Autor según el artículo: Yanes, O. Guinovart, J.J. Duran, J. Saez, I. Samino, S. Vinaixa, M.
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Departamento: Enginyeria Electrònica, Elèctrica i Automàtica
e-ISSN: 2218-1989
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Resumen: 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.
Entidad: Universitat Rovira i Virgili.
Año de publicación de la revista: 2012
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Página inicial: 775
Página final: 795
Volumen de revista: 2
Repositori URV