Uncertainty budgeting in fold change determination and implications for non-targeted metabolomics studies in model systems.

The p-value is the most prominent established metric for statistical significance in non-targeted metabolomics. However, its adequacy has repeatedly been the subject of discussion criticizing its uncertainty and its dependence on sample size and statistical power. These issues compromise non-targeted metabolomics in model systems, where studies typically investigate 5-10 samples per group. In this paper we propose a different approach for assessing the relevance of fold change (FC) data, where the FC is treated as a quantitative value and is validated by uncertainty budgeting. For the purpose of large-scale application in non-targeted metabolomics, we present a simplified approach for uncertainty propagation using experimental standard deviations of metabolite intensities as type A-summarized standard uncertainties. The resulting expanded FC uncertainty can be used to derive a minimum relevant FC as a complementary criterion in metabolomics data evaluation. This concept overcomes the need for a uniform p-value cut-off for all metabolites by considering the experimental uncertainty for each metabolite individually. The proposed procedure is part of analytical method validation, however the concept has not previously been applied to non-targeted metabolomics. A case study on mesenchymal stem cells cultured in normoxia and hypoxia demonstrates the practical value of this approach, in particular for studies with a small sample size. An online two-dimensional LC method coupled to mass spectrometry was crucial in providing both broad metabolome coverage and excellent experimental precision (<8% CV for peak areas, on average 0.5% CV for retention times) that was required for sensitive differential analysis as low as FC 1.1.