Detecting Circadian Gene Expressions via Bayesian Analysis: an Application to the Arabidopsis Thaliana Dataset

Abstract

In genomic applications, there is often interest in identifying genes whose time-course expression trajectories exhibit periodic oscillations with a period of approximately 24 hours (circadian genes). While it is natural to expect that the expression of gene $i$ at time $j$ might depend to some degree on the expression of the other genes measured at the same time, widely-used rhythmicity detection techniques do not accommodate for the potential dependence across genes. We develop a Bayesian approach for periodicity identification that explicitly takes into account the complex dependence structure across time-course trajectories in gene expressions. The methodology is applied to a plant gene expression dataset.

Silvia Montagna

Assistant Professor in Statistics