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Filtering procedures for untargeted LC-MS metabolomics data
2019
BMC Bioinformatics
Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our
doi:10.1186/s12859-019-2871-9
fatcat:5gaw3jbbwncwlbl3tkfnw67a5u