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stHGNN: Deciphering spatial transcriptomics data via dual hypergraph learning enhancement
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DOI：10.1016/j.patcog.2026.114585.png)
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Abstract
En 中文
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We propose a dual-view hypergraph framework for spatial transcriptomics.
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We model higher-order dependencies for robust spatial domain identification.
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We combine ZINB and smoothing losses to enhance biological consistency.
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Extensive experiments show the superiority of our method in downstream analyses.
Abstract
Spatial Transcriptomics (ST) reveals gene expression patterns within the context of tissue microenvironment by in situ measuring gene expression at native spatial locations, enabling the deciphering of spatial domains. However, existing methods often simplify cellular dependencies to pairwise k-nearest neighbor graphs, overlooking the contextual and higher-order dependencies of cellular networks. To address this issue, we propose stHGNN, a dual-view hypergraph enhanced framework for identifying spatial domains in ST data. stHGNN captures contextual and higher-order cellular dependencies using hypergraphs, adopts hypergraph representation learning from both spatial and gene expression views, and then facilitates cross-view knowledge transfer through a cross-attention mechanism and self-correlation reorganization. Additionally, a multi-view self-supervised clustering strategy is adopted to learn clustering-friendly representations, while hypergraph smoothing is applied to enhance consistency with biological priors. Finally, a zero-inflated negative binomial reconstruction is adopted to handle the inherent over-dispersion and dropouts in ST data. Comprehensive experiments and downstream analysis demonstrate the effectiveness of our stHGNN.
Keywords:
Spatial Transcriptomics
Hypergraph learning
Spatial domain identification
Clustering
Journal
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1.3W
Citations:
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