Generates simulated bulk RNA-seq mixtures from single-cell reference data using Dirichlet-sampled cell-type proportions, optional sparsity, and optional rare-cell perturbations.
Usage
tape_simulate(
sc_data,
d_prior = NULL,
n = 500,
samplenum = 5000,
random_state = NULL,
sparse = TRUE,
sparse_prob = 0.5,
rare = FALSE,
rare_percentage = 0.4,
celltype_col = "CellType",
assay = "RNA",
slot = "counts"
)Arguments
- sc_data
A `Seurat` object, a `SingleCellExperiment` object, or a matrix/data.frame with cells in rows and genes in columns.
- d_prior
Numeric vector or `NULL`. Dirichlet prior for cell-type fractions. If `NULL`, a vector of ones is used.
- n
Integer scalar. Number of cells per simulated bulk sample.
- samplenum
Integer scalar. Number of pseudobulk samples to generate.
- random_state
Integer scalar or `NULL`. Random seed for reproducible simulation.
- sparse
Logical scalar. Whether to generate sparse cell-type mixtures.
- sparse_prob
Numeric scalar in `[0, 1]`. Controls both the proportion of sparse samples and the proportion of cell types set to zero within those samples, matching the provided implementation.
- rare
Logical scalar. Whether to perturb selected cell types to very small fractions.
- rare_percentage
Numeric scalar in `[0, 1]`. Fraction of cell types selected for rare-cell perturbation.
- celltype_col
Character scalar giving the metadata column with cell-type labels.
- assay
Character scalar giving the assay name for `Seurat` input.
- slot
Character scalar giving the assay slot or assay name to extract.
Value
A named list with components:
- X
A numeric matrix of simulated pseudobulks with samples in rows and genes in columns.
- obs
A data frame of simulated cell-type proportions with samples in rows and cell types in columns.
- var
A data frame whose row names are the simulated gene names.
Details
This function generates simulated pseudobulk mixtures for the R/torch implementation of TAPE, following the simulation strategy used in the original TAPE workflow.
Examples
set.seed(1)
n_genes <- 1000
cell_types <- c("Tcell", "Bcell", "Mono")
cells_per_type <- 100
n_cells <- length(cell_types) * cells_per_type
counts <- matrix(
rpois(n_genes * n_cells, lambda = 5),
nrow = n_genes, ncol = n_cells
)
rownames(counts) <- paste0("gene", seq_len(n_genes))
colnames(counts) <- paste0("cell", seq_len(n_cells))
meta <- data.frame(
cell_type = rep(cell_types, each = cells_per_type),
row.names = colnames(counts)
)
sc <- SeuratObject::CreateSeuratObject(counts = counts, meta.data = meta)
#> Warning: Data is of class matrix. Coercing to dgCMatrix.
simudata <- tape_simulate(
sc_data = sc,
samplenum = 50,
n = 100,
celltype_col = "cell_type"
)
#> [00:00:00] Simulation started
#> [00:00:00] Generating Dirichlet proportions
#> [00:00:00] Applying sparse perturbation
#> [00:00:00] Sampling cells and building pseudobulks
#>
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#> [00:00:00] Simulation finished
print(names(simudata))
#> [1] "X" "obs" "var"