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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

16 Scopus Citations
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Author(s)

  • Xiao-Fei Zhang
  • Le Ou-Yang
  • Shuo Yang
  • Xing-Ming Zhao
  • Xiaohua Hu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)4827-4829
Journal / PublicationBioinformatics
Volume35
Issue number22
Online published24 May 2019
Publication statusPublished - 15 Nov 2019

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. 

Citation Format(s)

EnImpute : imputing dropout events in single-cell RNA-sequencing data via ensemble learning. / Zhang, Xiao-Fei; Ou-Yang, Le; Yang, Shuo; Zhao, Xing-Ming; Hu, Xiaohua; Yan, Hong.

In: Bioinformatics, Vol. 35, No. 22, 15.11.2019, p. 4827-4829.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review