Bayesian Inference of Gene Expression Dynamics in Alzheimer Brains

Alzheimer's disease (AD) is a serious neurodegenerative disease consisting of four stages where the illness gets progressively worse. It is of great significance to detect the gene regulatory mechanism as AD progresses and, thus, to help us better understand the causes of AD and find ways to treat or control AD. There are numerous researches to conduct this kind of study. However, the majority of methods are processing region by region of brain, stage by stage of AD, and then compare the results to detect changes. It is unclear how to combine these three dimensions, i.e., gene, region and stage, simultaneously to study gene expression dynamics of AD. This is the motivation of our research. In our study, we propose a statistical model of increments to clarify the relationship between gene expression in adjacent stages, so that we could better estimate the missing data we want and obtain a complete reasonable dynamic regulatory network model. Simulations are conducted to validate the statistical power of our algorithm. Moreover, a real data analysis shows that our method can capture the dynamic gene regulatory relationships among this complex brain data.