Title: Bayesian analysis of chromosomal interactions in hi-c data using the hidden Markov random field model
Authors: Itunu Osuntoki - University of Essex (United Kingdom) [presenting]
Andrew Harrison - University of Essex (United Kingdom)
Hongsheng Dai - University of Essex (United Kingdom)
Yanchun Bao - University of Essex (United Kingdom)
Nicolae Radu Zabet - University of Essex (United Kingdom)
Abstract: There are different biological methods that have been developed over the years for analysis of the 3D structure of the DNA. Few computational and statistical methods have, however, been developed to analysis data generated using the Hi-C method. We follow statistical methodology to explore the Hi-C data. The Hi-C data is well suited to be analysed using a finite mixture model. The Potts model, a hidden Markov random field model, was employed to analyze the hidden (latent) components. The hidden components are categorised into three; noise, false signal and true signal. Using the Metropolis-within-Gibbs approach to analyze the data, the proposed method was able to detect interactions (short and long range) and false interactions. A large part of the significant interactions that we detect are found within Topological Associated Domains, which is one of the 3D structures known to occur in Hi-C data.