Wang Xiang, Li Wei Vivian, Li Hongwei
School of Mathematics and Physics, China University of Geosciences, Wuhan, China.
Department of Statistics, University of California, Riverside, USA.
Genome Biol. 2025 Jun 23;26(1):177. doi: 10.1186/s13059-025-03636-0.
Advances in spatial transcriptomics demand new tools to integrate data across tissue slices and identify consistent spatial domains. We introduce spCLUE, a comprehensive framework combining multi-view graph network, contrastive learning, attention mechanisms, and a batch prompting module to learn informative spot representations and integrate data from both aligned and unaligned samples. spCLUE outperforms nine single-slice and seven multi-slice methods when tested on diverse datasets and reveals biologically relevant domains across different tissues and conditions. spCLUE offers a powerful solution to spatial domain analysis and integration in spatial transcriptomics, enabling more accurate and interpretable studies of tissue organization.
空间转录组学的进展需要新的工具来整合跨组织切片的数据并识别一致的空间域。我们引入了spCLUE，这是一个综合框架，结合了多视图图网络、对比学习、注意力机制和一个批处理提示模块，以学习信息丰富的斑点表示并整合来自对齐和未对齐样本的数据。在不同数据集上进行测试时，spCLUE优于九种单切片方法和七种多切片方法，并揭示了不同组织和条件下的生物学相关域。spCLUE为空间转录组学中的空间域分析和整合提供了一个强大的解决方案，使对组织组织的研究更加准确和可解释。
