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Epigenetic observations are represented by the total number of reads from agiven pool of cells and the number of methylated reads, making it reasonable tomodel this data by a binomial distribution. There are numerous factors that caninfluence the probability of success in a particular region. Moreover, there isa strong spatial (alongside the genome) dependence of these probabilities. Weincorporate dependence on the covariates and the spatial dependence of themethylation probability for observations from a pool of cells by means of abinomial regression model with a latent Gaussian field and a logit linkfunction. We apply a Bayesian approach including prior specifications on modelconfigurations. We run a mode jumping Markov chain Monte Carlo algorithm(MJMCMC) across different choices of covariates in order to obtain the jointposterior distribution of parameters and models. This also allows finding thebest set of covariates to model methylation probability within the genomicregion of interest and individual marginal inclusion probabilities of thecovariates.

A Bayesian binomial regression model with latent Gaussian processes for modelling DNA methylation