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J Proteome Res. 2019 Mar 1;18(3):1446-1450. doi: 10.1021/acs.jproteome.8b00893. Epub 2018 Dec 31.

MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment.

Author information

1
Computational Biology Department, School of Computer Science , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States.
2
Metabolomics Platform , Broad Institute of MIT and Harvard , Cambridge , Massachusetts 02142 , United States.
3
Department of Preventive Medicine and Public Health, School of Medicine , University of Navarra , 31009 Pamplona , Spain.
4
Ciber Fisiopatología de la Obesidad y Nutrición (CIBEROBN) , Instituto de Salud Carlos III (ISCIII) , 28029 Madrid , Spain.
5
Human Nutrition Department, Hospital Universitari Sant Joan, Institut d'Investigació Sanitaria Pere Virgili , Universitat Rovira i Virgili , E-43204 Reus , Spain.
6
Department of Preventive Medicine , University of Valencia , 46010 Valencia , Spain.
7
Department of Preventive Medicine , University of Malaga , 29071 Malaga , Spain.
8
Institute of Health Sciences , Instituto de Investigación Sanitaria de Palma , 07120 Palma de Mallorca , Spain.
9
Department of Family Medicine, Primary Care Division of Sevilla , San Pablo Health Center , 41007 Sevilla , Spain.
10
Cardiovascular and Nutrition Research Group, IMIM , Institut de Recerca Hospital del Mar , Parc de Salut Mar , 08003 Barcelona , Spain.
11
Department of Cardiology , University Hospital of Alava , 01009 Vitoria , Spain.
12
Research Institute of Biomedical and Health Sciences , University of Las Palmas de Gran Canaria , 35016 Las Palmas , Spain.

Abstract

High-throughput metabolomics using liquid chromatography and mass spectrometry (LC/MS) provides a useful method to identify biomarkers of disease and explore biological systems. However, the majority of metabolic features detected from untargeted metabolomics experiments have unknown ion signatures, making it critical that data should be thoroughly quality controlled to avoid analyzing false signals. Here, we present a postalignment method relying on intermittent pooled study samples to separate genuine metabolic features from potential measurement artifacts. We apply the method to lipid metabolite data from the PREDIMED (PREvención con DIeta MEDi-terránea) study to demonstrate clear removal of measurement artifacts. The method is publicly available as the R package MetProc, available on CRAN under the GPL-v2 license.

KEYWORDS:

measurement artifact; missing pattern; pooled QC sample; untargeted metabolomics

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