Bayesian Modeling for Histone Modifications

• Activity Number: 234
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Ritendranath Mitra*+
• Companies: The University of Texas MD Anderson Cancer Center
• Keywords: Auto-logistic ; MCMC ; Pathway dependence ; Markov random fields

Abstract

Histone modifications (HMs) are an important post-translational feature. Different types of HMs are believed to co-regulate biological processes such as gene expression, and therefore are intrinsically dependent on each other. We develop inference for this complex biological network of HMs based on a graphical model for the dependence structure across HMs. A critical computational hurdle in the inference for the proposed graphical model is the evaluation of a normalization constant in an autologistic model that builds on the graphical model. We tackle the problem by constructing posterior Markov chain Monte Carlo (MCMC) simulation in a way that avoids the evaluation of these normalization constants. We report inference on HM dependence in a case study with ChIP-Seq data from a next-generation sequencing experiment. An important feature of our approach is that we can report coherent probabilities and estimates related to any event or parameter of interest, including honest uncertainties. Posterior inference is obtained from a joint probability