title: Preprocessing and Pretreatment of Metabolomics Data for Statistical Analysis.
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.
authors.affiliation: 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. i.karaman@imperial.ac.uk.
authors.forename: Ibrahim
authors.initials: I
authors.lastname: Karaman
mesh_terms: D003627:Data Interpretation, Statistical; D001330:Electronic Data Processing; D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D013058:Mass Spectrometry; D055432:Metabolomics / Q000379:methods*; D011786:Quality Control
keywords: Alignment; Normalization; Preprocessing; Pretreatment; Scaling; Transformation