scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics \textbar Nature Biotechnology. Song, D., Wang, Q., Yan, G., Liu, T., Sun, T., & Li, J. J. Nature Biotechnology, May, 2023.
Paper doi abstract bibtex 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.
@article{song_scdesign3_2023,
title = {{scDesign3} generates realistic in silico data for multimodal single-cell and spatial omics {\textbar} {Nature} {Biotechnology}},
issn = {1546-1696},
url = {https://www.nature.com/articles/s41587-023-01772-1},
doi = {10.1038/s41587-023-01772-1},
abstract = {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.},
urldate = {2023-05-24},
journal = {Nature Biotechnology},
author = {Song, Dongyuan and Wang, Qingyang and Yan, Guanao and Liu, Tianyang and Sun, Tianyi and Li, Jingyi Jessica},
month = may,
year = {2023},
}
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