  The success of metabolomics studies depends upon the "fitness" of each
biological sample used for analysis: it is critical that metabolite levels
reported for a biological sample represent an accurate snapshot of the studied
organism's metabolite profile at time of sample collection. Numerous factors
may compromise metabolite sample fitness, including chemical and biological
factors which intervene during sample collection, handling, storage, and
preparation for analysis. We propose a probabilistic model for the quantitative
assessment of metabolite sample fitness. Collection and processing of nuclear
magnetic resonance (NMR) and ultra-performance liquid chromatography (UPLC-MS)
metabolomics data is discussed. Feature selection methods utilized for
multivariate data analysis are briefly reviewed, including feature clustering
and computation of latent vectors using spectral methods. We propose that the
time-course of metabolite changes in samples stored at different temperatures
may be utilized to identify changing-metabolite-to-stable-metabolite ratios as
markers of sample fitness. Tolerance intervals may be computed to characterize
these ratios among fresh samples. In order to discover additional structure in
the data relevant to sample fitness, we propose using data labeled according to
these ratios to train a Dirichlet process mixture model (DPMM) for assessing
sample fitness. DPMMs are highly intuitive since they model the metabolite
levels in a sample as arising from a combination of processes including, e.g.,
normal biological processes and degradation- or contamination-inducing
processes. The outputs of a DPMM are probabilities that a sample is associated
with a given process, and these probabilities may be incorporated into a final
classifier for sample fitness.
