MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder.

ABSTRACT: Spatial transcriptomics (ST) technology provides gene expression profiles with spatial context, offering critical insights into cellular interactions and tissue architecture. A core task in ST is spatial domain identification, which involves detecting coherent regions with similar spatial expression patterns. However, existing methods often fail to fully exploit spatial information, leading to limited representational capacity and suboptimal clustering accuracy. Here, we introduce MAEST, a novel graph neural network model designed to address these limitations in ST data. MAEST leverages graph masked autoencoders to denoise and refine representations while incorporating graph contrastive learning to prevent feature collapse and enhance model robustness. By integrating one-hop and multi-hop

SUBMITTER: Zhu P 

PROVIDER: S-EPMC11886571 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature