Scaling and Normalization Effects in NMR Spectroscopic Metabonomic Data Sets

• Published in: Analytical chemistry (Washington) 2006-04, Vol.78 (7), p.2262-2267
• Main Authors: Craig, Andrew, Cloarec, Olivier, Holmes, Elaine, Nicholson, Jeremy K, Lindon, John C
• Format: Article
• Language: English
• Subjects: Algorithms; Analytical chemistry; Biomarkers - analysis; Biomarkers - metabolism; Body Fluids - metabolism; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography, High Pressure Liquid - methods; Data Collection; Data processing; Databases, Factual; Exact sciences and technology; Magnetic Resonance Spectroscopy - methods; NMR; Nuclear magnetic resonance; Other chromatographic methods; Proteomics - methods; Spectrometric and optical methods; Spectrum analysis; Urinalysis - methods; Urine
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac0519312

Considerable confusion appears to exist in the metabonomics literature as to the real need for, and the role of, preprocessing the acquired spectroscopic data. A number of studies have presented various data manipulation approaches, some suggesting an optimum method. In metabonomics, data are usually presented as a table where each row relates to a given sample or analytical experiment and each column corresponds to a single measurement in that experiment, typically individual spectral peak intensities or metabolite concentrations. Here we suggest definitions for and discuss the operations usually termed normalization (a table row operation) and scaling (a table column operation) and demonstrate their need in 1H NMR spectroscopic data sets derived from urine. The problems associated with “binned” data (i.e., values integrated over discrete spectral regions) are also discussed, and the particular biological context problems of analytical data on urine are highlighted. It is shown that care must be exercised in calculation of correlation coefficients for data sets where normalization to a constant sum is used. Analogous considerations will be needed for other biofluids, other analytical approaches (e.g., HPLC−MS), and indeed for other “omics” techniques (i.e., transcriptomics or proteomics) and for integrated studies with “fused” data sets. It is concluded that data preprocessing is context dependent and there can be no single method for general use.