Biological pathway selection through Bayesian integrative modeling.


Journal Article

Pathway analysis has become a central approach to understanding the underlying biology of differentially expressed genes. As large amounts of microarray data have been accumulated in public repositories, flexible methodologies are needed to extend the analysis of simple case-control studies in order to place them in context with the vast quantities of available and highly heterogeneous data sets. To address this challenge, we have developed a two-level model, consisting of 1) a joint Bayesian factor model that integrates multiple microarray experiments and ties each factor to a predefined pathway and 2) a point mass mixture distribution that infers which factors are relevant/irrelevant to each dataset. Our method can identify pathways specific to a particular experimental trait which are concurrently induced/repressed under a variety of interventions. In this paper, we describe the model in depth and provide examples of its utility in simulations as well as real data from a study of radiation exposure. Our analysis of the radiation study leads to novel insights into the molecular basis of time- and dose- dependent response to ionizing radiation in mice peripheral blood. This broadly applicable model provides a starting point for generating specific and testable hypotheses in a pathway-centric manner.

Full Text

Duke Authors

Cited Authors

  • Zheng, L; Yan, X; Suchindran, S; Dressman, H; Chute, JP; Lucas, J

Published Date

  • August 2014

Published In

Volume / Issue

  • 13 / 4

Start / End Page

  • 435 - 457

PubMed ID

  • 24937506

Pubmed Central ID

  • 24937506

Electronic International Standard Serial Number (EISSN)

  • 1544-6115

Digital Object Identifier (DOI)

  • 10.1515/sagmb-2013-0043


  • eng

Conference Location

  • Germany