title: Extracting meaningful information from metabonomic data using multivariate statistics.
abstract: Metabonomics aims to identify and quantify all small-molecule metabolites in biologically relevant samples using high-throughput techniques such as NMR and chromatography/mass spectrometry. This generates high-dimensional data sets with properties that require specialized approaches to data analysis. This chapter describes multivariate statistics and analysis tools to extract meaningful information from metabonomic data sets. The focus is on the use and interpretation of latent variable methods such as principal component analysis (PCA), partial least squares/projections to latent structures (PLS), and orthogonal PLS (OPLS). Descriptions of the key steps of the multivariate data analyses are provided with demonstrations from example data.
authors: Fios Genomics Ltd, Nine Edinburgh Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, UK, max.bylesjo@fiosgenomics.com.
authors: Max
authors: M
authors: Bylesjö
mesh_terms: D000818:Animals; D016002:Discriminant Analysis; D016018:Least-Squares Analysis; D055432:Metabolomics / Q000379:methods*; D051379:Mice; D015999:Multivariate Analysis; D009928:Organ Specificity; D025341:Principal Component Analysis; D013223:Statistics as Topic