MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment.
Mark D ChaffinLiu CaoAmy A DeikClary B ClishFrank B HuMiguel A Martínez-GonzálezCristina RazquinMonica BulloDolores CorellaEnrique Gómez-GraciaMiquel FiolRamon EstruchJosé LapetraMontserrat FitóFernando ArósLluís Serra-MajemEmilio RosLiming LiangPublished in: Journal of proteome research (2018)
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.
Keyphrases
- mass spectrometry
- liquid chromatography
- high resolution mass spectrometry
- gas chromatography
- high throughput
- capillary electrophoresis
- high performance liquid chromatography
- high resolution
- tandem mass spectrometry
- randomized controlled trial
- magnetic resonance imaging
- high intensity
- simultaneous determination
- machine learning
- clinical trial
- cone beam
- solid phase extraction
- open label
- deep learning
- dna methylation
- big data
- double blind
- fatty acid
- phase iii
- gas chromatography mass spectrometry