TL;DR
This paper introduces hypergraph diffusion wavelets to effectively model higher-order relationships in data, demonstrating their utility in spatial transcriptomics for biomedical discovery related to Alzheimer's disease.
Contribution
The paper presents a novel hypergraph wavelet framework with advantageous spectral and spatial properties for analyzing complex higher-order data relationships.
Findings
Effective representation of cellular niches in spatial transcriptomics.
Application to Alzheimer's disease reveals disease-relevant cellular interactions.
Demonstrates the utility of hypergraph wavelets in biomedical data analysis.
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.
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Taxonomy
MethodsDiffusion
