spAttClu: A spatial domain clustering model lev-eraging spatially-weighted graph attention and contrastive learning

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ID: 317414
2026
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Abstract
MOTIVATION: The rapid growth of spatial transcriptomics data holds potential for deep understanding of spatial specificity and tissue heterogeneity. Recognizing spatial domains is a fundamental step for deciphering tissue functional architecture and dissecting tissue heterogeneity. However, existing models typically define adjacency relations using static weights, which cannot dynamically adjust neighbor importance based on expression context, thereby limiting the accuracy and robustness of spatial domain recognition. RESULTS: We propose spAttClu, a clustering model integrating spatially-weighted graph attention with contrastive learning. It adaptively learns neighbor contributions in varying contexts through a distance-weighted graph attention mechanism and enhances embedding discriminability via multi-level contrastive learning. spAttClu demonstrates superior clustering performance on the DLPFC dataset. Moreover, it shows cross-platform adaptability and enables vertical/horizontal inte-gration of multiple tissue slices. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164858791 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tianjiao Zhang, Ruolan Zhang, Hongfei Zhang, Zhongqian Zhao, Ruihan Wang, Shenghe Li, Yucai Jiang, Binyang Wei, G Wang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag384
URL
https://doi.org/10.1093/bioinformatics/btag384
Keywords Keywords not found

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