Work

Log-Linear Models for Gene Association

Year: 2009

Type: article

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.

Source: Journal of the American Statistical Association

DOI: 10.1198/jasa.2009.0025

Authors: Jianhua Hu, Adarsh Joshi, Valen E. Johnson

Institutions: The University of Texas MD Anderson Cancer Center, Texas A&M University, National Institutes of Health

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Institute of General Medical Sciences

Award: G4024790572