[2603.26858] A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis

Computer Science > Machine Learning arXiv:2603.26858 (cs) [Submitted on 27 Mar 2026] Title:A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis Authors:Xiang Xiang Wang, Guo-Wei We View a PDF of the paper titled A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis, by Xiang Xiang Wang and Guo-Wei We View PDF HTML (experimental) Abstract:Single-cell RNA-seq data analysis typically requires representations that capture heterogeneous local structure across multiple scales while remaining stable and interpretable. In this work, we propose a hierarchical sheaf spectral embedding (HSSE) framework that constructs informative cell-level features based on persistent sheaf Laplacian analysis. Starting from scale-dependent low-dimensional embeddings, we define cell-centered local neighborhoods at multiple resolutions. For each local neighborhood, we construct a data-driven cellular sheaf that encodes local relationships among cells. We then compute persistent sheaf Laplacians over sampled filtration intervals and extract spectral statistics that summarize the evolution of local relational structure across scales. These spectral descriptors are aggregated into a unified feature vector for each cell and can be directly used in downstream learning tasks without additional model training. We evaluate HSSE on twelve benchmark single-cell RNA-seq datasets covering diverse biological systems and data scales. Under a consistent clas...