, Volume 2, Issue 4, pp 171–196

Statistical strategies for avoiding false discoveries in metabolomics and related experiments


DOI: 10.1007/s11306-006-0037-z

Cite this article as:
Broadhurst, D.I. & Kell, D.B. Metabolomics (2006) 2: 171. doi:10.1007/s11306-006-0037-z

Many metabolomics, and other high-content or high-throughput, experiments are set up such that the primary aim is the discovery of biomarker metabolites that can discriminate, with a certain level of certainty, between nominally matched ‘case’ and ‘control’ samples. However, it is unfortunately very easy to find markers that are apparently persuasive but that are in fact entirely spurious, and there are well-known examples in the proteomics literature. The main types of danger are not entirely independent of each other, but include bias, inadequate sample size (especially relative to the number of metabolite variables and to the required statistical power to prove that a biomarker is discriminant), excessive false discovery rate due to multiple hypothesis testing, inappropriate choice of particular numerical methods, and overfitting (generally caused by the failure to perform adequate validation and cross-validation). Many studies fail to take these into account, and thereby fail to discover anything of true significance (despite their claims). We summarise these problems, and provide pointers to a substantial existing literature that should assist in the improved design and evaluation of metabolomics experiments, thereby allowing robust scientific conclusions to be drawn from the available data. We provide a list of some of the simpler checks that might improve one’s confidence that a candidate biomarker is not simply a statistical artefact, and suggest a series of preferred tests and visualisation tools that can assist readers and authors in assessing papers. These tools can be applied to individual metabolites by using multiple univariate tests performed in parallel across all metabolite peaks. They may also be applied to the validation of multivariate models. We stress in particular that classical p-values such as “p < 0.05”, that are often used in biomedicine, are far too optimistic when multiple tests are done simultaneously (as in metabolomics). Ultimately it is desirable that all data and metadata are available electronically, as this allows the entire community to assess conclusions drawn from them. These analyses apply to all high-dimensional ‘omics’ datasets.


statistics machine learning false discovery receiver–operator characteristic hypothesis testing statistical power Bonferroni correction bias overfitting cross validiation credit assignment visualisation 

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.School of ChemistryThe University of ManchesterManchesterUK
  2. 2.Manchester Centre for Integrative Systems Biology, The Manchester Interdisciplinary BiocentreThe University of ManchesterManchesterUK