  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
in cases 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 point
estimates have sixfold lower mean squared 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 estimate the concentrations of 26 metabolites and compare to
manual quantification by five expert spectroscopists. We discuss the reason for
discrepancies and the robustness of our methods concentration estimates.
