With the rapid advancement of Spatial Resolved Transcriptomics (SRT) technology, it is now possible to comprehensively measure gene transcription while preserving the spatial context of tissues. Spatial domain identification and gene denoising are key objectives in SRT data analysis. We propose a Contrastively Augmented Masked Graph Autoencoder (STMGAC) to learn low-dimensional latent representations for domain identification. In the latent space, persistent signals for representations are obtained through self-distillation to guide self-supervised matching. At the same time, positive and negative anchor pairs are constructed using triplet learning to augment the discriminative ability. We evaluated the performance of STMGAC on five datasets, achieving results superior to those of existing baseline methods. All code and public datasets used in this paper are available at https://github.com/wenwenmin/STMGAC and https://zenodo.org/records/13253801.

本研究针对空间分辨转录组数据分析中的空间域识别和基因去噪这一关键问题，提出了一种新颖的对比增强掩蔽图形自编码器（STMGAC），能够学习低维潜在表示。通过自蒸馏技术在潜在空间中获得持久信号，并利用三元组学习构建正负锚点对以增强判别能力，STMGAC在五个数据集上的表现超越了现有基线方法。

具有对比增强的掩蔽图形自编码器用于空间分辨转录组数据