Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation |
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| Authors: | Helen M Parsons Christian Ludwig Ulrich L Günther Mark R Viant |
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| Institution: | (1) Centre for Systems Biology, The University of Birmingham, Birmingham, B15 2TT Edgbaston, UK;(2) The Henry Wellcome Building for Biomolecular NMR Spectroscopy, The University of Birmingham, Birmingham, B15 2TT Edgbaston, UK;(3) School of Biosciences, The University of Birmingham, Birmingham, B15 2TT Edgbaston, UK |
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| Abstract: | 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). |
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