Modeling the Marginal Distribution of Gene Expression with Mixture Models

We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal expression distribution of the gene. The base distributions used were normal, lognormal and gamma. The standard expectation-maximization algorithm was used to learn the model parameters. Experiments with articifial data were performed to ascertain the robustness of learning. Applying the procedure to data from two publicly available microarray datasets, we conclude that lognormal performed the best function for modeling the marginal distributions of gene expression. Our results should provide some guidance in the development of informed priors or gene specific normalization for use with gene network inference algorithms.

• Type: Conference
• Year: 2008
• Where: FGCN
• Authors: Edward Wijaya, Hajime Harada, Paul Horton
• Keywords: Communications, FGCN 2008, Fitting Mixture Models, Gene Specific Normalization, Marginal Expression Distribution