Chen Xuanwei, Ran Qinghua, Tang Junjie, Chen Zihao, Huang Siyuan, Shi Xingjie, Xi Ruibin
School of Mathematical Sciences, Peking University, Beijing 100871, China.
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Bioinformatics. 2025 Mar 29;41(4). doi: 10.1093/bioinformatics/btaf131.
The rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets.
In response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of 30 synthesized and 74 real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists in selecting suitable algorithms for their research, while also aid bioinformatics scientists in developing more powerful and efficient computational methods in spatial transcriptomic research.
The source code of this benchmarking framework is available at both Github (https://github.com/XiDsLab/svg-benchmark) and Zenodo (https://doi.org/10.5281/zenodo.15031083). In addition, all real and synthetic datasets considered in this study are also publicly available at Zenodo (https://doi.org/10.5281/zenodo.7227771).
空间转录组学的快速发展凸显了识别空间可变基因的重要性。作为空间转录组数据分析中的一项基础任务，空间可变基因识别已得到广泛研究。然而，由于缺乏全面的基准测试，很难用真实世界数据集验证大量研究中分散的各种算法的有效性。
对此，本文提出了一个基准框架，通过分析30个合成数据集和74个真实世界数据集来评估识别空间可变基因的算法，旨在确定最佳算法及其相应的应用场景。该框架可以帮助医学和生命科学家为其研究选择合适的算法，同时也有助于生物信息学科学家在空间转录组学研究中开发更强大、高效的计算方法。
