Spatial domains identification in spatial transcriptomics by domain knowledge-aware and subspace-enhanced graph contrastive learning

We propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and subspace module to accurately distinguish different spatial domains by capturing 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 images prior, preserving information crucial for the clustering task. Experimental results on 8 ST datasets from 5 different platforms show that GRAS4T outperforms five state-of-the-art competing methods in spatial domain identification. 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.我们提出了一种图对比学习（Graph Contrastive Learning）框架GRAS4T，该框架结合对比学习与子空间模块，通过同一域内测序位点的自表达性捕获组织微环境，从而精准区分不同空间域。为挖掘用于空间域识别的相关特征，GRAS4T采用基于组织学图像先验的图增强策略，保留对聚类任务至关重要的信息。在来自5种不同平台的8个空间转录组（Spatial Transcriptomics, ST）数据集上的实验结果表明，GRAS4T在空间域识别任务中优于5种当前最优的同类竞争方法。尤为重要的是，GRAS4T能够出色地分离不同组织结构，并揭示更精细的空间域。GRAS4T融合了子空间分析与图表示学习的优势并具备可扩展性，是一种适用于ST域识别的理想框架。