WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition. Issue 5 (8th April 2021)

• Record Type: Journal Article
• Authors: Hu, Yinlei; Li, Bin; Zhang, Wen; Liu, Nianping; Cai, Pengfei; Chen, Falai; Qu, Kun
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-04-08
• Subjects: single-cell RNA-seq -- imputation -- denoising -- matrix decomposition; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab085
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: The low capture rate of expressed RNAs from single-cell sequencing technology is one of the major obstacles to downstream functional genomics analyses. Recently, a number of imputation methods have emerged for single-cell transcriptome data, however, recovering missing values in very sparse expression matrices remains a substantial challenge. Here, we propose a new algorithm, WEDGE (WEighted Decomposition of Gene Expression), to impute gene expression matrices by using a biased low-rank matrix decomposition method. WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations and improved the clustering of cells, performing impressively for applications with sparse datasets. Overall, this study shows a potent approach for imputing sparse expression matrix data, and our WEDGE algorithm should help many researchers to more profitably explore the biological meanings embedded in their single-cell RNA sequencing datasets. The source code of WEDGE has been released at https://github.com/QuKunLab/WEDGE .