M-Band Wavelet-Based Imputation of scRNA-seq Matrix and Multi-view Clustering of Cells.

Wavelet analysis has been recognized as a cutting-edge and promising tool in the fields of signal processing and data analysis. However, application of wavelet-based method in single-cell RNA sequencing (scRNA-seq) data is little known. Here, we present M-band wavelet-based imputation of scRNA-seq matrix and multi-view clustering of cells. We applied combination 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 a trend (low frequency or low resolution) component and M-1 fluctuation (high frequency or high resolution) components. Our hybrid analysis enables us to examine multi-view clustering of cell types, identity, and functional states. Distinct to standard scRNA-seq workflow, our wavelet-based approach is a new addition to resolve the notorious chaotic sparsity of scRNA-seq matrix and to uncover rare cell types with a fine-resolution.