In this paper we propose a Bayesian approach for inference about
dependence of high throughput gene expression. Our goals are to
use prior knowledge about pathways to anchor inference about
dependence among genes; to account for this dependence while
making inferences about differences in mean expression across
phenotypes; and to explore differences in the dependence itself
across phenotypes. Useful features of the proposed approach are
a model-based parsimonious representation of expression as an
ordinal outcome, a novel and flexible representation of prior
information on the nature of dependencies, and the use of a
coherent probability model over both the structure and strength
of the dependencies of interest. We evaluate our approach
through simulations and in the analysis of data on expression of
genes in the Complement and Coagulation Cascade pathway in
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