EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

Abstract

Summary: Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis.

Availability and implementation: The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute.

Supplementary information: Supplementary data are available at Bioinformatics online.