose = TRUE, ...) {
cell.facs <- unlist(cell.facs)
groups <- unlist(groups)
}
+ colnames(cell.means) <- cell.names
+ rownames(cell.means) <- gene.names
if (verbose) {message("Simulating counts...")}
counts <- matrix(rnbinom(nGenes * nCells, mu = cell.means,
@@ -106,32 +111,31 @@ lunSimulate <- function(params = newLunParams(), verbose = TRUE, ...) {
nrow = nGenes, ncol = nCells)
if (verbose) {message("Creating final SCESet...")}
- cell.names <- paste0("Cell", seq_len(nCells))
- gene.names <- paste0("Gene", seq_len(nGenes))
rownames(counts) <- gene.names
colnames(counts) <- cell.names
- phenos <- new("AnnotatedDataFrame",
- data = data.frame(Cell = cell.names, CellFac = cell.facs))
- rownames(phenos) <- cell.names
- features <- new("AnnotatedDataFrame",
- data = data.frame(Gene = gene.names, GeneMean = gene.means))
- rownames(features) <- gene.names
- sim <- newSCESet(countData = counts, phenoData = phenos,
- featureData = features)
+ cells <- data.frame(Cell = cell.names, CellFac = cell.facs)
+ rownames(cells) <- cell.names
- colnames(cell.means) <- cell.names
- rownames(cell.means) <- gene.names
- set_exprs(sim, "CellMeans") <- cell.means
+ features <- data.frame(Gene = gene.names, GeneMean = gene.means)
+ rownames(features) <- gene.names
if (nGroups > 1) {
- pData(sim)$Group <- groups
+ cells$Group <- groups
for (idx in seq_along(de.facs)) {
- fData(sim)[[paste0("DEFacGroup", idx)]] <- de.facs[[idx]]
- fData(sim)[[paste0("GeneMeanGroup", idx)]] <- gene.means *
+ features[[paste0("DEFacGroup", idx)]] <- de.facs[[idx]]
+ features[[paste0("GeneMeanGroup", idx)]] <- gene.means *
de.facs[[idx]]
}
}
+ sim <- SingleCellExperiment(assays = list(counts = counts,
+ CellMeans = cell.means),
+ rowData = features,
+ colData = cells,
+ metadata = list(params = params))
+
+ if (verbose) {message("Done!")}
+
return(sim)
}
diff --git a/R/lun2-simulate.R b/R/lun2-simulate.R
index bc8d9b696ad8eeb0086c664ca8aa2028d281cd03..439e3c60ae6353b7d861a31b3627e4e74c638ee8 100644
--- a/R/lun2-simulate.R
+++ b/R/lun2-simulate.R
@@ -91,31 +91,13 @@ lun2Simulate <- function(params = newLun2Params(), zinb = FALSE,
de.nGenes <- getParam(params, "de.nGenes")
- #if (name == "nGenes") {
- # old.nGenes <- getParam(object, "nGenes")
- # if (value != old.nGenes) {
- # warning("nGenes has been changed. Gene parameter vectors will be ",
- # "sampled to length new nGenes.")
