Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data
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 e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/12/5/429 |
| _version_ | 1797497942579347456 |
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| author | Isaac Ampong Kip D. Zimmerman Peter W. Nathanielsz Laura A. Cox Michael Olivier |
| author_facet | Isaac Ampong Kip D. Zimmerman Peter W. Nathanielsz Laura A. Cox Michael Olivier |
| author_sort | Isaac Ampong |
| collection | DOAJ |
| description | 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 <i>p</i>-values. |
| first_indexed | 2024-03-10T03:26:20Z |
| format | Article |
| id | doaj.art-b5fe8aba01df4dbbafea541a6945ec3b |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-10T03:26:20Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-b5fe8aba01df4dbbafea541a6945ec3b2023-11-23T12:07:18ZengMDPI AGMetabolites2218-19892022-05-0112542910.3390/metabo12050429Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics DataIsaac Ampong0Kip D. Zimmerman1Peter W. Nathanielsz2Laura A. Cox3Michael Olivier4Center for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University, Winston-Salem, NC 27157, USACenter for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University, Winston-Salem, NC 27157, USACenter for the Study of Fetal Programming, University of Wyoming, Laramie, WY 82071, USACenter for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University, Winston-Salem, NC 27157, USACenter for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University, Winston-Salem, NC 27157, USAGas 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 <i>p</i>-values.https://www.mdpi.com/2218-1989/12/5/429metabolomicsHR GC–MSimputation missing values |
| spellingShingle | Isaac Ampong Kip D. Zimmerman Peter W. Nathanielsz Laura A. Cox Michael Olivier Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data Metabolites metabolomics HR GC–MS imputation missing values |
| title | Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data |
| title_full | Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data |
| title_fullStr | Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data |
| title_full_unstemmed | Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data |
| title_short | Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data |
| title_sort | optimization of imputation strategies for high resolution gas chromatography mass spectrometry hr gc ms metabolomics data |
| topic | metabolomics HR GC–MS imputation missing values |
| url | https://www.mdpi.com/2218-1989/12/5/429 |
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