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bioRxiv · 10.1101/2025.07.27.667021

Hypergraph Neural Networks Reveal Spatial Domains from Single-cell Transcriptomics Data

Soltani, M.·Rueda, L.
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Abstract

I.Spatial transcriptomics enables the measurement of gene expression while preserving spatial context within tissue samples. A key challenge is detecting spatial domains of biologically meaningful cell clusters, typically addressed using graph-based models like SpaGCN and STAGATE. However, these methods only capture pairwise relationships and fail to model complex higher-order interactions. We propose a hypergraph-based framework for spatial transcriptomics using Hypergraph Neural Networks (HGNNs). Our approach constructs hyperedges from top-K densest overlapping subgraphs and integrates histological image features and gene expression profiles. Combined with autoencoders, our model effectively learns expressive node embeddings in an unsupervised setting. Evaluated on a mouse brain dataset, our model achieves the highest iLISI score of 1.843 and outperforms state-of-the-art baselines with an ARI of 0.51 and a Leiden score of 0.60.

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Soltani, M., Rueda, L.. 2025-07-31. Hypergraph Neural Networks Reveal Spatial Domains from Single-cell Transcriptomics Data. https://doi.org/10.1101/2025.07.27.667021

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