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Sparse graphical models for exploring gene expression data. (English) Zbl 1047.62104
Summary: 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.

MSC:
62P10 Applications of statistics to biology and medical sciences; meta analysis
62J99 Linear inference, regression
05C90 Applications of graph theory
62F15 Bayesian inference
Software:
HdBCS; Graphviz
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