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VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

Author:
Chen, Mengjie, Zhou, Xiang
Source:
Genome biology 2018 v.19 no.1 pp. 196
ISSN:
1474-760X
Subject:
gene expression, regression analysis, transcriptome
Abstract:
We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments.
Agid:
6207780