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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 <jats:italic>M<\/jats:italic>\u20131 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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013060","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T18:03:06Z","timestamp":1748023386000},"page":"e1013060","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["M-band wavelet-based multi-view clustering of 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