%0 Journal Article %A Dongyuan Song %A Qingyang Wang %A Guanao Yan %A Tianyang Liu %A Jingyi Jessica Li %T A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics %D 2022 %R 10.1101/2022.09.20.508796 %J bioRxiv %P 2022.09.20.508796 %X 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, scDesgin3 can infer biologically meaningful parameters, assess the quality of cell clusters and trajectories, and generate in silico negative and positive controls for benchmarking computational tools.Competing Interest StatementThe authors have declared no competing interest. %U https://www.biorxiv.org/content/biorxiv/early/2022/09/22/2022.09.20.508796.full.pdf