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

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 exam

SUBMITTER: Ampong I 

PROVIDER: S-EPMC9144635 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature