338 – Bayesian Modeling in Biomedical Applications
A Semi-parametric Bayesian Framework for Identifying up and down Regulated Genes in Subjects with Neurocysticercosis (NCC) Associated Epilepsy
Michael P. Anderson, PhD
The University of Oklahoma
Cheuk H. Leung, MS
OUHSC
Suzanne R. Dubnicka, PhD
Kansas State University
Douglas A. Drevets, MD
OUHSC
Vedantam Rajshekhar, MD
Christian Medical College
Anna Oommen, PhD
Prabhakaran Vasudevan, PhD
Josephin Justin Babu, RN
Ramajayam Govindan, PhD
Hélène Carabin
Neurocysticercosis (NCC) is a neurological inflammatory process caused by Taenia solium larvae cysts in the brain and is one of the most common causes of seizures in developing countries. Subjects reporting to the Department of Neurological Sciences at the Christian Medical College in Vellore, India were recruited for participation in a study to determine inflammation relevant gene expression profiles specific to NCC in peripheral blood monocytes. While technology to measure and describe gene regulation has become quite sophisticated, statistical methods for analyzing such data have remained somewhat pedestrian. Traditional micro-array analysis often relies on unwarranted assumptions of normality and a battery of several thousand t-tests to identify significantly up or down regulated genes between groups. In this work we propose a semi-parametric Bayesian framework as a robust, probability based alternative to the t-test approach. Combined posterior change measures based on suitable priors and interpreted as the probability of correct group affiliation, are used to identify deferentially expressed genes. We compare the top genes identified by both methods in the NCC data set.