Identifying differentially regulated genes

Microarray experiments often measure expressions of genes taken from sample tissues in the presence of external perturbations such as medication, radiation, or disease. Typically in such experiments, gene expressions are measured before and after the application of external perturbation. In this paper, we focus on an important class of such microarray experiments that inherently have two groups of tissue samples. The external perturbation can change the expressions of some genes directly or indirectly through gene interaction network. When such different groups exist, the expressions of genes after the perturbation can be different between the two groups. It is not only important to identify the genes that respond differently across the two groups, but also to mine the reason behind this differential response. In this paper, we aim to identify the cause of this differential behavior of genes, whether because of the perturbation or due to interactions with other genes in two group perturbation experiments. We propose a new probabilistic Bayesian method with Markov Random Field to find such genes. Our method incorporates information about relationship from gene networks as prior information. Experimental results on synthetic and real datasets demonstrate the superiority of our method compared to existing techniques.