SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.

ABSTRACT: Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. Masking mechanisms, as an effective self-supervised learning strategy, can enhance the

SUBMITTER: Min W 

PROVIDER: S-EPMC11968113 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature