491 – SPEED: Advances in Statistical Genetics
Hybrid-Network: A Bayesian Approach
Demba Fofana
University of Memphis
Dale Bowman
University of Memphis
Ebenezer O. George
University of Memphis
Analyzing gene expression data rigorously requires taking assumptions into consideration but also relies on using information about network relations that exist among genes. Combining these different elements cannot only improve statistical power, but also provide a better framework through which gene expression can be properly analyzed. We propose a novel statistical model that combines assumptions and gene network information into the analysis. Assumptions are important since every test statistic is valid only when required assumptions hold. We incorporate gene network information into the analysis because neighboring genes share biological functions. This correlation factor is taken into account via similar prior probabilities for neighboring genes. With a series of simulations our approach is compared with other approaches. Our method that combines assumptions and network information into the analysis is shown to be more powerful.