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HyperNiche: Learning Heterophilic Cellular Niches with Hypergraph Neural Networks
Md Ishtyaq Mahmud1, Tania Banerjee1
1Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77479.
Biorxiv : the Preprint Server for Biology
|June 12, 2026
Summary
HyperNiche models complex cellular niches using hypergraphs, capturing diverse cell interactions beyond simple similarity. This approach enhances the understanding of spatial tissue organization and tumor microenvironments.
Area of Science:
- Computational Biology
- Spatial Transcriptomics
- Systems Biology
Background:
- Conventional graph-based methods often oversimplify cellular interactions by focusing on pairwise similarities, leading to homogeneous cluster identification.
- Understanding complex, heterogeneous cellular niches within spatial transcriptomics data requires methods that capture higher-order relationships.
Purpose of the Study:
- To introduce HyperNiche, a novel hypergraph-based framework for modeling higher-order, heterogeneous cellular niches from spatial transcriptomics data.
- To overcome limitations of pairwise similarity-based methods in capturing diverse cell type interactions within niches.
Main Methods:
- HyperNiche utilizes a hypergraph framework with anchor-centered hyperedges, employing a compatibility-driven mechanism to model both homophilic and heterophilic cell relationships.
- The model decouples node roles into anchor and member representations and integrates spatial geometry into hyperedge construction.
- Evaluation was performed on high-plex Xenium spatial transcriptomics datasets from breast and lung cancer tissue microarrays.
Main Results:
- HyperNiche demonstrated improved clustering performance (ARI, NMI) and biological interpretability compared to state-of-the-art graph-based baselines.
- Analysis revealed that HyperNiche generates hyperedges with significantly higher intra-edge feature diversity, indicating superior capture of heterogeneous cellular niches.
- The framework successfully identified multicellular niches spanning diverse cell types.
Conclusions:
- Higher-order relational modeling is crucial for accurately understanding complex spatial tissue organization and tumor microenvironments.
- HyperNiche provides an advanced computational framework for dissecting cellular heterogeneity and interactions within spatial contexts.
- The results underscore the potential of hypergraph approaches in advancing spatial biology research.
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