Bayesian binomial regression model with a latent Gaussian field for analysis of epigenetic data

Abstract

Epigenetic observations are represented by the total amount of reads from a particular cell and the amount of methylated reads, making it reasonable to model this data by a binomial distribution. There are numerous factors that can influence probability of success from a particular region. We might also expect spatial dependence of these probabilities. We incorporate dependence on the covariates and spatial dependence of methylation probability for observation from a particular cell by means of a binomial regression model with a latent Gaussian field. We run Mode Jumping Markov Chain Monte Carlo algorithm (MJMCMC) across different choices of covariates in order to obtain the joint posterior distribution of parameters and models. This also allows to find the best set of covariates to model methylation probability within the genomic region of interest