We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positi
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Simulate a single-cell RNA-seq dataset with a continuous developmental trajectory from a reference dataset and visualize the simulation quality.
library(scDesign3)
library(SingleCellExperiment)
library(ggplot2)
# Load example data
data("example_count", package = "scDesign3")
data("pseudotime", package = "scDesign3")
# Construct reference SingleCellExperiment
example_sce <- SingleCellExperiment(
assays = list(counts = example_count, logcounts = log1p(example_count)),
colData = DataFrame(pseudotime = pseudotime)
)
# Run simulation
set.seed(123)
example_simu <- scdesign3(
sce = example_sce,
assay_use = "counts",
celltype = NULL,
pseudotime = "pseudotime",
spatial = NULL,
other_covariates = NULL,
mu_formula = "s(pseudotime, k = 10, bs = 'cr')",
sigma_formula = "1",
family_use = "nb",
n_cores = 2,
usebam = FALSE,
corr_formula = "1",
copula = "gaussian",
DT = TRUE,
pseudo_obs = FALSE,
return_model = FALSE,
nonzerovar = FALSE
)
# Create simulated SingleCellExperiment
simu_sce <- SingleCellExperiment(
list(counts = example_simu$new_count),
colData = example_simu$new_covariate
)
logcounts(simu_sce) <- log1p(counts(simu_sce))
# Visualize and compare
compare_figure <- plot_reduceddim(
ref_sce = example_sce,
sce_list = list(simu_sce),
name_vec = c("Reference", "scDesign3"),
assay_use = "logcounts",
if_plot = TRUE,
color_by = "pseudotime",
n_pc = 20
)
plot(compare_figure$p_umap)
Input: A SingleCellExperiment reference object with pseudotime. Output: A simulated SingleCellExperiment object and a UMAP comparison plot.
scdesign3().plot_reduceddim().scran or scater directly.SingleCellExperiment object containing a raw count matrix in its assays (e.g., "counts").colData of the SingleCellExperiment object.SingleCellExperiment reference object."counts"): The assay name in sce to use for simulation.colData representing pseudotime."s(pseudotime, k = 10, bs = 'cr')")."1"): Formula for the dispersion/variance parameter."nb"): Distribution family to use (e.g., "nb" for negative binomial)."gaussian"): Copula type to model gene correlation (e.g., "gaussian" or "vine").2): Number of cores to use for parallel computation.set.seed() before running scdesign3() and visualization functions to ensure reproducibility of the simulated counts and UMAP layouts.sigma_formula to a spline formula (e.g., "s(pseudotime, k = 5, bs = 'cr')"), keeping in mind this increases computational cost.sigma_formula along pseudotime can significantly increase runtime. Fix: Keep sigma_formula = "1" unless variable dispersion is critical.scater: For basic single-cell visualization and QC, but does not perform parametric simulation.scran: For normalization and variance modeling, but not for generating synthetic datasets.No reviews yet. Tell others what you thought.
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