Rxivist logo

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

By Yinlei Hu, Bin Li, Wen Zhang, Nianping Liu, Pengfei Cai, Falai Chen, Kun Qu

Posted 04 Dec 2019
bioRxiv DOI: 10.1101/864488

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 (bLRMD). WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations, and improved the clustering of cells, performing impressively for applications with multiple cell type datasets with high dropout rates. Overall, this study demonstrates 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 scRNA-seq datasets. ### Competing Interest Statement The authors have declared no competing interest.

Download data

  • Downloaded 547 times
  • Download rankings, all-time:
    • Site-wide: 51,484
    • In bioinformatics: 5,273
  • Year to date:
    • Site-wide: 41,692
  • Since beginning of last month:
    • Site-wide: 44,464

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide