Bioinformatics Advance Access originally published online on June 29, 2004
Bioinformatics 2004 20(17):3034-3044; doi:10.1093/bioinformatics/bth357
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Bioinformatics vol. 20 issue 17 © Oxford University Press 2004; all rights reserved.
Sample classification from protein mass spectrometry, by ‘peak probability contrasts’
1 Department of Health, Research and Policy, 2 Department of Statistics and 3 Department of Radiation Oncology, Stanford University, CA 94305, USA
Received on April 19, 2004; revised on May 13, 2004; accepted on May 26, 2004
Advance Access Publication June 29, 2004
Motivation: Early cancer detection has always been a major research focus in solid tumor oncology. Early tumor detection can theoretically result in lower stage tumors, more treatable diseases and ultimately higher cure rates with less treatment-related morbidities. Protein mass spectrometry is a potentially powerful tool for early cancer detection.
We propose a novel method for sample classification from protein mass spectrometry data. When applied to spectra from both diseased and healthy patients, the ‘peak probability contrast’ technique provides a list of all common peaks among the spectra, their statistical significance and their relative importance in discriminating between the two groups. We illustrate the method on matrix-assisted laser desorption and ionization mass spectrometry data from a study of ovarian cancers.
Results: Compared to other statistical approaches for class prediction, the peak probability contrast method performs as well or better than several methods that require the full spectra, rather than just labelled peaks. It is also much more interpretable biologically. The peak probability contrast method is a potentially useful tool for sample classification from protein mass spectrometry data.
Supplementary Information: http://www.stat.stanford.edu/~tibs/ppc
Contact: tibs{at}stanford.edu
* To whom correspondence should be addressed.
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