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- Title
EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning.
- Authors
Zhang, Xiao-Fei; Ou-Yang, Le; Yang, Shuo; Zhao, Xing-Ming; Hu, Xiaohua; Yan, Hong
- 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.
- Subjects
SCHOOL dropouts; INTERNET servers; DATA; VISUALIZATION; BIOINFORMATICS
- Publication
Bioinformatics, 2019, Vol 35, Issue 22, p4827
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btz435