  Mass spectrometry based clinical proteomics has emerged as a powerful tool
for highthroughput protein profiling and biomarker discovery. Recent
improvements in mass spectrometry technology have boosted the potential of
proteomic studies in biomedical research. However, the complexity of the
proteomic expression introduces new statistical challenges in summarizing and
analyzing the acquired data. Statistical methods for optimally processing
proteomic data are currently a growing field of research. In this paper we
present simple, yet appropriate methods to preprocess, summarize and analyze
high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control
fashion, while dealing with the statistical challenges that accompany such
data. The known statistical properties of the isotopic distribution of the
peptide molecules are used to preprocess the spectra and translate the
proteomic expression into a condensed data set. Information on either the
intensity level or the shape of the identified isotopic clusters is used to
derive summary measures on which diagnostic rules for disease status allocation
will be based. Results indicate that both the shape of the identified isotopic
clusters and the overall intensity level carry information on the class outcome
and can be used to predict the presence or absence of the disease.
