Professor Luis Nieto of the Instituto Tecnológico Autónomo de México presents "Bayesian Analysis of Functional Proteomics Profiles."
Using a new type of array technology, the reverse phase protein array (RPPA), we measure time-course protein expression for a set of selected markers that are known to co-regulate biological functions in a pathway structure. To accommodate the complex dependent nature of the data, including temporal correlation and pathway dependence for the protein markers, we propose a mixed effects model with temporal and protein-specific components. We develop a sequence of random probability measures (RPM) to account for the dependence in time of the protein expression measurements. We also acknowledge the pathway dependence among proteins via a conditionally autoregressive (CAR) model. Applying our model to the RPPA data, we reveal a pathway-dependent functional profile for the set of proteins as well as marginal expression profiles over time for individual markers.