Biostatistics Seminar: Peter Mueller

Presentation: Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis

Speaker: Peter Mueller, Ph.D., Professor and Chair (interim) of Statistics and Data Science, Professor of Mathematics, University of Texas at Austin

Abstract: Constructing gene regulatory networks is a fundamental task in systems biology. We introduce a Gaussian reciprocal graphical model for inference about gene regulatory relationships by integrating mRNA gene expression and DNA level information including copy number and methylation. Data integration allows for inference on the directionality of certain regulatory relationships, which would be otherwise indistinguishable due to Markov equivalence. Efficient inference is developed based on simultaneous equation models. Bayesian model selection techniques are adopted to estimate the graph structure. We illustrate our approach by simulations and two applications in ZODIAC pairwise gene interaction analysis and colon adenocarcinoma pathway analysis.

Y. Ni, Y. Ji and P. Mueller Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis

Date/Time
Thu, Oct 19, 2017, 3:30pm to 5:00pm
Location
Room T-639 (HST)
Sponsors

Faculty Coordinator: Noah Simon, nrsimon@uw.edu

Seminar Coordinator: Sandra Coke, sjcoke@uw.edu