Department of Statistics Seminar
North Carolina State University

presents

Dan Nettleton
Iowa State University

Identifying Differentially Expressed Gene Categories via a Hidden Markov Model Approach for Testing Nodes on a Directed Acyclic Graph


ABSTRACT

Gene category testing problems involve testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph.  The logical relationships among the nodes in the graph imply that only some configurations of true and false null hypotheses are possible and that a test for a given node should depend on data from neighboring nodes.  We use a multivariate nonparametric permutation test to obtain a p-value for each gene category.  We then model these p-values with a hidden Markov model that takes the relationships among categories into account.   Using a Markov chain Monte Carlo approach, we provide coherent decisions about the differential expression status of each category in this structured multiple hypothesis testing problem.  The method - which provides an alternative to get set enrichment analysis and related techniques - will be illustrated by testing Gene Ontology terms for evidence of differential expression.  This is joint work with Iowa State Ph.D. student Kun Liang.

Friday, December 05, 2008
3:35-4:35 pm
321 Riddick

Refreshments will be served in the Riddick Reading Room at 3:00pm.  NOTE:  No food or drink is allowed in any of the classrooms in Riddick Hall.