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Updated: Jan 31, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment
Mark D Chaffin, Liu Cao1, Amy A Deik2
1Computational Biology Department, School of Computer Science , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States.
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.
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