ProtoPSR: Prototype-guided Pairwise Similarity Regularization for Robust Spatial Multi-Omics Integration

Abstract

Spatially resolved multi-omics technologies measure multiple molecular layers at the same tissue locations, providing a more comprehensive view of tissue organization than any single modality. However, unsupervised spatial domain discovery remains challenging: noise across modalities and mismatched data distributions can distort neighborhood graphs, while pseudo-label self-training may drift over training, resulting in unstable optimization and fragmented domain maps. We propose ProtoPSR, an unsupervised framework for robust spatial multi-omics domain discovery. ProtoPSR stabilizes both local clustering structure and global pairwise relations. It integrates multiple modalities using a dual-graph backbone with stabilized dynamic graph refinement, and improves clustering via prototype-aware contrastive learning with confidence-guided updates. In addition, a pairwise similarity regularization aligns embedding similarities with pseudo same-cluster relations, improving global consistency. Extensive experiments on spatial multi-omics benchmarks show that ProtoPSR consistently outperforms representative baselines in clustering accuracy and pairwise agreement, and produces more spatially coherent domain maps. Moreover, ProtoPSR demonstrates strong robustness under modality-specific feature corruption.