(804.3) M-Band Wavelet-Based Imputation of scRNA-seq Matrix and Multi-view Clustering of Cells

Poster Board Number: A236

Zihuan Liu (Yale University), Tong Liu (Tsinghua University), Wenke Sun (University of Massachusetts ), Yongzhong Zhao (Frontage Laboratories), Xiaodi Wang (Western Connecticut State University)

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.