Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics
Machine Learning
2024-09-17 v1 Machine Learning
Signal Processing
Quantitative Methods
Abstract
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.
Cite
@article{arxiv.2409.09469,
title = {Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics},
author = {Xingzhi Sun and Charles Xu and João F. Rocha and Chen Liu and Benjamin Hollander-Bodie and Laney Goldman and Marcello DiStasio and Michael Perlmutter and Smita Krishnaswamy},
journal= {arXiv preprint arXiv:2409.09469},
year = {2024}
}