Probabilistic Disease Classi cation of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum

We have developed an algorithm called Q5 for probabilistic classiŽ cation of healthy versus disease whole serum samples using mass spectrometry. The algorithm employs principal components analysis (PCA) followed by linear discriminant analysis (LDA) on whole spectrum surface-enhanced laser desorption/ionization time of  ight (SELDI-TOF) mass spectrometry (MS) data and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classiŽ cation of complete mass spectra of a complex protein mixture. Q5 employs a probabilistic classiŽ cation algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efŽ cient; it is noniterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and veriŽ ed for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classiŽ cation method achieves excellent performance. We achieve sensitivity, speciŽ city, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classiŽ cation techniques and can provide clues as to the molecular identities of differentially expressed proteins and peptides.