SpaICL: image-guided curriculum strategy-based graph contrastive learning for spatial transcriptomics clustering.

ABSTRACT: Spatial transcriptomics, by capturing both gene expression and spatial information, holds great promise for unraveling the complex organization of tissues. In this study, we introduce SpaICL, an image-guided curriculum strategy-based graph contrastive learning framework for spatial transcriptomics clustering. SpaICL integrates gene expression, spatial coordinates, and histological image features to construct a low-dimensional latent representation that enhances the de-lineation of spatial functional domains. The model employs a complementary masking strategy and a shared graph neural network encoder to generate dual embeddings, while a dual cross-attention mechanism aligns local and global features across multiple modalities. Additionally, the curriculum learning module further facilitates

SUBMITTER: Zhao J 

PROVIDER: S-EPMC12368861 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature