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Activity Number: 380
Type: Invited
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Asociacion Mexicana de Estadistica
Abstract - #303724
Title: Bayesian Analysis of Functional Proteomics Profiles
Author(s): Luis Enrique Nieto-Barajas*+
Companies: Instituto Tecnológico Autónomo de México
Address: Rio Hondo No. 1, Progreso Tizapan, Mexico, D.F. , International, 01080, Mexico
Keywords: Bayesian nonparametrics ; dependent random measures ; mixed effects models ; conditionally autoregressive processes ; time series analysis

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.

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