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Preprocessing and Pretreatment of Metabolomics Data for Statistical Analysis

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Metabolomics: From Fundamentals to Clinical Applications

Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 965))

Abstract

From data acquisition to statistical analysis, metabolomics data need to undergo several processing steps, which are crucial for the data quality and interpretation of the results. In this chapter, methods for preprocessing, normalization, and pretreatment of metabolomics data generated from nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are presented and discussed. Preprocessing is reported for both NMR and MS analysis. The challenges in preprocessing such complex data are highlighted. Subsequently, normalization methods such as total area normalization, probabilistic quotient normalization, and quantile normalization are explained. Finally, several scaling and data transformation methods are discussed for metabolomics data pretreatment, which is an important step prior to statistical analysis.

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Abbreviations

ANOVA:

Analysis of variance

CPMG:

Carr-Purcell-Meiboom-Gill

GC:

Gas chromatography

glog:

Generalized log

LC:

Liquid chromatography

LOESS:

Locally estimated smoothing

m/z :

Mass-to-charge ratio

MS:

Mass spectrometry

NMR:

Nuclear magnetic resonance spectroscopy

PCA:

Principal component analysis

PLSR:

Partial least squares regression

R2 :

Linear regression coefficient

RSD:

Relative standard deviation

RT:

Retention time

QCs:

Quality control samples

TSP:

3-trimethylsilylpropionic acid

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Acknowledgements

The author thanks Rui Pinto for helpful discussions in the preparation of this book chapter.

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Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, St. Mary’s Campus, Norfolk Place, W2 1PG, London, UK

    Ibrahim Karaman

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  1. Ibrahim Karaman
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Correspondence to Ibrahim Karaman .

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Editors and Affiliations

  1. University of Campinas (UNICAMP) Institute of Chemistry Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Campinas, Brazil

    Alessandra Sussulini

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Karaman, I. (2017). Preprocessing and Pretreatment of Metabolomics Data for Statistical Analysis. In: Sussulini, A. (eds) Metabolomics: From Fundamentals to Clinical Applications. Advances in Experimental Medicine and Biology(), vol 965. Springer, Cham. https://doi.org/10.1007/978-3-319-47656-8_6

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