Multivariate Analysis in Metabolomics

Title:Multivariate Analysis in Metabolomics

Volume: 1 Issue: 1

Author(s): Bradley Worley and Robert Powers

Affiliation:

Keywords: Multivariate analysis, metabolomics, metabonomics, OPLS-DA, PCA, PLS-DA.

Abstract: Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

Cite this article as:

Worley Bradley and Powers Robert, Multivariate Analysis in Metabolomics, Current Metabolomics 2013; 1 (1) . https://dx.doi.org/10.2174/2213235X11301010092