  Epigenetic observations are represented by the total number of reads from a
given pool of cells and the number of methylated reads, making it reasonable to
model this data by a binomial distribution. There are numerous factors that can
influence the probability of success in a particular region. Moreover, there is
a strong spatial (alongside the genome) dependence of these probabilities. We
incorporate dependence on the covariates and the spatial dependence of the
methylation probability for observations from a pool of cells by means of a
binomial regression model with a latent Gaussian field and a logit link
function. We apply a Bayesian approach including prior specifications on model
configurations. We run a 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 finding the
best set of covariates to model methylation probability within the genomic
region of interest and individual marginal inclusion probabilities of the
covariates.
