A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data

Maria Vinaixa, Sara Samino, Isabel Saez, Jordi Duran, Joan J. Guinovart, Oscar Yanes

Research output: Indexed journal article Reviewpeer-review

202 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)775-795
Number of pages21
Issue number4
Publication statusPublished - 18 Oct 2012
Externally publishedYes


  • Mass spectrometry
  • Metabolomics
  • Univariate


Dive into the research topics of 'A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data'. Together they form a unique fingerprint.

Cite this