Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics

Autor:	Sun, Xingzhi, Xu, Charles, Rocha, João F., Liu, Chen, Hollander-Bodie, Benjamin, Goldman, Laney, DiStasio, Marcello, Perlmutter, Michael, Krishnaswamy, Smita
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.09469 Zobrazit plný text záznamu View this record from Arxiv