STGAT: spatial domain identification of consecutive slices based on graph contrastive learning

With recent advances in spatial transcriptomic technologies, multi-tissue section datasets are proliferating. While existing computational methods have achieved substantial progress in integrating multiple sections and correcting for batch effects, current approaches for spatial domain identification often fail to fully leverage both spatial context and gene expression information across consecutive sections. Moreover, prevailing graph contrastive learning frameworks typically depend on the construction of positive and negative sample pairs—a process susceptible to the introduction of noise. To overcome these limitations, we introduce STGAT, a framework that first achieves precise spatial alignment across sections using gene expression similarity. Within a unified spatial domain, STGAT employs a graph contrastive learning strategy that requires only positive pairs, enabling effective self-supervised representation learning of graph nodes. Experimental results demonstrate that STGAT effectively enhances clustering accuracy in spatial domain identification tasks across multi-section and cross-technology datasets. When applied to mouse olfactory bulb sections, the method yields sharply defined spatial domain boundaries and allows accurate identification of distinct anatomical regions. Furthermore, STGAT provides a more refined characterization of the tumor microenvironment. The source code used in this paper can be found in https://github.com/Jinsl-lab/STGAT.