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

In the single-cell and spatial omics field, computational challenges include method bench-marking, 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 quality of cell clusters and trajectories, and generate in silico negative and positive controls for benchmarking computational tools.