背景:空间转录组学(ST)正在促进我们对复杂组织和生物体的理解。然而,构建稳健的聚类算法以在单个组织切片中定义空间相干区域,并对齐或整合源自不同来源的多个组织切片以进行必要的下游分析仍然具有挑战性。众多的集群,对齐,集成方法是通过利用ST数据的空间信息专门为ST数据设计的。缺乏全面的基准研究使方法的选择和未来的方法开发变得复杂。
结果:在这项研究中,我们系统地对各种最先进的算法进行基准测试,使用各种大小不同的真实和模拟数据集,技术,物种,和复杂性。我们使用不同的定量和定性指标和分析来分析每种方法的优缺点,包括空间聚类准确性和连续性的八个指标,均匀流形逼近和投影可视化,逐层和逐点对准精度,和三维重建,旨在评估方法性能和数据质量。用于评估的代码可在我们的GitHub上找到。此外,我们提供在线笔记本教程和文档,以促进所有基准测试结果的复制,并支持新方法和新数据集的研究。
结论:我们的分析得出了涵盖多个方面的全面建议,帮助用户为他们的特定需求选择最佳工具,并指导未来的方法开发。
BACKGROUND: Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering,
alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development.
RESULTS: In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot
alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets.
CONCLUSIONS: Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.