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Activity Number: 418
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #319036
Title: Nonparametric Bayesian Learning of Heterogeneous Dynamic Transcription Factor Networks
Author(s): Xiangyu Luo* and Yingying Wei
Companies: The Chinese University of Hong Kong and The Chinese University of Hong Kong
Keywords: Transcription Factor Networks ; Poisson Graphical Model ; Nonparametric Bayes ; Parallel Markov Chain Monte Carlo ; Next Generation Sequencing

Gene expression is largely controlled by transcription factors (TFs) in a collaborative manner. Therefore, understanding TF collaboration is crucial for elucidating gene regulation. The co-activation among TFs can be represented by networks. These networks are dynamic over diverse biological conditions and heterogeneous across the genome within each biological condition. Existing methods for constructing TF networks lack solid statistical models, analyze each biological condition separately, and enforce a single network for all the genome positions within one biological condition, which suffer from low statistical power and result in misleading spurious association. In this paper, we present a novel Bayesian nonparametric dynamic Poisson graphical model for inference on heterogeneous dynamic TF networks. Our approach automatically teases out the genome heterogeneity and borrows information across conditions to improve signal detection, thus offering a valid and efficient measure of TF co-activations. An efficient parallel Markov Chain Monte Carlo algorithm is developed for posterior computation. The proposed approach is applied to study TF associations in ENCODE cell lines.

Authors who are presenting talks have a * after their name.

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