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

• DOI: 10.1093/bioinformatics/btz435
• Publication Date: 2019-05-21T11:18:00Z
• Authors: Xiao-Fei Zhang; Le Ou-Yang; Shuo Yang; Xing-Ming Zhao; Xiaohua Hu; Hong Yan
• Keywords: Dropout (neural networks); Imputation (statistics); Ensemble Learning; R package

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 introduces ensemble learning method for imputing scRNA-seq EnImpute combines the results obtained from multiple imputation methods to generate more accurate result. A Shiny application developed provide easier implementation and visualization. Experiment show outperforms individual state-of-the-art almost all situations. useful correcting noisy data before performing analysis. Availability The are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Bioinformatics online.