Strategies for data handling and statistical analysis in metabolomics studies

Marianne Defernez, Gwénaëlle Le Gall

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)493-555
Number of pages63
JournalAdvances in Botanical Research
Volume67
Early online date17 Jul 2013
DOIs
Publication statusPublished - 30 Jul 2013
Externally publishedYes

Keywords

  • Alignment
  • Artefacts
  • Bias
  • Deconvolution
  • Metabolomics
  • Multivariate
  • Overfitting
  • Preprocessing
  • Univariate
  • Workflow

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