Bioinformatics Advance Access published online on June 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth357
Bioinformatics © Oxford University Press 2004; all rights reserved
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1 Dept. of Health, Research & Policy, Stanford Univ, CA 94305; Dept. of Statistics, Stanford Univ, CA 94305
* To whom correspondence should be addressed. E-mail: tibs{at}stanford.edu.
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 (MALDI) 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 labeled peaks. It is also much more interpretable biologically. The peak probability contrast (PPC) method is a potentially useful tool for sample classification from protein mass spectrometry data.
Revised May 13, 2004
Accepted May 26, 2004
Article
Sample classification from protein mass spectrometry, by "peak probability contrasts"
2 Dept. of Statistics, Sequoia Hall, Stanford Univ., CA 94305; Dept. of Health, Research & Policy, Sequoia Hall, Stanford Univ., CA 94305
3 Dept. of Statistics, Sequoia Hall, Stanford Univ., CA 94305; Dept. of Health, Research & Policy, Sequoia Hall, Stanford Univ., CA 94305
4 Dept. of Radiation Oncology, Stanford Univ., 94305
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