scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

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 om...

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Veröffentlicht in:Nature biotechnology 2024-02, Vol.42 (2), p.247-252
Hauptverfasser: Song, Dongyuan, Wang, Qingyang, Yan, Guanao, Liu, Tianyang, Sun, Tianyi, Li, Jingyi Jessica
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Sprache:eng
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Zusammenfassung: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. The challenge of simulating multiomic single-cell data is addressed by a probabilistic model.
ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-023-01772-1