SPARSE GRAPHICAL MODELS FOR EXPLORING GENE EXPRESSION DATA
Adrian Dobra, Chris Hans, Beatrix Jones, Joseph R Nevins and Mike West
Duke University
We discuss the theoretical structure and constructive methodology for
large-scale graphical models, motivated by their potential in evaluating and aiding the
exploration of patterns of association in gene expression data. The theoretical
discussion covers
basic ideas and connections between Gaussian graphical models, dependency networks and
specific classes of directed acyclic graphs we refer to as compositional networks.
We describe a constructive approach to generating
interesting graphical models for very high-dimensional distributions that builds on
the relationships between these various stylized graphical representations. Issues
of consistency of models and priors across dimension are key.
The resulting methods are of value in evaluating patterns of association
in large-scale gene expression data with a view to generating biological insights
about genes related to a known molecular pathway or set of specified genes.
Some initial examples relate to the estrogen receptor pathway in
breast cancer, and the Rb/E2F cell proliferation control pathway.
Keywords:
Bayesian regression analysis; Compositional networks;
Estrogen receptor gene and pathway; ER pathway; Gene expression; Graphical models; Model selection;
Rb/E2F genes and pathway; Transitive gene expression pathways
The manuscript is available in pdf format
The paper is to appear in the Journal of Multivariate Analysis in
2004.