Summary of Study ST001450
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 PR000996. The data can be accessed directly via it's Project DOI: 10.21228/M8Z692 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 | ST001450 |
| Study Title | Five Easy Metrics of Data Quality for LC-MS based Global Metabolomics |
| Study Summary | Data quality in global metabolomics is of great importance for biomarker discovery and systems biology studies. However, comprehensive metrics and methods to evaluate and compare the data quality of global metabolomics data sets are lacking. In this work, we combine newly developed metrics, along with well-known measures, to comprehensively and quantitatively characterize the data quality across two similar LC-MS platforms, with the goal of providing an efficient and improved ability to evaluate the data quality in global metabolite profiling experiments. A pooled human serum sample was run 50 times on two high-resolution LC-QTOF-MS platforms to provide profile and centroid MS data. These data were processed using Progenesis Qi software and then analyzed using five important data quality measures, including retention time drift, compound coverage, missing values and MS reproducibility (2 measures). The coverage was fit to a Gamma distribution versus compound abundance, which was normalized to allow comparison of different platforms. To evaluate missing values, characteristic curves were obtained by plotting the compound detection percentage versus extraction frequency. To characterize reproducibility, the accumulative coefficient of variation (CV) versus percentage of total compounds detected and CV vs intra-class correlation coefficient (ICC) were investigated. Key findings include significantly better performance using profile mode data compared to centroid mode as well quantitatively better performance from the newer, higher resolution instrument. A summary of the results given in tabulated form gives a snapshot of the experimental results and provides a template to evaluate the global metabolite profiling workflow. In total, these measures give a good overall view of data quality in global profiling and allow comparisons of data acquisition strategies and platforms as well as optimization of parameters. |
| Institute | University of Washington |
| Department | Anesthesiology and Pain |
| Laboratory | Daniel Raftery |
| Last Name | Zhang |
| First Name | Xinyu |
| Address | 850 Republican Street, Seattle, Washington 98109, United States |
| xinyu.z@live.com | |
| Phone | 850-567-2757 |
| Submit Date | 2020-08-18 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | d |
| Analysis Type Detail | LC-MS |
| Release Date | 2020-09-10 |
| Release Version | 1 |
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Collection:
| Collection ID: | CO001519 |
| Collection Summary: | Human serum was frozen at -80 C. |
| Sample Type: | Blood (serum) |
