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Bioinformatics Advance Access originally published online on November 18, 2008
Bioinformatics 2009 25(2):258-264; doi:10.1093/bioinformatics/btn599
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Bag of Peaks: interpretation of NMR spectrometry

Gavin Brelstaff 1,*, Manuele Bicego 2,{dagger}, Nicola Culeddu 3 and Matilde Chessa 4,{ddagger}

1Biocomputing, CRS4, 09100 Pula (CA), Sardinia, 2DEIR, University of Sassari, via Torre Tonda 34, 07100 Sassari, 3ICB-CNR, 07040 Li Punti, Sassari and 4Porto Conte Ricerche, Loc. Tramariglio, Alghero, Italy

*To whom correspondence should be addressed.


  Abstract

Motivation: The analysis of high-resolution proton nuclear magnetic resonance (NMR) spectrometry can assist human experts to implicate metabolites expressed by diseased biofluids. Here, we explore an intermediate representation, between spectral trace and classifier, able to furnish a communicative interface between expert and machine. This representation permits equivalent, or better, classification accuracies than either principal component analysis (PCA) or multi-dimensional scaling (MDS). In the training phase, the peaks in each trace are detected and clustered in order to compile a common dictionary, which could be visualized and adjusted by an expert. The dictionary is used to characterize each trace with a fixed-length feature vector, termed Bag of Peaks, ready to be classified with classical supervised methods.

Results: Our small-scale study, concerning Type I diabetes in Sardinian children, provides a preliminary indication of the effectiveness of the Bag of Peaks approach over standard PCA and MDS. Consistently, higher classification accuracies are obtained once a sufficient number of peaks (>10) are included in the dictionary. A large-scale simulation of noisy spectra further confirms this advantage. Finally, suggestions for metabolite-peak loci that may be implicated in the disease are obtained by applying standard feature selection techniques.

Availability: Matlab code to compute the Bag of Peaks representation may be found at http://economia.uniss.it/docenti/bicego/BagOfPeaks/BagOfPeaks.zip

Contact: gjb{at}crs4.it

{dagger}Present address: Dip. di Informatica, University of Verona, Strada Le Grazie, 15 - 37134 Verona, Italy.

{ddagger}Present address: Vincenzo Migaleddu - Imaging s.rl. Viale Caprera n. 3/A, 07100 Sassari, Italy.

Associate Editor: Jonathan Wren

Received on July 9, 2008; revised on November 12, 2008; accepted on November 16, 2008

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