Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

Scientific Reports

Volumn 8, Issue 1, 2018, Pages

  Wei, Runmin ^a,b   Wang, Jingye ^a   Su, Mingming ^a,c   Jia, Erik ^d   Chen, Shaoqiu ^a,b   Chen, Tianlu ^e   Ni, Yan ^a  

^a UNIVERSITY OF HAWAII CANCER CENTER   (United States)

^b UNIVERSITY OF HAWAII   (United States)

^c Metabo Profile Biotechnology Shanghai Co Ltd   (China)

^d Punahou School   (United States)

^e SHANGHAI JIAO TONG UNIVERSITY AFFILIATED SIXTH PEOPLE S HOSPITAL   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION ANALYSIS; DECOMPOSITION; HAWAII; HUMAN; K NEAREST NEIGHBOR; MASS SPECTROMETRY; METABOLOMICS; PARTIAL LEAST SQUARES REGRESSION; PRINCIPAL COMPONENT ANALYSIS; RANDOM FOREST; SOFTWARE; STUDENT; STUDENT T TEST; UNIVARIATE ANALYSIS; BIOLOGY; CLUSTER ANALYSIS; LEAST SQUARE ANALYSIS; PROCEDURES;

CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; LEAST-SQUARES ANALYSIS; MASS SPECTROMETRY; METABOLOMICS; PRINCIPAL COMPONENT ANALYSIS;

EID: 85040601018     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-19120-0     Document Type: Article

References (34)

1. Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom Rev 26, 51-78, https://doi.org/10.1002/mas.20108 (2007).
2. Bijlsma, S. et al. Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Analytical Chemistry 78, 567-574, https://doi.org/10.1021/ac051495j (2006).
3. Hrydziuszko, O. & Viant, M. R. Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline. Metabolomics 8, 161-174, https://doi.org/10.1007/s11306-011-0366-4 (2012).
4. Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models. 529-542 (Cambridge University Press, 2006).
5. Little, R. J. A. & Rubin, D. B. Statistical Analysis with Missing Data. 11-19 (John Wiley & Sons, 2002).
6. Lazar, C., Gatto, L., Ferro, M., Bruley, C. & Burger, T. Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. Journal of Proteome Research 15, 1116-1125, https://doi.org/10.1021/acs.jproteome.5b00981 (2016).
7. Karpievitch, Y. V., Dabney, A. R. & Smith, R. D. Normalization and missing value imputation for label-free LC-MS analysis. Bmc Bioinformatics 13, S5, https://doi.org/10.1186/1471-2105-13-S16-S5 (2012).
8. Xie, G. et al. Profiling of serum bile acids in a healthy Chinese population using UPLC-MS/MS. J Proteome Res 14, 850-859, https://doi.org/10.1021/pr500920q (2015).
9. Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78, 779-787, https://doi.org/10.1021/ac051437y (2006).
10. Ni, Y., Su, M., Qiu, Y., Jia, W. & Du, X. ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies. Anal Chem 88, 8802-8811, https://doi.org/10.1021/acs.analchem.6b02222 (2016).
11. Yang, J., Zhao, X., Lu, X., Lin, X. & Xu, G. A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis. Frontiers in molecular biosciences 2, 4, https://doi.org/10.3389/fmolb.2015.00004 (2015).
12. Taylor, S. L., Leiserowitz, G. S. & Kim, K. Accounting for undetected compounds in statistical analyses of mass spectrometry 'omic studies. Stat Appl Genet Mol 12, 703-722, https://doi.org/10.1515/sagmb-2013-0021 (2013).
13. Zhan, X., Patterson, A. D. & Ghosh, D. Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data. Bmc Bioinformatics 16, 77, https://doi.org/10.1186/s12859-015-0506-3 (2015).
14. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics (Oxford, England) 17, 520-525, https://doi.org/10.1093/bioinformatics/17.6.520 (2001).
15. Stekhoven, D. J. & Bühlmann, P. Missforest-Non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112-118, https://doi.org/10.1093/bioinformatics/btr597 (2012).
16. Hastie, T., Tibshirani, R. & Sherlock, G. Imputing missing data for gene expression arrays. Technical Report, Division of Biostatistics, Stanford University, 1-9 (1999).
17. Kessler, N. et al. MeltDB 2.0-advances of the metabolomics software system. Bioinformatics 29, 2452-2459, https://doi.org/10.1093/bioinformatics/btt414 (2013).
18. Luedemann, A., von Malotky, L., Erban, A. & Kopka, J. TagFinder: preprocessing software for the fingerprinting and the profiling of gas chromatography-mass spectrometry based metabolome analyses. Methods Mol Biol 860, 255-286, https://doi.org/10.1007/978-1-61779-594-7-16 (2012).
19. Xia, J., Sinelnikov, I. V., Han, B. & Wishart, D. S. MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res 43, W251-257, https://doi.org/10.1093/nar/gkv380 (2015).
20. Katajamaa, M., Miettinen, J. & Oresic, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22, 634-636, https://doi.org/10.1093/bioinformatics/btk039 (2006).
21. Mak, T. D., Laiakis, E. C., Goudarzi, M. & Fornace, A. J. Jr. MetaboLyzer: a novel statistical workflow for analyzing Postprocessed LC-MS metabolomics data. Anal Chem 86, 506-513, https://doi.org/10.1021/ac402477z (2014).
22. Xia, J., Psychogios, N., Young, N. & Wishart, D. S. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 37, W652-660, https://doi.org/10.1093/nar/gkp356 (2009).
23. Huan, T. & Li, L. Counting Missing Values in a Metabolite-Intensity Data Set for Measuring the Analytical Performance of a Metabolomics Platform. Anal. Chem. 87, 1306-1313, https://doi.org/10.1021/ac5039994 (2015).
24. Armitage, E. G., Godzien, J., Alonso-Herranz, V., Lopez-Gonzalvez, A. & Barbas, C. Missing value imputation strategies for metabolomics data. Electrophoresis 36, 3050-3060, https://doi.org/10.1002/elps.201500352 (2015).
25. Gromski, P. S. et al. Influence of missing values substitutes on multivariate analysis of metabolomics data. Metabolites 4, 433-452, https://doi.org/10.3390/metabo4020433 (2014).
26. Di Guida, R. et al. Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics 12, 93, https://doi.org/10.1007/s11306-016-1030-9 (2016).
27. imputeLCMD: A collection of methods for left-censored missing data imputation v. version 2.0 (2015).
28. Ni, Y. et al. Circulating Unsaturated Fatty Acids Delineate the Metabolic Status of Obese Individuals. EBioMedicine 2, 1513-1522, https://doi.org/10.1016/j.ebiom.2015.09.004 (2015).
29. Lei, S. et al. The ratio of dihomo-gamma-linolenic acid to deoxycholic acid species is a potential biomarker for the metabolic abnormalities in obesity. Faseb J 31, 3904-3912, https://doi.org/10.1096/fj.201700055R (2017).
30. R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/(2013).
31. Stacklies, W., Redestig, H., Scholz, M., Walther, D. & Selbig, J. pcaMethods-A bioconductor package providing PCA methods for incomplete data. Bioinformatics 23, 1164-1167, https://doi.org/10.1093/bioinformatics/btm069 (2007).
32. Oba, S. et al. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19, 2088-2096, https://doi.org/10.1093/bioinformatics/btg287 (2003).
33. Multivariate Analysis of Ecological Communities in R: vegan tutorial v. version 2.4-3 (2015).
34. Thévenot, E. A., Roux, A., Xu, Y., Ezan, E. & Junot, C. Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. Journal of Proteome Research 14, 3322-3335, https://doi.org/10.1021/acs.jproteome.5b00354 (2015).