Comparative Research of Different Dimension Reduction Methods Combined with RWR Network Smoothing in Single Cell RNA-seq Data

Abstract

Single-cell RNA sequencing (scRNA-seq) has been an important inspiration for the study of biomolecules through its reveal of cell heterogeneity. However, due to the low capture efficiency and frequent drop-out events in the single-cell sequencing process, the scRNA-seq data often has high sparsity and random missing values, which brings great difficulties to the subsequent analysis. The network propagation method based on random walk with restart (RWR) effectively fills in the missing values in the scRNA-seq data and reduces noise by referring to the prior information of gene interaction. Dimensionality reduction is also a commonly used pre-processing method for high-dimensional and sparse scRNA-seq data, which can be combined with the RWR-based data imputation to achieve noise reduction and feature extraction of scRNA-seq data. This article compares the performance of the commonly used single-cell data dimension reduction methods combined with the RWR network smoothing in different type of scRNA-seq data sets, and analyzes their applicability and stability.