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

Song, D.; Wang, Q.; Yan, G.; Liu, T.; Li, J. J.

doi:10.1101/2022.09.20.508796

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bioRxiv · 10.1101/2022.09.20.508796

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

Song, D.·Wang, Q.·Yan, G.·Liu, T.·Li, J. J.

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Abstract

In the single-cell and spatial omics field, computational challenges include method benchmarking, data interpretation, and in silico data generation. To address these challenges, we propose an all-in-one 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 datasets. Furthermore, using a unified probabilistic model for single-cell and spatial omics data, scDesign3 can infer biologically meaningful parameters, assess the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations, and generate in silico negative and positive controls for benchmarking computational tools.

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Song, D., Wang, Q., Yan, G., Liu, T., Li, J. J.. 2022-09-22. A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics. https://doi.org/10.1101/2022.09.20.508796

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