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Title: Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data Authors:  Alberto Cassese - Maastricht University (Netherlands) [presenting]
Qiwei Li - The University of Texas Southwestern Medical Center (United States)
Michele Guindani - University of California, Irvine (United States)
Marina Vannucci - Rice University (United States)
Abstract: A Bayesian hierarchical mixture regression model is developed for studying the association between a multivariate response, measured as counts on a set of features, and a set of covariates. We have available RNASeq and DNA methylation data on breast cancer patients at different stages of the disease. We account for heterogeneity and overdispersion of count data by considering a mixture of negative binomial distributions and incorporate the covariates into the model via a linear modeling construction on the mean components. Our modeling construction employs selection techniques allowing the identification of a small subset of features that best discriminate the samples, simultaneously selecting a set of covariates associated to each feature. Additionally, it incorporates known dependencies into the feature selection process via Markov random field priors. On simulated data, we show how incorporating existing information via the prior model can improve the accuracy of feature selection. In the case study, we incorporate knowledge on relationships among genes via a gene network, extracted from the KEGG database. Our data analysis identifies genes that are discriminatory of cancer stages and simultaneously selects significant associations between those genes and DNA methylation sites. A biological interpretation of our findings reveals several biomarkers that can help to understand the effect of DNA methylation on gene expression transcription across cancer stages.