Format

Send to

Choose Destination
Metabolites. 2012 Oct 18;2(4):775-95. doi: 10.3390/metabo2040775.

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

Author information

1
Metabolomics Platform, Campus Sescelades, Edifici N2, Rovira i Virgili University, Tarragona 43007, Spain. maria.vinaixa@urv.cat.
2
Metabolomics Platform, Campus Sescelades, Edifici N2, Rovira i Virgili University, Tarragona 43007, Spain. sara.samino@urv.cat.
3
Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain. isabel.saez@irbbarcelona.org.
4
Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Passeig Bonanova 69, Barcelona 08017, Spain. jordi.duran@irbbarcelona.org.
5
Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Passeig Bonanova 69, Barcelona 08017, Spain. guinovart@irbbarcelona.org.
6
Metabolomics Platform, Campus Sescelades, Edifici N2, Rovira i Virgili University, Tarragona 43007, Spain. oscar.yanes@urv.cat.

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.

Supplemental Content

Full text links

Icon for Multidisciplinary Digital Publishing Institute (MDPI) Icon for PubMed Central
Loading ...
Support Center