Summary of project PR001350

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, 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.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

Project ID: PR001350
Project DOI:doi: 10.21228/M86X36
Project Title:Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabo-lomics Data
Project Summary: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 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.
Institute:Wake Forest School of Medicine
Department:Department of Internal Medicine
Laboratory:Olivier Lab
Last Name:Ampong
First Name:Isaac
Address:Center for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University, Winston-Salem, North Carolina, United States
Email:iampong@wakehealth.edu
Phone:3367162091

Summary of all studies in project PR001350

Study IDStudy TitleSpeciesInstituteAnalysis
(* : Contains Untargted data)
Release
Date
VersionSamplesDownload
(* : Contains raw data)
ST002132 Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data Papio hamadryas Wake Forest School of Medicine MS 2022-04-27 1 242 Uploaded data (89.7G)*
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