Multivariate analysis of proteomic data: Functional group analysis using a global tes

Proteomics is a relatively new discipline being implemented in life science fields. Proteomics allows a whole-systems approach to discerning changes in organismal physiology due to physical perturbations. The advantages of a proteomic approach may be counteracted by the ability to analyze the data in a meaningful way due to inherent problems with statistical assumptions. Furthermore, analyzing significant protein volume differences among treatment groups often requires analysis of numerous proteins even when limiting analyses to a particular protein type or physiological pathway. Improper use of traditional techniques leads to problems with multiple hypotheses testing.;This research will examine two common techniques used to analyze proteomic data and will apply these to a novel proteomic data set. In addition, a Global Test originally developed for gene array data will be employed to discover its utility for proteomic data and the ability to counteract the multiple hypotheses testing problems encountered with traditional analyses.