Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity - Huang, Meng; Ye, Xiucai; Li, Hongmin; Sakurai, Tetsuya (Frontiers in Genetics, 2022)
Single-cell RNA-sequencing (scRNA-seq) technologies enable the measurements of gene expression at individual cells, which is helpful for exploring cancer heterogeneity and precision medicine. However, various technical noises lead to the false zero values (missing gene expression values) in scRNA-seq data, termed dropout events. These zero values complicate the analysis of cell patterns, which affects high-precision analysis in intra-tumor heterogeneity. Recovering missing gene expression values is still a major obstacle in scRNA-seq data analysis. In this study, taking the cell heterogeneity into consideration, we develop a novel method, called ingle cell Gauss-Newton Gene expression Imputation (scGNGI), to impute the scRNA-seq expression matrices by using a low-rank matrix completion. The obtained experiment results on simulated datasets and real scRNA-seq datasets show that scGNGI can more effectively impute the missing values for scRNA-seq gene expression and improve down-stream analysis compared to other state-of-the-art methods. Moreover, we show that the proposed method can better preserve gene expression variability among cells. Overall, this study helps explore the complex biological system and precision medicine in scRNA-seq data.