Statistics – Methodology
Scientific paper
2011-05-11
Statistics
Methodology
Scientific paper
Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain profiles of metabolites dissolved in biofluids such as cell supernatants. Methods for estimating metabolite concentrations from these spectra are presently confined to manual peak fitting and to binning procedures for integrating resonance peaks. Extensive information on the patterns of spectral resonance generated by human metabolites is now available in online databases. By incorporating this information into a Bayesian model we can deconvolve resonance peaks from a spectrum and obtain explicit concentration estimates for the corresponding metabolites. Spectral resonances that cannot be deconvolved in this way may also be of scientific interest so we model them jointly using wavelets. We describe a Markov chain Monte Carlo algorithm which allows us to sample from the joint posterior distribution of the model parameters, using specifically designed block updates to improve mixing. The strong prior on resonance patterns allows the algorithm to identify peaks corresponding to particular metabolites automatically, eliminating the need for manual peak assignment. We assess our method for peak alignment and concentration estimation. Except sometimes when the target resonance signal is very weak, alignment is unbiased and precise. We compare the Bayesian concentration estimates to those obtained from a conventional numerical integration method and find that our estimates have 11 fold reduced mean estimation error. Finally, we apply our method to a spectral dataset taken from an investigation of the metabolic response of yeast to recombinant protein expression. We compare our results to the manual deconvolutions of an expert spectroscopist and find concurrence except in the case of one compound. We discuss the reason for such discrepancies and the robustness of our methods concentration estimates.
Astle William
Ebbels Timothy
Iorio Maria de
Richardson Sylvia
Stephens David
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