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This paper addresses the possibility of mathematically partition and process urine 1 H-NMR spectra to enhance the efficiency of the subsequent multivariate data analysis in the context of metabolic profiling of a toxicity study. We show that by processing the NMR data with the peak alignment using reduced set mapping (PARS) algorithm and the use of sparse representation of the data results in the information contained in the original NMR data being preserved with retained resolution but free of the problem of peak shifts. We can now describe a method for differential expression analysis of NMR spectra by using prior knowledge, i.e ., the onset of dosing, a partitioning not possible to achieve using raw or bucketed data. In addition we also outline a scheme for soft removal of "biological noise" from the aligned data: exhaustive bio-noise subtraction (EBS). The result is a straightforward protocol for detection of peaks that appear as a consequence of the drug response. In other words, it is possible to elucidate peak origin, either from endogenous substances or from the administered drug/biomarkers. The partition of data originating from the normally regulating metabolome can, furthermore, be analyzed free of the superimposed biological noise. The proposed protocol results in enhanced interpretability of the processed data, i.e ., a more refined metabolic trace, simplification of detection of consistent biomarkers, and a simplified search for metabolic end products of the administered drug.

metabolomics

New modes of data partitioning based on PARS peak alignment for improved multivariate biomarker/biopattern detection in 1H-NMR spectroscopic metabolic profiling of urine