Author, as appears in the article.: Yanes, O. Guinovart, J.J. Duran, J. Saez, I. Samino, S. Vinaixa, M.
Department: Enginyeria Electrònica, Elèctrica i Automàtica
e-ISSN: 2218-1989
Abstract: 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.
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Last page: 795
Journal volume: 2
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: http://www.mdpi.com/2218-1989/2/4/775
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.3390/metabo2040775
Entity: Universitat Rovira i Virgili.
Journal publication year: 2012
First page: 775
Repositori URV