Aug 2026· Bioinformatics· Vol 42· 0 citations· 39 references
Medicine
TL;DR
SpaAlign is introduced, a novel two-stage framework for the robust integration of multiple omics and histological data that creates a unified semantic space, followed by a self-supervised clustering stage that enforces structural coherence.
Abstract
Abstract Motivation Integrating spatial multi-omics data is crucial for decoding tissue function, yet existing methods struggle with noise and cross-modal alignment. Results We introduce SpaAlign, a novel two-stage framework for the robust integration of multiple omics and histological data. At its core, a contrastive learning stage creates a unified semantic space, followed by a self-supervised clustering stage that enforces structural coherence. Our evaluations demonstrate its effectiveness in accurately delineating the capsule-cortex-medulla architecture in human lymph nodes and precisely identifying germinal centers in human tonsil data, outperforming baseline methods. Availability and Implementation The source code is available at https://github.com/VitaIntelli-CQU/SpaAlign. The source code is archived at https://doi.org/10.5281/zenodo.20250508.
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Ning-Hui Hao, Bo-Shen Yan, Dong Li et al.· Proceedings of the 32nd ACM...· 0 citations
ProtoPSR is proposed, an unsupervised framework for robust spatial multi-omics domain discovery that stabilizes both local clustering structure and global pairwise relations and demonstrates strong robustness under modality-specific feature corruption.
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A novel method of multimodal spatial omics fusion, named mmspao, is proposed to obtain cross-modal interactive features and combine them with the features of each modality to obtain fusion results at the spatial resolution.
