Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics
This provides a method for analyzing complex interactions in biomedical data like spatial transcriptomics, though it appears incremental as it extends existing graph wavelet concepts to hypergraphs.
The authors tackled the problem of modeling higher-order relationships in complex data by introducing hypergraph diffusion wavelets, which they applied to spatial transcriptomics to represent disease-relevant cellular niches for Alzheimer's disease, demonstrating utility for biomedical discovery.
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergraphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer's disease.