美国佐治亚理工学院Zhang Xiuwei研究组近日取得一项新成果。他们发现，cMultiSim：基于基因调控网络和细胞间相互作用的单细胞多组学和空间数据模拟。2025年4月17日出版的《自然—方法学》杂志发表了这项成果。

在这里，课题组介绍scMultiSim，一个综合的模拟器，生成多模态单细胞数据，包括基因表达，染色质可及性，RNA速度和空间细胞位置，同时考虑模式之间的关系。与现有的专注于有限生物因素的工具不同，scMultiSim同时模拟细胞身份、基因调控网络、细胞-细胞相互作用和染色质可及性，同时纳入技术噪声。

此外，它允许主题轻松调整每个因素的影响。

在这里，该团队展示了scMultiSim生成具有预期生物效应的数据，并通过对广泛的计算任务进行基准测试来展示其应用，包括多模态和多批次数据集成、RNA速度估计、基因调控网络推断和以空间解析基因表达数据为主题的细胞-细胞相互作用推断。与现有的模拟器相比，scMultiSim可以对更广泛的现有计算问题甚至新的潜在任务进行基准测试。

据悉，模拟的单细胞数据对于设计和评估缺乏实验地面真值的计算方法是必不可少的。

附：英文原文

Title: scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell–cell interactions

Author: Li, Hechen, Zhang, Ziqi, Squires, Michael, Chen, Xi, Zhang, Xiuwei

Issue&Volume: 2025-04-17

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.

DOI: 10.1038/s41592-025-02651-0

Source: https://www.nature.com/articles/s41592-025-02651-0