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

ABSTRACT: 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 GR

SUBMITTER: Gui Y 

PROVIDER: S-EPMC12510406 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature