title: Log-Linear Models for Gene Association.
abstract: We describe a class of log-linear models for the detection of interactions in high-dimensional genomic data. This class of models leads to a Bayesian model selection algorithm that can be applied to data that have been reduced to contingency tables using ranks of observations within subjects, and discretization of these ranks within gene/network components. Many normalization issues associated with the analysis of genomic data are thereby avoided. A prior density based on Ewens' sampling distribution is used to restrict the number of interacting components assigned high posterior probability, and the calculation of posterior model probabilities is expedited by approximations based on the likelihood ratio statistic. Simulation studies are used to evaluate the efficiency of the resulting algorithm for known interaction structures. Finally, the algorithm is validated in a microarray study for which it was possible to obtain biological confirmation of detected interactions.
authors: Jianhua Hu is Assistant Professor of Biostatistics, Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030 (E-mail: jhu@mdanderson.org ). Adarsh Joshi is graduate student, Department of Statistics, Texas A&M University, College Station, TX 77030 (E- mail: adarsh@stat.tamu.edu ). Valen E. Johnson is Professor of Biostatistics, Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 447, Houston, TX 77030 (E-mail: vejohnson@mdanderson.org ).
authors: Jianhua
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authors: Hu
authors: Adarsh
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authors: Joshi
authors: Valen E
authors: VE
authors: Johnson