A Bayesian Graphical Model for ChIP-Seq Data on Histone Modifications

• 成果类型: Article
• 署名作者: Mitra, Riten; Mueller, Peter; Liang, Shoudan; Yue, Lu; Ji, Yuan
• 署名单位: University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; NorthShore University Health System
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.746058
• 发表日期: 2013
• 页码: 69-80
• 关键词: chromatin; methylations; acetylation; expression; domains; map

摘要

Histone modifications (HMs) are an important post-translational feature. Different types of HMs are believed to co-exist and 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 using ChIP-Seq data. 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 Monte Carlo evaluation of ratios of normalization constants. We carry out a set of simulations to validate the proposed approach and to compare it with a standard approach using Bayesian networks. 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 model on latent indicators for the recorded HMs. We illustrate this in the motivating case study. An R package including an implementation of posterior simulation in C is available from Riten Mitra upon request.