- # selected <- sample(seq_len(old.nGenes), size = value,
- # replace = TRUE)
- # for (parameter in grep("gene", slotNames(object), value = TRUE)) {
- # old.value <- getParam(object, parameter)
- # object <- setParamUnchecked(object, parameter,
- # old.value[selected])
- # }
- # }
- #}
-
-
# Set up objects to store intermediate values
cell.names <- paste0("Cell", seq_len(nCells))
gene.names <- paste0("Gene", seq_len(nGenes))
- features <- new("AnnotatedDataFrame",
- data = data.frame(Gene = gene.names, GeneMean = gene.means,
- GeneDisp = gene.disps))
- phenos <- new("AnnotatedDataFrame",
- data = data.frame(Cell = cell.names, Plate = cell.plates))
+ features <- data.frame(Gene = gene.names, GeneMean = gene.means,
+ GeneDisp = gene.disps)
+ cells <- data.frame(Cell = cell.names, Plate = cell.plates)
if (zinb) {
features$GeneZeroProp <- gene.ziProps
@@ -166,7 +148,7 @@ lun2Simulate <- function(params = newLun2Params(), zinb = FALSE,
if (verbose) {message("Simulating libray size factors...")}
lib.facs <- lib.sizes / mean(lib.sizes)
lib.facs <- sample(lib.facs, nCells, replace = TRUE) * lib.mod
- phenos$LibSizeFac <- lib.facs
+ cells$LibSizeFac <- lib.facs
if (verbose) {message("Simulating cell means...")}
cell.means <- plate.means[, as.integer(cell.plates)]
@@ -187,25 +169,26 @@ lun2Simulate <- function(params = newLun2Params(), zinb = FALSE,
if (verbose) {message("Creating final SCESet...")}
- rownames(phenos) <- cell.names
+ rownames(cells) <- cell.names
rownames(features) <- gene.names
rownames(counts) <- gene.names
colnames(counts) <- cell.names
- sim <- newSCESet(countData = counts, phenoData = phenos,
- featureData = features)
-
rownames(cell.means) <- gene.names
colnames(cell.means) <- cell.names
- set_exprs(sim, "CellMeans") <- cell.means
-
rownames(true.counts) <- gene.names
colnames(true.counts) <- cell.names
- set_exprs(sim, "TrueCounts") <- true.counts
+
+ sim <- SingleCellExperiment(assays = list(counts = counts,
+ CellMeans = cell.means,
+ TrueCounts <- true.counts),
+ rowData = features,
+ colData = cells,
+ metadata = list(params = params))
if (zinb) {
rownames(is.zero) <- gene.names
colnames(is.zero) <- cell.names
- set_exprs(sim, "ZeroInflation") <- is.zero
+ assays(sim)$ZeroInflation <- is.zero
}
if (verbose) {message("Done!")}
diff --git a/R/scDD-simulate.R b/R/scDD-simulate.R
index 478443f68054c276c49d75e9418c93e18b9d4046..b3d949827160a8449ff90cf2b458d01323d5f16b 100644
--- a/R/scDD-simulate.R
+++ b/R/scDD-simulate.R
@@ -100,16 +100,19 @@ scDDSimulate <- function(params = newSCDDParams(), plots = FALSE,
rownames(counts) <- gene.names
colnames(counts) <- cell.names
- phenos <- new("AnnotatedDataFrame",
- data = data.frame(Cell = cell.names,
- Condition = rep(1:2, each = nCells)))
- rownames(phenos) <- cell.names
- features <- new("AnnotatedDataFrame",
- data = data.frame(Gene = gene.names, DEStatus = de.status,
- FoldChange = foldchanges))
+
+ cells <- data.frame(Cell = cell.names,
+ Condition = rep(1:2, each = nCells))
+ rownames(cells) <- cell.names
+
+ features <- data.frame(Gene = gene.names, DEStatus = de.status,
+ FoldChange = foldchanges)
rownames(features) <- gene.names
- sim <- newSCESet(countData = counts, phenoData = phenos,
- featureData = features)
+
+ sim <- SingleCellExperiment(assays = list(counts = counts),
+ rowData = features,
+ colData = cells,
+ metadata = list(params = params))
if (verbose) {message("Done!")}
diff --git a/R/simple-simulate.R b/R/simple-simulate.R
