SpaCross deciphers spatial structures and corrects batch effects in multi-slice spatially resolved transcriptomics.

ABSTRACT: Spatially Resolved Transcriptomics (SRT) has revolutionized tissue architecture analysis by integrating gene expression with spatial coordinates. However, existing spatial domain identification methods struggle with unsupervised learning constraints, lack of implicit supervision in latent space, and challenges in balancing local spatial continuity with global semantic consistency, particularly in multi-slice integration. To address these issues, we propose SpaCross, a comprehensive deep learning framework for SRT that enhances spatial pattern recognition and cross-slice consistency. SpaCross employs a cross-masked graph autoencoder to reconstruct gene expression features while preserving spatial relationships and mitigating identity mapping issues. A cross-masked latent consistency module

SUBMITTER: Fang D 

PROVIDER: S-EPMC12484869 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature