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Web End = Metabolomics (2015) 11:518528 DOI 10.1007/s11306-014-0712-4
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Web End = Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample
Joanna Godzien Vanesa Alonso-Herranz
Coral Barbas Emily Grace Armitage
Received: 9 June 2014 / Accepted: 18 July 2014 / Published online: 27 July 2014 Springer Science+Business Media New York 2014
Abstract The type and use of quality control (QC) samples is a hot topic in metabolomics. QCs are not novel in analytical chemistry; however since the evolution of using QCs to control the quality of data in large scale metabolomics studies (rst described in 2011), the need for detailed knowledge of how to use QCs and the effects they can have on data treatment is growing. A controlled experiment has been designed to illustrate the most advantageous uses of QCs in metabolomics experiments. For this, samples were formed from a pool of plasma whereby different metabolites were spiked into two groups in order to simulate biological biomarkers. Three different QCs were compared: QCs pooled from all samples, QCs pooled from each experimental group of samples separately and QCs provided by an external source (QC surrogate). On the experimentation of different data treatment strategies, it was revealed that QCs collected separately for groups offers the closest matrix to the samples and improves the statistical outcome, especially for biomarkers unique to one group. A novel quality assurance plus procedure has also been proposed that builds on previously published methods and has the ability to improve statistical results for QC pool. For this dataset, the best option to work with QC surrogate was to lter data based only on
group presence. Finally, a novel use of recursive analysis is portrayed that allows the improvement of statistical analyses with respect to the ratio between true and false positives.
Keywords Quality control samples Quality assurance
procedure False positives Recursive analysis In silico
QC surrogate
1 Introduction
Quality control (QC) is a hot topic in metabolomics and many research articles have been published that both exemplify...