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

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 neighborhoo...

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Bibliographic Details
Published inGenome Biology Vol. 19; no. 1; p. 196
Main Authors Chen, Mengjie, Zhou, Xiang
Format Journal Article
LanguageEnglish
Published England BioMed Central 12.11.2018
BMC
Subjects
Bioinformatics
Data processing
DNA methylation
Gene expression
Gene Expression Profiling - methods
genome
Genomes
Genomics
Method
Methods
Parameter estimation
Regression Analysis
Ribonucleic acid
RNA
Sequence Analysis, RNA
Single-Cell Analysis
Stem cells
transcriptome
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Summary: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.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-018-1575-1