Xu, Yun 
ORCID: 0000-0003-3228-5111 and Goodacre, Royston ORCID: 0000-0003-2230-645X
(2025)
Mind your Ps and Qs – Caveats in metabolomics data analysis.
TrAC Trends in Analytical Chemistry, 183.
p. 118064.
ISSN 0165-9936, 1879-3142
Abstract
Metabolomics studies use high-throughput analytical platforms to measure metabolites in biological samples. These mass spectrometry and/or NMR spectroscopy platforms generate complex data sets, and the analysis of such data poses many challenges, in particular the high dimensionality with relatively fewer number of samples means that sophisticated statistical models are required to analyse these data and these models come with caveats. In this review, we discuss some of these common caveats associated with most popular statistical tests and models. We present common mistakes found in metabolomics data analysis, along with recommendations on how to avoid them. The aim of this review is to raise awareness of the potential risks of misusing or abusing statistical models, and to promote good practices for reliable and reproducible metabolomics research. A new form of permutation test with emphasis on assessing the statistical significance level of the effect captured by supervised model is also proposed.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 3401 Analytical Chemistry, 34 Chemical Sciences, 2.1 Biological and endogenous factors, Generic health relevance |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology Faculty of Health and Life Sciences > Tech, Infrastructure and Environmental Directorate |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 13 Dec 2024 15:53 |
| Last Modified: | 13 Dec 2024 15:53 |
| DOI: | 10.1016/j.trac.2024.118064 |
| Open Access URL: | https://doi.org/10.1016/j.trac.2024.118064 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3189172 |
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