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 reinforces implicit constraints on latent representations, improving feature robustness. More importantly, an adaptive spatial-semantic graph structure dynamically integrates local and global contextual information, enabling effective multi-slice integration. Extensive evaluations demonstrate that SpaCross outperforms thirteen state-of-the-art methods on single-slice datasets and achieves robust batch effect correction while preserving biologically meaningful spatial architectures in multi-slice integration. Notably, SpaCross integrates embryonic mouse tissues across developmental stages, identifying conserved regions and uncovering stage-specific structures such as the dorsal root ganglion. In the heart domain, it reconstructs developmental trajectories capturing key transcriptional transitions and gene programs associated with cardiac maturation.