M-band wavelet-based multi-view clustering of cells

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.Author summary: We develop M-band wavelet-based multi-view clustering method of cells. Our new approach integrates M-band wavelet analysis and UMAP to a panel of single cell sequencing datasets via breaking up the data matrix into an approximation or low resolution component and M–1 detail or high resolution components. Our method enables us to examine multi-view clustering of cell types, identity, and functional states, potentializing missing cell types recovery, rare cell types discovery, as well as functional cell states exploration.