Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logari

Background: Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) 1H, projections of 2D 1H, 1H J-resolved (pJRES), and intact 2D J-resolved (JRES). Results: Here, the effects of the glog transform are compared against two commonly used variance stabilising techniques, autoscalin...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Helen M. Parsons, Christian Ludwig, Ulrich L. Günther, Mark R. Viant
• Keywords: BMCBI 2007, Metabolomics, Technical Variance, Variance