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Mass spectrometry based clinical proteomics has emerged as a powerful toolfor highthroughput protein profiling and biomarker discovery. Recentimprovements in mass spectrometry technology have boosted the potential ofproteomic studies in biomedical research. However, the complexity of theproteomic expression introduces new statistical challenges in summarizing andanalyzing the acquired data. Statistical methods for optimally processingproteomic data are currently a growing field of research. In this paper wepresent simple, yet appropriate methods to preprocess, summarize and analyzehigh-throughput MALDI-FTICR mass spectrometry data, collected in a case-controlfashion, while dealing with the statistical challenges that accompany suchdata. The known statistical properties of the isotopic distribution of thepeptide molecules are used to preprocess the spectra and translate theproteomic expression into a condensed data set. Information on either theintensity level or the shape of the identified isotopic clusters is used toderive summary measures on which diagnostic rules for disease status allocationwill be based. Results indicate that both the shape of the identified isotopicclusters and the overall intensity level carry information on the class outcomeand can be used to predict the presence or absence of the disease.

Statistical development and assessment of summary measures to account  for isotopic clustering of Fourier transform mass spectrometry data in  clinical diagnostic studies