Journal article
EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning
Bioinformatics, v 35(22), pp 4827-4829
01 Nov 2019
PMID: 31125056
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
Metrics
Details
- Title
- EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning
- Creators
- Xiao-Fei Zhang - Central China Normal UniversityLe Ou-Yang - Shenzhen UniversityShuo Yang - Wuhan No.1 HospitalXing-Ming Zhao - Fudan UniversityXiaohua Hu - Drexel UniversityHong Yan - City University of Hong Kong
- Publication Details
- Bioinformatics, v 35(22), pp 4827-4829
- Publisher
- Oxford University Press
- Grant note
- 17ZR1445600 / Natural Science Foundation of Shanghai (10.13039/100007219) C1007-15G; 11200818 / Hong Kong Research Grants Council 11871026; 61532008; 61602309; 61772368; 61602347; 91530321; 61572363 / National Natural Science Foundation of China (10.13039/501100001809) 2017077 / Natural Science Foundation of SZU 2018CFB521; CCNU18TS026 / Natural Science Foundation of Hubei province (10.13039/501100003819) JCYJ20170817095210760 / Shenzhen Research and Development program
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000501728500050
- Scopus ID
- 2-s2.0-85074965590
- Other Identifier
- 991019167605004721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability