Heterogeneous graph contrastive learning for integration and alignment of spatial transcriptomics data

Spatial transcriptomics (ST) technology enables the simultaneous capture of gene expression profile and spatial information within 2D tissue slices. However, conventional analyses that process each individual slice independently often overlook shared features across multiple slices, limiting comprehensive biological insights. To address this, we introduce GRASS, a deep graph representation learning-based framework designed for the integration and alignment of multislice ST data. GRASS consists of two core modules: GRASS_Integration, which employs a heterogeneous graph architecture integrating contrastive learning and a multi-expert collaboration strategy to fully utilize both shared and unique information, enabling multislice integration, clustering, and various downstream analyses; and GRASS_Alignment, which uses a dual-perception similarity metric to guide spot-level alignment, supporting downstream tasks such as imputation and 3D reconstruction. Experimental results on seven ST datasets from five different platforms demonstrate that GRASS consistently outperforms eight state-of-the-art methods in both integration and alignment tasks. By comprehensively addressing multi-level information integration, GRASS emerges as an ideal solution for the joint analysis of multislice ST data.