Unified Representation Learning for Spatial Multi-Omics.
basic_science · Level V
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- Record sourced from PubMed, PMID 42635533.
- Also identified by DOI 10.1093/bioinformatics/btag629.
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Abstract
Integrating spatial multi-omics data is crucial for decoding tissue function, yet existing methods struggle with noise and cross-modal alignment. 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. 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.