index e52b216440bb1f567fdaa006e247f885e18361fd..add68255c0f3a9e8b91a283cb88518e4704254d1 100644
--- a/R/simple-simulate.R
+++ b/R/simple-simulate.R
@@ -53,13 +53,15 @@ simpleSimulate <- function(params = newSimpleParams(), verbose = TRUE, ...) {
rownames(counts) <- gene.names
colnames(counts) <- cell.names
- phenos <- new("AnnotatedDataFrame", data = data.frame(Cell = cell.names))
- rownames(phenos) <- cell.names
- features <- new("AnnotatedDataFrame",
- data = data.frame(Gene = gene.names, GeneMean = means))
+ cells <- data.frame(Cell = cell.names)
+ rownames(cells) <- cell.names
+ features <- data.frame(Gene = gene.names, GeneMean = means)
rownames(features) <- gene.names
- sim <- newSCESet(countData = counts, phenoData = phenos,
- featureData = features)
+
+ sim <- SingleCellExperiment(assays = list(counts = counts),
+ rowData = features,
+ colData = cells,
+ metadata = list(params = params))
return(sim)
}
diff --git a/R/splat-simulate.R b/R/splat-simulate.R
index 5256804ea4ee0dc210b9d3cfe502bb28ab041211..7967e701b10e6021acd6196b316a0222d730fa10 100644
--- a/R/splat-simulate.R
+++ b/R/splat-simulate.R
@@ -159,27 +159,24 @@ splatSimulate <- function(params = newSplatParams(),
}
# Create SCESet with dummy counts to store simulation
- dummy.counts <- matrix(1, ncol = nCells, nrow = nGenes)
- rownames(dummy.counts) <- gene.names
- colnames(dummy.counts) <- cell.names
- phenos <- new("AnnotatedDataFrame", data = data.frame(Cell = cell.names))
- rownames(phenos) <- cell.names
- features <- new("AnnotatedDataFrame", data = data.frame(Gene = gene.names))
+ cells <- data.frame(Cell = cell.names)
+ rownames(cells) <- cell.names
+ features <- data.frame(Gene = gene.names)
rownames(features) <- gene.names
- sim <- newSCESet(countData = dummy.counts, phenoData = phenos,
- featureData = features)
+ sim <- SingleCellExperiment(rowData = features, colData = cells,
+ metadata = list(params = params))
# Make batches vector which is the index of param$batchCells repeated
# params$batchCells[index] times
batches <- lapply(seq_len(nBatches), function(i, b) {rep(i, b[i])},
b = batch.cells)
batches <- unlist(batches)
- pData(sim)$Batch <- batch.names[batches]
+ colData(sim)$Batch <- batch.names[batches]
if (method != "single") {
groups <- sample(seq_len(nGroups), nCells, prob = group.prob,
replace = TRUE)
- pData(sim)$Group <- group.names[groups]
+ colData(sim)$Group <- group.names[groups]
}
if (verbose) {message("Simulating library sizes...")}
@@ -211,21 +208,8 @@ splatSimulate <- function(params = newSplatParams(),
if (verbose) {message("Simulating dropout (if needed)...")}
sim <- splatSimDropout(sim, params)
- if (verbose) {message("Creating final SCESet...")}
- # Create new SCESet to make sure values are calculated correctly
- sce <- newSCESet(countData = counts(sim),
- phenoData = new("AnnotatedDataFrame", data = pData(sim)),
- featureData = new("AnnotatedDataFrame", data = fData(sim)))
-
- # Add intermediate matrices stored in assayData
- for (assay.name in names(assayData(sim))) {
- if (!(assay.name %in% names(assayData(sce)))) {
- set_exprs(sce, assay.name) <- get_exprs(sim, assay.name)
- }
- }
-
if (verbose) {message("Done!")}
- return(sce)
+ return(sim)
}
#' @rdname splatSimulate
@@ -272,7 +256,7 @@ splatSimLibSizes <- function(sim, params) {
lib.scale <- getParam(params, "lib.scale")
exp.lib.sizes <- rlnorm(nCells, lib.loc, lib.scale)
- pData(sim)$ExpLibSize <- exp.lib.sizes
