A statistical simulator that can generate realistic single-cell and spatial omics data including cell latent structures, feature modalities, spatial locations, and experimental designs is presented in Nature Biotechnology this week. Called scDesign3, the simulator learns interpretable parameters from real data, according to the University of California, Los Angeles researchers who developed it. ScDesign3 offers a probabilistic model that unifies the generation and inference for single-cell and spatial omics data, they write, inferring biologically meaningful parameters and assessing the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations. The model also generates in silico negative and positive controls for benchmarking computational tools. "Although the scDesign3 model should not be treated as the true model, its interpretable parameters precede functionalities besides data simulation," the researchers add.
UCLA Team Develops Computational Tool for Generating Realistic Single-Cell, Spatial Omics Data
May 12, 2023