Optimization of Imputation Strategies for High-Resolution Gas Chromatography&ndash;Mass Spectrometry (HR GC&ndash;MS) Metabolomics Data

Ampong, Isaac; Zimmerman, Kip D.; Nathanielsz, Peter W.; Cox, Laura A.; Olivier, Michael

doi:10.3390/metabo12050429

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Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data

^2,3,

^1,3

and

Michael Olivier

^1,*

Center for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University, Winston-Salem, NC 27157, USA

Center for the Study of Fetal Programming, University of Wyoming, Laramie, WY 82071, USA

Southwest National Primate Research Center, San Antonio, TX 78227, USA

Author to whom correspondence should be addressed.

Academic Editor: Leonardo Tenori

Metabolites 2022, 12(5), 429; https://doi.org/10.3390/metabo12050429

Received: 6 April 2022 / Revised: 7 May 2022 / Accepted: 9 May 2022 / Published: 11 May 2022

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Abstract

Gas chromatography–coupled mass spectrometry (GC–MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC–MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values. View Full-Text

Keywords: metabolomics; HR GC–MS; imputation missing values

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MDPI and ACS Style

Ampong, I.; Zimmerman, K.D.; Nathanielsz, P.W.; Cox, L.A.; Olivier, M. Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data. Metabolites 2022, 12, 429. https://doi.org/10.3390/metabo12050429

AMA Style

Ampong I, Zimmerman KD, Nathanielsz PW, Cox LA, Olivier M. Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data. Metabolites. 2022; 12(5):429. https://doi.org/10.3390/metabo12050429

Chicago/Turabian Style

Ampong, Isaac, Kip D. Zimmerman, Peter W. Nathanielsz, Laura A. Cox, and Michael Olivier. 2022. "Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data" Metabolites 12, no. 5: 429. https://doi.org/10.3390/metabo12050429

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Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data

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