Abstract
Abstract Untargeted metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases. This challenge is faced employing state-of-the-art analytical platforms, including liquid chromatography coupled to mass spectrometry (LC-MS) instruments. LC-MS instruments provide suitable sensitivity, selectivity and analytical throughput and generate information-rich data structures. The inherent complexity of LC-MS metabolomics data sets requires the use of quality control (QC) and quality assurance (QA) tools in order to allow the extraction of biologically meaningful information. These tools are embedded in different stages of the metabolomics workflow, including data acquisition and preprocessing. The chapter describes how to integrate different types of QC checkpoints during LC-MS analysis and discusses qualitative and quantitative data quality estimators that can be employed for QC. Finally, approaches for improving data quality are described.
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