An Evaluation of Statistical Methods for DNA Methylation Microarray Data Analysis

• Activity Number: 533
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Dongmei Li* and Zidian Xie and Marc Le Pape and Timothy Dye
• Companies: University of Rochester and University of Rochester and University of Hawaii and University of Rochester
• Keywords: DNA methylation ; Power ; Stability

Abstract

DNA methylation offers a process for elucidating how epigenetic information affects gene expression. Beta values and M values are commonly used to quantify DNA methylation. Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov-Smirnov test, permutation test, empirical Bayes method, and bump hunting method. We compared six statistical approaches relevant to DNA methylation microarray analysis in terms of false discovery rate control, statistical power, and stability through simulation studies and real data examples. For DNA methylation data analysis purposes, different results were observed between beta and M values in terms of false discovery rate control, power, and stability for correlated methylation levels across CpG loci. For DNA methylation studies with small sample size, the empirical Bayes method is recommended when DNA methylation levels across CpG loci are independent, while the bump hunting method is recommended when DNA methylation levels are correlated across CpG loci. All methods are acceptable for medium or large sample sizes.