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- Publisher Website: 10.1371/journal.pcbi.1005973
- Scopus: eid_2-s2.0-85041401127
- PMID: 29385130
- WOS: WOS:000423845000047
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Article: GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies
Title | GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies |
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Authors | |
Issue Date | 2018 |
Citation | PLoS Computational Biology, 2018, v. 14, n. 1, article no. e1005973 How to Cite? |
Abstract | Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp. |
Persistent Identifier | http://hdl.handle.net/10722/342554 |
ISSN | 2023 Impact Factor: 3.8 2023 SCImago Journal Rankings: 1.652 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wei, Runmin | - |
dc.contributor.author | Wang, Jingye | - |
dc.contributor.author | Jia, Erik | - |
dc.contributor.author | Chen, Tianlu | - |
dc.contributor.author | Ni, Yan | - |
dc.contributor.author | Jia, Wei | - |
dc.date.accessioned | 2024-04-17T07:04:38Z | - |
dc.date.available | 2024-04-17T07:04:38Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | PLoS Computational Biology, 2018, v. 14, n. 1, article no. e1005973 | - |
dc.identifier.issn | 1553-734X | - |
dc.identifier.uri | http://hdl.handle.net/10722/342554 | - |
dc.description.abstract | Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp. | - |
dc.language | eng | - |
dc.relation.ispartof | PLoS Computational Biology | - |
dc.title | GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1371/journal.pcbi.1005973 | - |
dc.identifier.pmid | 29385130 | - |
dc.identifier.scopus | eid_2-s2.0-85041401127 | - |
dc.identifier.volume | 14 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | article no. e1005973 | - |
dc.identifier.epage | article no. e1005973 | - |
dc.identifier.eissn | 1553-7358 | - |
dc.identifier.isi | WOS:000423845000047 | - |