title: Sample classification from protein mass spectrometry, by 'peak probability contrasts'.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Health, Research and Policy, Stanford University, CA 94305, USA. tibs@stanford.edu
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
authors.forename: Trevor
authors.initials: T
authors.lastname: Hastie
authors.forename: Balasubramanian
authors.initials: B
authors.lastname: Narasimhan
authors.forename: Scott
authors.initials: S
authors.lastname: Soltys
authors.forename: Gongyi
authors.initials: G
authors.lastname: Shi
authors.forename: Albert
authors.initials: A
authors.lastname: Koong
authors.forename: Quynh-Thu
authors.initials: QT
authors.lastname: Le
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000097:blood* / Q000145:classification; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000097:blood* / Q000145:classification; D010051:Ovarian Neoplasms / Q000097:blood / Q000145:classification* / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins