An Empirical Bayes Approach for Methylation Differentiation at the Single Nucleotide Resolution

DNA methylation is an important epigenetic phenomenon that is associated with a variety of diseases, particularly cancers. Recent development of high throughput sequencing technology has enabled researchers to investigate the methylation rate at a single nucleotide resolution for any given sample. Testing for methylation rate equality or difference between two samples, however, is challenged by the small sample size observed at many sites across the genome. Fisher's exact test is typically used in this situation; however, it is conservative and it cannot be used to test for specific difference in methylation rate between two samples. In this paper, we propose an empirical Bayes approach that utilizes the genome-wide data as prior information for methylation differentiation between two samples. We show that this new approach is more powerful than Fisher's exact test. In addition, it can be used to test for any specific methylation difference while controlling the false discovery rate (FDR). The new method is applied to a real data set from a colon tumor study.