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.

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Study IDST001450
Study TitleFive Easy Metrics of Data Quality for LC-MS based Global Metabolomics
Study SummaryData 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
DepartmentAnesthesiology and Pain
LaboratoryDaniel Raftery
Last NameZhang
First NameXinyu
Address850 Republican Street, Seattle, Washington 98109, United States
Emailxinyu.z@live.com
Phone850-567-2757
Submit Date2020-08-18
Raw Data AvailableYes
Raw Data File Type(s)d
Analysis Type DetailLC-MS
Release Date2020-09-10
Release Version1
Xinyu Zhang Xinyu Zhang
https://dx.doi.org/10.21228/M8Z692
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Data type
SA12379967Agilent 6520 centroid data
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SA12380265Agilent 6520 centroid data
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SA12381552Agilent 6520 centroid data
SA12381656Agilent 6520 centroid data
SA12381757Agilent 6520 centroid data
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SA12382059Agilent 6520 centroid data
SA12382158Agilent 6520 centroid data
SA12382275Agilent 6520 centroid data
SA12382378Agilent 6520 centroid data
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SA12383096Agilent 6520 centroid data
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SA12384876Agilent 6520 centroid data
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SA1238676Agilent 6520 profile data
SA1238687Agilent 6520 profile data
SA12386911Agilent 6520 profile data
SA12387010Agilent 6520 profile data
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SA1238728Agilent 6520 profile data
SA12387325Agilent 6520 profile data
SA1238741Agilent 6520 profile data
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SA12388749Agilent 6520 profile data
SA12388837Agilent 6520 profile data
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SA12389535Agilent 6520 profile data
SA12389636Agilent 6520 profile data
SA12389734Agilent 6520 profile data
SA12389833Agilent 6520 profile data
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