+ colData(sim)$ExpLibSize <- exp.lib.sizes
return(sim)
}
@@ -311,9 +295,9 @@ splatSimGeneMeans <- function(sim, params) {
means.gene <- base.means.gene
means.gene[is.outlier] <- outlier.means[is.outlier]
- fData(sim)$BaseGeneMean <- base.means.gene
- fData(sim)$OutlierFactor <- outlier.facs
- fData(sim)$GeneMean <- means.gene
+ rowData(sim)$BaseGeneMean <- base.means.gene
+ rowData(sim)$OutlierFactor <- outlier.facs
+ rowData(sim)$GeneMean <- means.gene
return(sim)
}
@@ -336,13 +320,13 @@ splatSimBatchEffects <- function(sim, params) {
nBatches <- getParam(params, "nBatches")
batch.facLoc <- getParam(params, "batch.facLoc")
batch.facScale <- getParam(params, "batch.facScale")
- means.gene <- fData(sim)$GeneMean
+ means.gene <- rowData(sim)$GeneMean
for (idx in seq_len(nBatches)) {
batch.facs <- getLNormFactors(nGenes, 1, 0.5, batch.facLoc[idx],
batch.facScale[idx])
batch.means.gene <- means.gene * batch.facs
- fData(sim)[[paste0("BatchFacBatch", idx)]] <- batch.facs
+ rowData(sim)[[paste0("BatchFacBatch", idx)]] <- batch.facs
}
return(sim)
@@ -362,15 +346,15 @@ splatSimBatchEffects <- function(sim, params) {
splatSimBatchCellMeans <- function(sim, params) {
nBatches <- getParam(params, "nBatches")
- cell.names <- pData(sim)$Cell
- gene.names <- fData(sim)$Gene
- gene.means <- fData(sim)$GeneMean
+ cell.names <- colData(sim)$Cell
+ gene.names <- rowData(sim)$Gene
+ gene.means <- rowData(sim)$GeneMean
if (nBatches > 1) {
- batches <- pData(sim)$Batch
+ batches <- colData(sim)$Batch
batch.names <- unique(batches)
- batch.facs.gene <- fData(sim)[, paste0("BatchFac", batch.names)]
+ batch.facs.gene <- rowData(sim)[, paste0("BatchFac", batch.names)]
batch.facs.cell <- as.matrix(batch.facs.gene[, factor(batches)])
} else {
@@ -384,7 +368,7 @@ splatSimBatchCellMeans <- function(sim, params) {
colnames(batch.means.cell) <- cell.names
rownames(batch.means.cell) <- gene.names
- set_exprs(sim, "BatchCellMeans") <- batch.means.cell
+ assays(sim)$BatchCellMeans <- batch.means.cell
return(sim)
}
@@ -414,13 +398,13 @@ splatSimGroupDE <- function(sim, params) {
de.downProb <- getParam(params, "de.downProb")
de.facLoc <- getParam(params, "de.facLoc")
de.facScale <- getParam(params, "de.facScale")
- means.gene <- fData(sim)$GeneMean
+ means.gene <- rowData(sim)$GeneMean
for (idx in seq_len(nGroups)) {
de.facs <- getLNormFactors(nGenes, de.prob[idx], de.downProb[idx],
de.facLoc[idx], de.facScale[idx])
group.means.gene <- means.gene * de.facs
- fData(sim)[[paste0("DEFacGroup", idx)]] <- de.facs
+ rowData(sim)[[paste0("DEFacGroup", idx)]] <- de.facs
}
return(sim)
@@ -441,14 +425,14 @@ splatSimPathDE <- function(sim, params) {
for (path in path.order) {
from <- path.from[path]
if (from == 0) {
- means.gene <- fData(sim)$GeneMean
+ means.gene <- rowData(sim)$GeneMean
} else {
- means.gene <- fData(sim)[[paste0("GeneMeanPath", from)]]
+ means.gene <- rowData(sim)[[paste0("GeneMeanPath", from)]]
}
de.facs <- getLNormFactors(nGenes, de.prob[path], de.downProb[path],
de.facLoc[path], de.facScale[path])
path.means.gene <- means.gene * de.facs
- fData(sim)[[paste0("DEFacPath", path)]] <- de.facs
+ rowData(sim)[[paste0("DEFacPath", path)]] <- de.facs
}
return(sim)
@@ -476,10 +460,10 @@ NULL
splatSimSingleCellMeans <- function(sim, params) {
nCells <- getParam(params, "nCells")
- cell.names <- pData(sim)$Cell
- gene.names <- fData(sim)$Gene
- exp.lib.sizes <- pData(sim)$ExpLibSize
- batch.means.cell <- get_exprs(sim, "BatchCellMeans")
