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

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.