Summary of Study ST002132

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001350. The data can be accessed directly via it's Project DOI: 10.21228/M86X36 This work is supported by NIH grant, U2C- DK119886.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST002132
Study TitleOptimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data
Study SummaryGas 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 imputa-tion 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 demon-strate that Random Forest (RF), Glmnet Ridge Regression (GRR), and Bayesian Principal Com-ponent Analysis (BPCA) shared the lowest Root Mean Squared Error (RMSE) in technical repli-cate 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.
Wake Forest School of Medicine
Last NameAmpong
First NameIsaac
AddressCenter for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University, Winston-Salem, North Carolina, United States
Submit Date2022-04-01
Raw Data AvailableYes
Raw Data File Type(s)mzML
Analysis Type DetailGC-MS
Release Date2022-04-27
Release Version1
Isaac Ampong Isaac Ampong application/zip

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Sample Preparation:

Sampleprep ID:SP002223
Sampleprep Summary:15 μL of plasma or liver samples were subjected to sequential solvent extraction, once each with 1 mL of acetonitrile: isopropanol: water (3:3:2) and 500 μL of acetonitrile: water (1:1) mixtures at 4°C [14]. An internal standard, adonitol (2 μL from 10 mg/ml stock) was added to each aliquot prior to the extraction. The extracts were dried under vacuum at 4°C prior to chemical derivatization (silylation reactions). Blank tubes without samples, were treated similarly as sample tubes and added to account for background noise and other sources of contamination. Samples and blanks were sequentially derivatized with meth-oxyamine hydrochloride (MeOX) and 1% TMCS in N-methyl-N-trimethylsilyl-trifluoroacetamide (MSTFA) or 1% TMCS containing N-(t-butyldimethylsilyl)-N-methyltrifluoroacetamide (MTBSTFA) as described elsewhere [15]. Briefly, the steps involved addition of 20 μL of MeOX (20 mg mL-1) in pyridine incu-bated at 55°C for 60 min followed by trimethylsilylation at 60°C for 60 min after adding 80 μL MTBSTFA.