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Title: | The impact of the method of extracting metabolic signal from H-1-NMR data on the classification of samples: A case study of binning and BATMAN in lung cancer | Authors: | PADAYACHEE, Trishanta KHAMIAKOVA, Tatsiana LOUIS, Evelyne ADRIAENSENS, Peter BURZYKOWSKI, Tomasz |
Issue Date: | 2019 | Publisher: | PUBLIC LIBRARY SCIENCE | Source: | PLOS ONE, 14(2) (Art N° e211854) | Abstract: | Nuclear magnetic resonance (NMR) spectroscopy is a principal analytical technique in metabolomics. Extracting metabolic information from NMR spectra is complex due to the fact that an immense amount of detail on the chemical composition of a biological sample is expressed through a single spectrum. The simplest approach to quantify the signal is through spectral binning which involves subdividing the spectra into regions along the chemical shift axis and integrating the peaks within each region. However, due to overlapping resonance signals, the integration values do not always correspond to the concentrations of specific metabolites. An alternate, more advanced statistical approach is spectral deconvolution. BATMAN (Bayesian AuTomated Metabolite Analyser for NMR data) performs spectral deconvolution using prior information on the spectral signatures of metabolites. In this way, BATMAN estimates relative metabolic concentrations. In this study, both spectral binning and spectral deconvolution using BATMAN were applied to 400 MHz and 900 MHz NMR spectra of blood plasma samples from lung cancer patients and control subjects. The relative concentrations estimated by BATMAN were compared with the binning integration values in terms of their ability to discriminate between lung cancer patients and controls. For the 400 MHz data, the spectral binning approach provided greater discriminatory power. However, for the 900 MHz data, the relative metabolic concentrations obtained by using BATMAN provided greater predictive power. While spectral binning is computationally advantageous and less laborious, complementary models developed using BATMAN-estimated features can add complementary information regarding the biological interpretation of the data and therefore are clinically useful. | Notes: | [Padayachee, Trishanta; Khamiakova, Tatsiana; Burzykowski, Tomasz] Hasselt Univ, I BioStat, Diepenbeek, Belgium. [Louis, Evelyne] Hasselt Univ, Fac Med & Life Sci, Diepenbeek, Belgium. [Adriaensens, Peter] Hasselt Univ, Appl & Analyt Chem, Inst Mat Res, Diepenbeek, Belgium. [Khamiakova, Tatsiana] Janssen Pharmaceut, Res & Dev, Beerse, Belgium. | Keywords: | Multidisciplinary Sciences | Document URI: | http://hdl.handle.net/1942/28549 | ISSN: | 1932-6203 | e-ISSN: | 1932-6203 | DOI: | 10.1371/journal.pone.0211854 | ISI #: | 000457874000081 | Rights: | 2019 Padayachee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2020 |
Appears in Collections: | Research publications |
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padayachee 1.pdf | Published version | 2.14 MB | Adobe PDF | View/Open |
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