Abbas Khalili1, Dustin Potter2,5, Pearlly Yan2, Lang Li3, Joe Gray4, Tim Huang2 and Shili Lin1,5
1Department of Statistics, The Ohio State University, Columbus, OH 43210. 2Human Cancer Genetics, The Ohio State University, Columbus, OH 43210. 3Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, One Cyclotron Rd. Indianapolis, IN 47405. 4Lawrence Berkeley National Laboratory, Berkeley, CA 94720. 5Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210.
Type: Original Research
Abstract: With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated, undifferentiated, and hypermethylated, from a single methylation microarray. This model was applied to study the methylation signatures of three breast cancer cell lines: MCF7, T47D, and MDAMB361. Biologically interesting and interpretable results are obtained, which highlights the heterogeneity nature of the three cell lines. This underlies the premise for the need of analyzing each of the microarray slides individually as opposed to pooling them together for a single analysis. Our comparisons with the fitted densities from the Normal-Uniform (NU) mixture model in the literature proposed for gene expression analysis show an improved goodness of fit of the GNG model over the NU model. Although the GNG model was proposed in the context of single-slide methylation analysis, it can be readily adapted to analyze multi-slide methylation data as well as other types of microarray data.
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