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

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.

SUBMITTER: Chen M

PROVIDER: S-EPMC6233584 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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

Chen Mengjie M Zhou Xiang X

Genome biology 20181112 1

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 ...[more]

PMID: 30419955

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