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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
日本語の概要は準備中です。原文の説明を表示しています。
This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
日本語の概要は準備中です。原文の説明を表示しています。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
日本語の概要は準備中です。原文の説明を表示しています。
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
日本語の概要は準備中です。原文の説明を表示しています。
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
日本語の概要は準備中です。原文の説明を表示しています。