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Metabolomics: From Fundamentals to Clinical Applications

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Attention for Chapter 6: Preprocessing and Pretreatment of Metabolomics Data for Statistical Analysis
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Chapter title
Preprocessing and Pretreatment of Metabolomics Data for Statistical Analysis
Chapter number 6
Book title
Metabolomics: From Fundamentals to Clinical Applications
Published in
Advances in experimental medicine and biology, January 2017
DOI 10.1007/978-3-319-47656-8_6
Pubmed ID
Book ISBNs
978-3-31-947655-1, 978-3-31-947656-8
Authors

Ibrahim Karaman

Editors

Alessandra Sussulini

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.

Mendeley readers

The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 81 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 19%
Student > Master 13 16%
Researcher 12 15%
Student > Bachelor 7 9%
Student > Doctoral Student 6 7%
Other 6 7%
Unknown 22 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 20%
Chemistry 15 19%
Agricultural and Biological Sciences 9 11%
Pharmacology, Toxicology and Pharmaceutical Science 5 6%
Medicine and Dentistry 2 2%
Other 8 10%
Unknown 26 32%