Strategies for data handling and statistical analysis in metabolomics studies

Defernez, Marianne and Le Gall, Gwénaëlle ORCID: (2013) Strategies for data handling and statistical analysis in metabolomics studies. Advances in Botanical Research, 67. pp. 493-555. ISSN 0065-2296

Full text not available from this repository.


Metabolomics is classically defined as the holistic detection of metabolites of a system and usually involves the following multistep workflow: sample preparation, profile recording, data processing and pretreatment, data analysis, metabolite identification and data interpretation. In this chapter, we focus on the later part of the workflow: the preprocessing, pretreatment and data analysis. Thus we will present techniques and approaches that are commonly used for the analysis of metabolomics data. More importantly, we show that the data analysis does not sit in isolation but is instead intimately linked to the experimental steps that have taken place upstream of it. We will demonstrate that this interaction can be used in a beneficial way, by exploring how the knowledge of the experimental steps can inform the correct implementation of statistical techniques and conversely how a better understanding of these interactions can help us to improve the experimental aspects.

Item Type: Article
Uncontrolled Keywords: alignment,artefacts,bias,deconvolution,metabolomics,multivariate,overfitting,preprocessing,univariate,workflow,plant science ,/dk/atira/pure/subjectarea/asjc/1100/1110
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 26 Oct 2018 15:30
Last Modified: 11 Aug 2023 00:19
DOI: 10.1016/B978-0-12-397922-3.00011-3

Actions (login required)

View Item View Item