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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| Published in | Genome Biology Vol. 19; no. 1; p. 196 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central
12.11.2018
BMC |
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| 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. |
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| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 196 |
| Author | Chen, Mengjie Zhou, Xiang |
| Author_xml | – sequence: 1 givenname: Mengjie orcidid: 0000-0003-1579-087X surname: Chen fullname: Chen, Mengjie – sequence: 2 givenname: Xiang surname: Zhou fullname: Zhou, Xiang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30419955$$D View this record in MEDLINE/PubMed |
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| Snippet | We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the... Abstract We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the... |
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| SubjectTerms | 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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| Title | VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies |
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