WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition

• Published in: Briefings in Bioinformatics 2021-04, Vol.22 (5)
• Main Authors: Hu, Yinlei, Li, Bin, Zhang, Wen, Liu, Nianping, Cai, Pengfei, Chen, Falai, Qu, Kun
• Format: Article
• Language: English
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbab085

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.