Abstract
AbstractWavelet 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 (WIMC). We applied 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 a trend (low frequency or low resolution) component and (M-1) fluctuation (high frequency or high resolution) components. We leverage a non-parametric wavelet-based imputation algorithm of sparse data that integrates M-band wavelet transform for recovering dropout events of scRNA-seq datasets. 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 resolve the notorious chaotic sparsity of scRNA-seq matrix and to uncover rare cell types with a fine-resolution.Author summaryWe develop M-band wavelet-based imputation of scRNA-seq matrix and multi-view clustering 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 a trend (low frequency or low resolution) component and (M– 1) fluctuation (high frequency or high resolution) components. Our method enables us to efficiently impute sparse scRNA-seq data matrix and to examine multi-view clustering of cell types, identity, and functional states, potentializing missing cell types recovery, fine rare cell types discovery, as well as functional cell states exploration.
Publisher
Cold Spring Harbor Laboratory