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Recursive Segment-Wise Peak Alignment of Biological 1H NMR Spectra for Improved Metabolic Biomarker Recovery

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Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anesthetics (SORA), Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, SW7 2AZ, London, U.K., and Department of Physics, Sevastopol National Technical University, Streletskaya Bay, Crimea, Ukraine
* To whom correspondence should be addressed. E-mail: [email protected]
†Imperial College London.
‡Sevastopol National Technical University.
Cite this: Anal. Chem. 2009, 81, 1, 56–66
Publication Date (Web):December 2, 2008
https://doi.org/10.1021/ac8011544
Copyright © 2008 American Chemical Society

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    Abstract

    Chemical shift variation in small-molecule 1H NMR signals of biofluids complicates biomarker information recovery in metabonomic studies when using multivariate statistical and pattern recognition tools. Current peak realignment methods are generally time-consuming or align major peaks at the expense of minor peak shift accuracy. We present a novel recursive segment-wise peak alignment (RSPA) method to reduce variability in peak positions across the multiple 1H NMR spectra used in metabonomic studies. The method refines a segmentation of reference and test spectra in a top-down fashion, sequentially subdividing the initial larger segments, as required, to improve the local spectral alignment. We also describe a general procedure that allows robust comparison of realignment quality of various available methods for a range of peak intensities. The RSPA method is illustrated with respect to 140 1H NMR rat urine spectra from a caloric restriction study and is compared with several other widely used peak alignment methods. We demonstrate the superior performance of the RSPA alignment over a wide range of peaks and its capacity to enhance interpretability and robustness of multivariate statistical tools. The approach is widely applicable for NMR-based metabolic studies and is potentially suitable for many other types of data sets such as chromatographic profiles and MS data.

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