Summary of Study ST002132
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
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.
| Study ID | ST002132 |
| Study Title | Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data |
| Study 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 |
| 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 |
| iampong@wakehealth.edu | |
| Phone | 3367162091 |
| Submit Date | 2022-04-01 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | GC-MS |
| Release Date | 2022-04-27 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Collection:
| Collection ID: | CO002210 |
| Collection Summary: | The NIST plasma metabolomics dataset consisted of 150 replicate samples which were bought from commercial vendors. The 12 batched datasets were pooled, aligned, and processed using open source software MS-DIAL (v4.6). The second dataset was generated from metabolic profiling of 45 baboon plasma samples collected from 35 females in the age range of 6-23 years and 10 males in the same age range. All 45 plasma samples were analyzed using an untargeted EI-GC-MS approach as described above. The third dataset consists of another EI-GC-MS analysis of metabolites extracted from 47 liver biopsy samples collected from the same adult healthy baboons as the plasma which included 39 females and 8 males in the age range of 6-23 years. |
| Sample Type: | Liver |
