WEDGE: recovery of gene expression values for sparse single-cell RNA-seq datasets using matrix decomposition

Hu, Y.; Li, B.; Liu, N.; Cai, P.; Chen, F.; Qu, K.

doi:10.1101/864488

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bioRxiv · 10.1101/864488

WEDGE: recovery of gene expression values for sparse single-cell RNA-seq datasets using matrix decomposition

Hu, Y.·Li, B.·Liu, N.·Cai, P.·Chen, F.·Qu, K.

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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 (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.

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Hu, Y., Li, B., Liu, N., Cai, P., Chen, F., Qu, K.. 2019-12-04. WEDGE: recovery of gene expression values for sparse single-cell RNA-seq datasets using matrix decomposition. https://doi.org/10.1101/864488

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