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.
Yu Zhang, Mengzhu Wang, Luoxi Jing et al.· Proceedings of the 32nd ACM...· 0 citations
Understanding gene regulation at single-cell resolution is crucial for unraveling development, disease, and cellular identity. We introduce single-cell regulatory graph attention network (scReGAT), a deep learning framework that integrates prior knowledge of cis-regulatory element (cRE)-gene and transcription factor-gene interactions to reconstruct cell-specific regulatory networks. Central to scReGAT is a knowledge-guided regulatory graph (kRG), which combines experimentally validated regulatory interactions with cell-resolved chromatin accessibility profiles. These graphs serve as the foundation for training a Graph Attention Network (GAT) to predict gene expression and quantify the contribution of specific regulatory interactions using an interpretable regulatory score for each edge. In benchmarking across five single-cell multi-omics datasets, scReGAT successfully recapitulates known cell-type-specific cRE-gene interactions. In both neuroblastoma and osteogenic differentiation systems, it uncovers dynamic regulatory rewiring that predicts transcriptional transitions. Furthermore, by integrating genome-wide association studies loci from Alzheimer's disease, multiple sclerosis, and schizophrenia, scReGAT identifies disease-associated cell types and uncovers candidate regulatory mechanisms underlying complex trait associations. These results position scReGAT as a robust and generalizable framework for decoding long-range gene regulation at single-cell resolution. The source code of scReGAT can be accessed at https://github.com/TianLab-Bioinfo/scReGAT/ and https://ngdc.cncb.ac.cn/biocode/tool/BT008081.
