VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies.
Mengjie ChenXiang ZhouPublished in: Genome biology (2018)
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.
Keyphrases
- single cell
- rna seq
- gene expression
- induced apoptosis
- high throughput
- cell cycle arrest
- dna methylation
- machine learning
- poor prognosis
- high resolution
- endoplasmic reticulum stress
- case control
- physical activity
- neural network
- mesenchymal stem cells
- signaling pathway
- deep learning
- mass spectrometry
- genome wide
- electronic health record
- liquid chromatography