Abstract
Simulated single-cell data are essential for designing and evaluating computational methods in the absence of experimental ground truth. Here we present scMultiSim, a comprehensive simulator that generates multimodal single-cell data encompassing gene expression, chromatin accessibility, RNA velocity and spatial cell locations while accounting for the relationships between modalities. Unlike existing tools that focus on limited biological factors, scMultiSim simultaneously models cell identity, gene regulatory networks, cell-cell interactions and chromatin accessibility while incorporating technical noise. Moreover, it allows users to adjust each factor's effect easily. Here we show that scMultiSim generates data with expected biological effects, and demonstrate its applications by benchmarking a wide range of computational tasks, including multimodal and multi-batch data integration, RNA velocity estimation, gene regulatory network inference and cell-cell interaction inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.