MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics

Recent advances in spatial transcriptomics (ST) highlight the need to integrate multiple slices for joint analysis. A key challenge is generating interpretable embeddings that preserve spatial geometry while correcting batch effects. We present MaskGraphene, a graph neural network that integrates ST data via masked self-supervised learning, triplet loss, and cluster-wise local alignment. By establishing indirect "soft-links" and direct "hard-links" across slices, MaskGraphene yields joint embeddings with high geometric fidelity. Benchmarks against eight methods demonstrate superior alignment and interpretability. MaskGraphene enhances downstream applications, including domain identification, trajectory reconstruction, biomarker discovery, and brain-layer mapping, enabling robust ST integration and biological insight.