清华合成与系统生物学中心
Tsinghua Center for Synthetic and Systems Biology

Bayesian Inference of Spatial Organizations of Chromosomes

Speaker: Ke Deng, Ph.D., Department of Statistics, Harvard University
Time: May, 23rd, 2013, 15:00 - 17:00 pm
Place: FIT 1-312a
ABSTRACT: Knowledge of spatial chromosomal organizations is critical for the study of transcriptional regulation and other nuclear processes in the cell. Recently, chromosome conformation capture (3C) based technologies, such as Hi-C and TCC, have been developed to provide a genome-wide, three-dimensional (3D) view of chromatin organization. Appropriate methods for analyzing these data and fully characterizing the 3D chromosomal structure and its structural variations are still under development. Here we describe a novel Bayesian probabilistic approach, denoted as ‘‘Bayesian 3D constructor for Hi-C data’’ (BACH), to infer the consensus 3D chromosomal structure. Applying BACH to a high resolution Hi-C dataset generated from mouse embryonic stem cells, we found that most local genomic regions exhibit homogeneous 3D chromosomal structures. We further constructed a model for the spatial arrangement of chromatin, which reveals structural properties associated with euchromatic and heterochromatic regions in the genome. We observed strong associations between structural properties and several genomic and epigenetic features of the chromosome.

A Bayesian View of Sliced Inverse Regression with Interaction Detection

Speaker: Jun Liu, Ph.D. Professor, Department of Statistics, Harvard University
Time: May, 23rd, 2013, 15:00 - 17:00 pm
Place: FIT 1-312
ABSTRACT: Previously we have proposed a Bayesian partition model for detecting interactive variables in a classification setting with discrete covariates. This framework takes advantage of the structure of the naïve Bayes classifier and introduces latent indicator variables for selecting variables and interactions. In our effort to extend the methods to continuous covariates, we found interesting connections with semi-parametric index models and the Sliced Inverse Regression method. In index models, the response is influenced by the covariates through an unknown function of several linear combinations of the predictors. Our finding of the Bayesian formulation of such models enabled us to propose a set of new models and methods that can effectively discover second-order effects and interactions among the covariates. A two-stage stepwise procedure based on likelihood ratio test is developed to select relevant predictors and a Bayesian model with dynamic slicing scheme is derived. The performance of the proposed procedure in comparison with some existing method is demonstrated through simulation studies.