The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data
Open accessAbstract
Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student’s t -test followed by correlation analysis was conducted to evaluate the effects on univariate statistics. Our findings demonstrated that RF performed the best for MCAR/MAR and QRILC was the favored one for left-censored MNAR. Finally, we proposed a comprehensive strategy and developed a public-accessible web-tool for the application of missing value imputation in metabolomics ( https://metabolomics.cc.hawaii.edu/software/MetImp/ ).
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Cites
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
Data Analysis Using Regression and Multilevel/Hierarchical Models
Gelman, A., & Hill, J. (2006) · doi:10.1017/cbo9780511790942 · Cited by 13,597
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
MissForest—Non-parametric missing value imputation for mixed-type data
Stekhoven, D. J., & Bühlmann, P. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr597 · Cited by 6,156
Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., & Siuzdak, G. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,262
Missing Value Estimation Methods for DNA Microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., & Altman, R. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.520 · Cited by 4,230
MetaboAnalyst 3.0—making metabolomics more meaningful
Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv380 · Cited by 2,676
MetaboAnalyst: a web server for metabolomic data analysis and interpretation
Xia, J., Psychogios, N., Young, N., & Wishart, D. S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp356 · Cited by 2,266
Mass spectrometry‐based metabolomics
Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007) · Mass Spectrometry Reviews · doi:10.1002/mas.20108 · Cited by 2,241
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Cited by
Machine Learning Applications for Mass Spectrometry-Based Metabolomics
Liebal, U. W., Phan, A. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020) · Metabolites · doi:10.3390/metabo10060243 · Cited by 357
A Review on Quantitative Multiplexed Proteomics
Pappireddi, N., Martin, L., & Wühr, M. (2019) · ChemBioChem · doi:10.1002/cbic.201800650 · Cited by 299
Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181
“When an assumption of MCAR or MAR is used, the missingness is said to be ignorable and methods designed to handle missing values are applied to both cases (Gelman and Hill, 2006; Fang et al., 2018; Wei et al., 2018).”
BackgroundMethodDecoding the Transcriptional Response to Ischemic Stroke in Young and Aged Mouse Brain
Androvic, P., Kirdajova, D., Tureckova, J., Zucha, D., Rohlova, E., Abaffy, P., Kriska, J., Valny, M., Anderova, M., Kubista, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107777 · Cited by 118
Metadata
- Authors
- Wei, R., Wang, J., Su, M., Jia, E., Chen, S., Chen, T., & Ni, Y.
- Venue
- Scientific Reports
- Published
- 2018-01-12
- Volume
- 8
- Issue
- 1
- Pages
- 663-663
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2951837249
- Cited by
- 707
- References
- 33
- FWCI
- 16.95
- Type
- journal article
- Language
- en
- Open access
- gold
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