+ cell.names <- colData(sim)$Cell
+ gene.names <- rowData(sim)$Gene
+ exp.lib.sizes <- colData(sim)$ExpLibSize
+ batch.means.cell <- assays(sim)$BatchCellMeans
cell.means.gene <- batch.means.cell
cell.props.gene <- t(t(cell.means.gene) / colSums(cell.means.gene))
@@ -487,7 +471,7 @@ splatSimSingleCellMeans <- function(sim, params) {
colnames(base.means.cell) <- cell.names
rownames(base.means.cell) <- gene.names
- set_exprs(sim, "BaseCellMeans") <- base.means.cell
+ assays(sim)$BaseCellMeans <- base.means.cell
return(sim)
}
@@ -498,22 +482,22 @@ splatSimSingleCellMeans <- function(sim, params) {
splatSimGroupCellMeans <- function(sim, params) {
nGroups <- getParam(params, "nGroups")
- cell.names <- pData(sim)$Cell
- gene.names <- fData(sim)$Gene
- groups <- pData(sim)$Group
+ cell.names <- colData(sim)$Cell
+ gene.names <- rowData(sim)$Gene
+ groups <- colData(sim)$Group
group.names <- sort(unique(groups))
- exp.lib.sizes <- pData(sim)$ExpLibSize
- batch.means.cell <- get_exprs(sim, "BatchCellMeans")
+ exp.lib.sizes <- colData(sim)$ExpLibSize
+ batch.means.cell <- assays(sim)$BatchCellMeans
- group.facs.gene <- fData(sim)[, paste0("DEFac", group.names)]
- cell.facs.gene <- as.matrix(group.facs.gene[, factor(groups)])
+ group.facs.gene <- rowData(sim)[, paste0("DEFac", group.names)]
+ cell.facs.gene <- as.matrix(group.facs.gene[, paste0("DEFac", groups)])
cell.means.gene <- batch.means.cell * cell.facs.gene
cell.props.gene <- t(t(cell.means.gene) / colSums(cell.means.gene))
base.means.cell <- t(t(cell.props.gene) * exp.lib.sizes)
colnames(base.means.cell) <- cell.names
rownames(base.means.cell) <- gene.names
- set_exprs(sim, "BaseCellMeans") <- base.means.cell
+ assays(sim)$BaseCellMeans <- base.means.cell
return(sim)
}
@@ -527,16 +511,16 @@ splatSimPathCellMeans <- function(sim, params) {
nGenes <- getParam(params, "nGenes")
nCells <- getParam(params, "nCells")
nGroups <- getParam(params, "nGroups")
- cell.names <- pData(sim)$Cell
- gene.names <- fData(sim)$Gene
+ cell.names <- colData(sim)$Cell
+ gene.names <- rowData(sim)$Gene
path.from <- getParam(params, "path.from")
path.length <- getParam(params, "path.length")
path.skew <- getParam(params, "path.skew")
path.nonlinearProb <- getParam(params, "path.nonlinearProb")
path.sigmaFac <- getParam(params, "path.sigmaFac")
- groups <- pData(sim)$Group
- exp.lib.sizes <- pData(sim)$ExpLibSize
- batch.means.cell <- get_exprs(sim, "BatchCellMeans")
+ groups <- colData(sim)$Group
+ exp.lib.sizes <- colData(sim)$ExpLibSize
+ batch.means.cell <- assays(sim)$BatchCellMeans
group.sizes <- table(groups)
@@ -546,7 +530,7 @@ splatSimPathCellMeans <- function(sim, params) {
is.nonlinear <- as.logical(rbinom(nGenes, 1, path.nonlinearProb))
sigma.facs <- rep(0, nGenes)
sigma.facs[is.nonlinear] <- path.sigmaFac
- fData(sim)[[paste0("SigmaFacPath", idx)]] <- sigma.facs
+ rowData(sim)[[paste0("SigmaFacPath", idx)]] <- sigma.facs
}
# Generate non-linear path factors
@@ -555,7 +539,7 @@ splatSimPathCellMeans <- function(sim, params) {
is.nonlinear <- as.logical(rbinom(nGenes, 1, path.nonlinearProb))
sigma.facs <- rep(0, nGenes)
sigma.facs[is.nonlinear] <- path.sigmaFac
- fData(sim)[[paste0("SigmaFacPath", idx)]] <- sigma.facs
+ rowData(sim)[[paste0("SigmaFacPath", idx)]] <- sigma.facs
}
# Generate paths. Each path is a matrix with path.length columns and
@@ -566,13 +550,13 @@ splatSimPathCellMeans <- function(sim, params) {
if (from == 0) {
facs.start <- rep(1, nGenes)
