Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 6 April 2022 / Approved: 12 April 2022 / Online: 12 April 2022 (09:49:42 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

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) 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.

Keywords

metabolomics; HR GCMS; Imputation; Missing values

Subject

Medicine and Pharmacology, Other

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