BayesImpute: a Bayesian imputation method for single-cell RNA-seq data

Abstract

Single-cell RNA-sequencing (scRNA-seq) data suffer from a large number of zeros. Such dropout events hinder the downstream data analyses. We propose BayesImpute, a statistical algorithm to impute dropouts in the scRNA-seq data. BayesImpute first identifies likely dropouts based on expression rate and coefficient of variation of genes within cell subpopulation, and then constructs the posterior distribution for each gene and utilizes the posterior mean to impute dropout values. With several simulated and real scRNA-seq datasets, we demonstrate that BayesImpute is capable of effectively identifying dropouts. In addition, BayesImpute successfully recovers the true expression levels of missing values, improves the clustering and visualization of cell subpopulations, and enhances the identification of differential expression genes. We also show that BayesImpute is scalable and fast with minimal memory usage compared with other statistical-based imputation methods.