} else {
- facs.start <- fData(sim)[[paste0("DEFacPath", from)]]
+ facs.start <- rowData(sim)[[paste0("DEFacPath", from)]]
}
# Find the factors at the end position
- facs.end <- fData(sim)[[paste0("DEFacPath", idx)]]
+ facs.end <- rowData(sim)[[paste0("DEFacPath", idx)]]
# Get the non-linear factors
- sigma.facs <- fData(sim)[[paste0("SigmaFacPath", idx)]]
+ sigma.facs <- rowData(sim)[[paste0("SigmaFacPath", idx)]]
# Build Brownian bridges from start to end
steps <- buildBridges(facs.start, facs.end, n = path.length[idx],
@@ -609,9 +593,8 @@ splatSimPathCellMeans <- function(sim, params) {
colnames(base.means.cell) <- cell.names
rownames(base.means.cell) <- gene.names
- #pData(sim)$Step <- unlist(cell.steps)
- pData(sim)$Step <- steps
- set_exprs(sim, "BaseCellMeans") <- base.means.cell
+ colData(sim)$Step <- steps
+ assays(sim)$BaseCellMeans <- base.means.cell
return(sim)
}
@@ -632,13 +615,13 @@ splatSimPathCellMeans <- function(sim, params) {
#' @importFrom stats rchisq rgamma
splatSimBCVMeans <- function(sim, params) {
- cell.names <- pData(sim)$Cell
- gene.names <- fData(sim)$Gene
+ cell.names <- colData(sim)$Cell
+ gene.names <- rowData(sim)$Gene
nGenes <- getParam(params, "nGenes")
nCells <- getParam(params, "nCells")
bcv.common <- getParam(params, "bcv.common")
bcv.df <- getParam(params, "bcv.df")
- base.means.cell <- get_exprs(sim, "BaseCellMeans")
+ base.means.cell <- assays(sim)$BaseCellMeans
if (is.finite(bcv.df)) {
bcv <- (bcv.common + (1 / sqrt(base.means.cell))) *
@@ -655,8 +638,8 @@ splatSimBCVMeans <- function(sim, params) {
colnames(means.cell) <- cell.names
rownames(means.cell) <- gene.names
- set_exprs(sim, "BCV") <- bcv
- set_exprs(sim, "CellMeans") <- means.cell
+ assays(sim)$BCV <- bcv
+ assays(sim)$CellMeans <- means.cell
return(sim)
}
@@ -677,11 +660,11 @@ splatSimBCVMeans <- function(sim, params) {
#' @importFrom stats rpois
splatSimTrueCounts <- function(sim, params) {
- cell.names <- pData(sim)$Cell
- gene.names <- fData(sim)$Gene
+ cell.names <- colData(sim)$Cell
+ gene.names <- rowData(sim)$Gene
nGenes <- getParam(params, "nGenes")
nCells <- getParam(params, "nCells")
- cell.means <- get_exprs(sim, "CellMeans")
+ cell.means <- assays(sim)$CellMeans
true.counts <- matrix(rpois(nGenes * nCells, lambda = cell.means),
nrow = nGenes, ncol = nCells)
@@ -689,7 +672,7 @@ splatSimTrueCounts <- function(sim, params) {
colnames(true.counts) <- cell.names
rownames(true.counts) <- gene.names
- set_exprs(sim, "TrueCounts") <- true.counts
+ assays(sim)$TrueCounts <- true.counts
return(sim)
}
@@ -712,11 +695,11 @@ splatSimTrueCounts <- function(sim, params) {
splatSimDropout <- function(sim, params) {
dropout.present <- getParam(params, "dropout.present")
- true.counts <- get_exprs(sim, "TrueCounts")
+ true.counts <- assays(sim)$TrueCounts
if (dropout.present) {
- cell.names <- pData(sim)$Cell
- gene.names <- fData(sim)$Gene
+ cell.names <- colData(sim)$Cell
+ gene.names <- rowData(sim)$Gene
nCells <- getParam(params, "nCells")
nGenes <- getParam(params, "nGenes")
dropout.mid <- getParam(params, "dropout.mid")
@@ -740,13 +723,13 @@ splatSimDropout <- function(sim, params) {
colnames(keep) <- cell.names
rownames(keep) <- gene.names
- set_exprs(sim, "DropProb") <- drop.prob
- set_exprs(sim, "Dropout") <- !keep
+ assays(sim)$DropProb <- drop.prob
+ assays(sim)$Dropout <- !keep
} else {
counts <- true.counts
}
- scater::counts(sim) <- counts
+ counts(sim) <- counts
return(sim)
}