Liu Tong, Liu Zihuan, Sun Wenke, Shankar Adeethyia, Zhao Yongzhong, Wang Xiaodi
Department of Mathematical Sciences, Tsinghua University, Beijing, China.
Data and Statistical Science, AbbVie, Chicago, Illinois, United States of America.
PLoS Comput Biol. 2025 May 23;21(5):e1013060. doi: 10.1371/journal.pcbi.1013060. eCollection 2025 May.
Wavelet analysis has been recognized as a widely used and promising tool in the fields of signal processing and data analysis. However, the application of wavelet-based method in single-cell RNA sequencing (scRNA-seq) data is little known. Here, we present M-band wavelet-based scRNA-seq multi-view clustering of cells (WMC). We applied for integration of M-band wavelet analysis and uniform manifold approximation and projection (UMAP) to a panel of single cell sequencing datasets by breaking up the data matrix into an approximation or low resolution component and M-1 detail or high resolution components. Our method is armed with multi-view clustering of cell types, identity, and functional states, enabling missing cell types visualization and new cell types discovery. Distinct to standard scRNA-seq workflow, our wavelet-based approach is a new addition to uncover rare cell types with a fine resolution.
小波分析已被公认为是信号处理和数据分析领域中一种广泛使用且很有前景的工具。然而，基于小波的方法在单细胞RNA测序（scRNA-seq）数据中的应用却鲜为人知。在此，我们提出了基于M带小波的单细胞RNA测序细胞多视图聚类（WMC）方法。我们通过将数据矩阵分解为一个近似或低分辨率分量以及M - 1个细节或高分辨率分量，将M带小波分析与均匀流形近似和投影（UMAP）应用于一组单细胞测序数据集。我们的方法具备对细胞类型、身份和功能状态的多视图聚类能力，能够实现缺失细胞类型的可视化以及新细胞类型的发现。与标准的scRNA-seq工作流程不同，我们基于小波的方法是一种以高分辨率揭示稀有细胞类型的新方法。
