Gui Yang, Li Chao, Xu Yan
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.
School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233041, China.
Comput Struct Biotechnol J. 2024 Oct 22;23:3703-3713. doi: 10.1016/j.csbj.2024.10.029. eCollection 2024 Dec.
Spatial transcriptomics (ST) technologies have emerged as an effective tool to identify the spatial architecture of tissues, facilitating a comprehensive understanding of organ function and the tissue microenvironment. Spatial domain identification is the first and most critical step in ST data analysis, which requires thoughtful utilization of tissue microenvironment and morphological priors. Here, we propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and a subspace analysis model to accurately distinguish different spatial domains by capturing the tissue microenvironment through self-expressiveness of spots within the same domain. To uncover the pertinent features for spatial domain identification, GRAS4T employs a graph augmentation based on histological image priors, preserving structural information crucial for the clustering task. Experimental results on eight ST datasets from five different platforms show that GRAS4T outperforms five state-of-the-art competing methods. Significantly, GRAS4T excels at separating distinct tissue structures and unveiling more detailed spatial domains. GRAS4T combines the advantages of subspace analysis and graph representation learning with extensibility, making it an ideal framework for ST domain identification.
空间转录组学（ST）技术已成为识别组织空间结构的有效工具，有助于全面了解器官功能和组织微环境。空间域识别是ST数据分析的首要也是最关键的步骤，这需要充分利用组织微环境和形态学先验知识。在此，我们提出了一种图对比学习框架GRAS4T，它将对比学习和子空间分析模型相结合，通过同一域内斑点的自表达来捕获组织微环境，从而准确区分不同的空间域。为了揭示用于空间域识别的相关特征，GRAS4T基于组织学图像先验进行图增强，保留对聚类任务至关重要的结构信息。在来自五个不同平台的八个ST数据集上的实验结果表明，GRAS4T优于五种最先进的竞争方法。值得注意的是，GRAS4T在分离不同组织结构和揭示更详细的空间域方面表现出色。GRAS4T结合了子空间分析和图表示学习的优点，具有可扩展性，使其成为ST域识别的理想框架。
