Bayesian Functional Clustering and VMR Identification in Methylation Microarray Data

Abstract

The study of the relation between DNA and health and disease has had a lot of time, energy, and money invested in it over the years. As more scientific knowledge has accumulated, it has become clear that the relations between DNA and health isn’t just a function of the sequence of nucleotide bases, but also on permanent modifications of DNA that affect DNA transcriptions and thus have a macroscopic effect on an individual. The study of modifications to DNA is known as epigenetics.Epigenetic changes have been shown to play a role in certain diseases, including cancer (Novak 2004). Finding locations of differential methylation in two groups of cells is an ongoing area of research in both science and bioinformatics. The number of developed statistical methods for establishing differential DNA methylation between two groups is limited (Bock 2012). Many developed methods are developed for nextgeneration sequencing data and may not work for microarray data, and vice versa. Bisulfite sequencing, the next-generation sequencing technique for attaining methylation data, often comes with limited sample size and considerations must be made for low and variable coverage, and smoothing the methylation values. The analysis of nextgeneration sequencing data also involves small sample sizes.In addition, these methods can be sensitive to how individual CpG regions are grouped together as a region for analysis. If the DMRs are small relative to the sizes of 5 established regions, then the method may not detect a region as having differential methylation. Robust methods for clustering microarray data have also been an ongoing area of research. It is desirable to have a method that could be applied to microarray data could increase the sample size and mitigate the previous problems if the method used is robust to missing values, outliers, and microarray data noise. Functional clustering has shown to be effective when properly conducted on gene expression data. It can be used when the data have temporal measurements to identify genes that are possibly co-expressed. The clustering of methylation data can also be shown to identify epigenetic subgroups that can potentially be very useful (Wang, 2011). [introduction]