Unified Representation Learning for Spatial Multi-Omics

Clicks: 32
ID: 326321
2026
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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.
Reference Key
openalex_W7161493972 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Haoxuan Li, Sijie Wan, Hongyu Zhang, Zheng Wang, Yuansong Zeng
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag629
URL
https://doi.org/10.1093/bioinformatics/btag629
Keywords Keywords not found

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