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The success of metabolomics studies depends upon the "fitness" of eachbiological sample used for analysis: it is critical that metabolite levelsreported for a biological sample represent an accurate snapshot of the studiedorganism's metabolite profile at time of sample collection. Numerous factorsmay compromise metabolite sample fitness, including chemical and biologicalfactors which intervene during sample collection, handling, storage, andpreparation for analysis. We propose a probabilistic model for the quantitativeassessment of metabolite sample fitness. Collection and processing of nuclearmagnetic resonance (NMR) and ultra-performance liquid chromatography (UPLC-MS)metabolomics data is discussed. Feature selection methods utilized formultivariate data analysis are briefly reviewed, including feature clusteringand computation of latent vectors using spectral methods. We propose that thetime-course of metabolite changes in samples stored at different temperaturesmay be utilized to identify changing-metabolite-to-stable-metabolite ratios asmarkers of sample fitness. Tolerance intervals may be computed to characterizethese ratios among fresh samples. In order to discover additional structure inthe data relevant to sample fitness, we propose using data labeled according tothese ratios to train a Dirichlet process mixture model (DPMM) for assessingsample fitness. DPMMs are highly intuitive since they model the metabolitelevels in a sample as arising from a combination of processes including, e.g.,normal biological processes and degradation- or contamination-inducingprocesses. The outputs of a DPMM are probabilities that a sample is associatedwith a given process, and these probabilities may be incorporated into a finalclassifier for sample fitness.

Probabilistic Approach for Evaluating Metabolite Sample Integrity