Autor segons l'article: Yanes, O. Guinovart, J.J. Duran, J. Saez, I. Samino, S. Vinaixa, M.
Departament: Enginyeria Electrònica, Elèctrica i Automàtica
e-ISSN: 2218-1989
Resum: 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.
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Pàgina final: 795
Volum de revista: 2
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: http://www.mdpi.com/2218-1989/2/4/775
DOI de l'article: 10.3390/metabo2040775
Entitat: Universitat Rovira i Virgili.
Any de publicació de la revista: 2012
Pàgina inicial: 775
Repositori URV