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A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics

Bioconductor version: Release (3.19)

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 positive controls for benchmarking computational tools.

Author: Dongyuan Song [aut, cre] , Qingyang Wang [aut]

Maintainer: Dongyuan Song <dongyuansong at>

Citation (from within R, enter citation("scDesign3")):


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Reference Manual PDF


biocViews GeneExpression, Sequencing, SingleCell, Software, Spatial
Version 1.2.0
In Bioconductor since BioC 3.18 (R-4.3) (0.5 years)
License MIT + file LICENSE
Depends R (>= 4.3.0)
Imports dplyr, tibble, stats, methods, mgcv, gamlss, gamlss.dist, SummarizedExperiment, SingleCellExperiment, mclust, mvtnorm, parallel, pbmcapply, rvinecopulib, umap, ggplot2, irlba, viridis, BiocParallel, matrixStats, Matrix, sparseMVN, coop
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Source Package scDesign3_1.2.0.tar.gz
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