College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China.
Nat Commun. 2024 Jun 12;15(1):5021. doi: 10.1038/s41467-024-49445-0.
A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.
在空间分辨转录组学（SRT）中，一个紧迫的挑战是对计算方法进行基准测试。一种广泛使用的方法涉及利用模拟数据。然而，目前可用的模拟 SRT 数据存在偏差，这严重影响了方法评估和验证的准确性。在这里，我们介绍了 scCube（https://github.com/ZJUFanLab/scCube），这是一个用于独立、可重复和技术多样化的 SRT 数据模拟的 Python 包。scCube 不仅能够在基于参考的模拟中保留基因的空间表达模式，还能够在无参考的模拟中生成具有不同空间可变性的模拟数据（涵盖空间模式类型、分辨率、斑点排列、目标基因类型和组织切片维度等）。我们全面地用现有的单细胞或 SRT 模拟器对 scCube 进行了基准测试，并通过三个应用详细展示了 scCube 在基准测试斑点去卷积、基因插补和分辨率增强方法方面的实用性。
