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http://dx.doi.org/10.5351/KJAS.2011.24.4.609

A Finite Mixture Model for Gene Expression and Methylation Pro les in a Bayesian Framewor  

Jeong, Jae-Sik (Department of Biostatistics, Indiana University)
Publication Information
The Korean Journal of Applied Statistics / v.24, no.4, 2011 , pp. 609-622 More about this Journal
Abstract
The pattern of methylation draws significant attention from cancer researchers because it is believed that DNA methylation and gene expression have a causal relationship. As the interest in the role of methylation patterns in cancer studies (especially drug resistant cancers) increases, many studies have been done investigating the association between gene expression and methylation. However, a model-based approach is still in urgent need. We developed a finite mixture model in the Bayesian framework to find a possible relationship between gene expression and methylation. For inference, we employ Expectation-Maximization(EM) algorithm to deal with latent (unobserved) variable, producing estimates of parameters in the model. Then we validated our model through simulation study and then applied the method to real data: wild type and hydroxytamoxifen(OHT) resistant MCF7 breast cancer cell lines.
Keywords
Expectation-Maximization; hierarchical statistical model; latent variable; methylation; mixture model;
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