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A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data

by 1,2,3,*, 1,3, 4,5, 2,4, 2,4,5 and 1,2,3,*
Metabolomics Platform, Campus Sescelades, Edifici N2, Rovira i Virgili University, Tarragona 43007, Spain
Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Passeig Bonanova 69, Barcelona 08017, Spain
Institut d’Investigació Biomédica Pere Virgili (IISPV), C/Sant Llorenç, 21, Reus 43201, Spain
Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain
Department of Biochemistry and Molecular Biology, University of Barcelona, Barcelona 08028, Spain
Authors to whom correspondence should be addressed.
Metabolites 2012, 2(4), 775-795;
Received: 2 August 2012 / Revised: 2 October 2012 / Accepted: 10 October 2012 / Published: 18 October 2012
(This article belongs to the Special Issue Analytical Techniques in Metabolomics)
Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples. View Full-Text
Keywords: univariate; metabolomics; mass spectrometry univariate; metabolomics; mass spectrometry
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MDPI and ACS Style

Vinaixa, M.; Samino, S.; Saez, I.; Duran, J.; Guinovart, J.J.; Yanes, O. A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data. Metabolites 2012, 2, 775-795.

AMA Style

Vinaixa M, Samino S, Saez I, Duran J, Guinovart JJ, Yanes O. A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data. Metabolites. 2012; 2(4):775-795.

Chicago/Turabian Style

Vinaixa, Maria, Sara Samino, Isabel Saez, Jordi Duran, Joan J. Guinovart, and Oscar Yanes. 2012. "A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data" Metabolites 2, no. 4: 775-795.

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