Explore ARCExplore ARC

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

By December 5, 2018Research

This research was supported by funding from the Michigan Center for Single-Cell Genomic Data Analytics—a part of the Michigan Institute for Data Science.

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

Published in
Genome Biology, November 12, 2018

DOI
10.1186/s13059-018-1575-1

Authors
Mengie Chen and Xiang Zhou

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.