Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data
Abstract
:1. Introduction
2. Results
2.1. Missing Values Simulation and Imputation Evaluation Using HR GC–MS Metabolomics Data for Replicates of NIST Plasma
2.2. Evaluation of RF, GRR, and BPCA Imputation Methods on NHP Plasma
2.3. Evaluation of RF, GRR, and BPCA Imputation Methods Using Metabolomics Data from Baboon Liver Biopsy Samples
2.4. In-Depth Evaluation of RF Imputation Accuracy at Wide Range of Missingness Using the Entire Baboon Liver HR GC–MS Metabolomics Dataset
3. Discussion
4. Materials and Methods
4.1. Chemicals and Reagents
4.2. Sample Processing
4.3. GC-HR Orbitrap MS Data Acquisition and Preprocessing
4.4. Generation of Missing Values
4.5. Evaluation of Imputation Methods
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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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
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 StyleAmpong, 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