Statistical approaches for the analysis of DNA methylation microarray data

Abstract

Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.

Figure 1

Three main approaches to DNA methylation microarray analysis. A) Black circles denote methylated CpGs and white circles denote unmethylated CpGs. B) Illumina’s bisulfite treatmentbased approach. Cy3/Cy5 labeling varies between Infinium I and Infinium II probes, C) Affinity enrichment using methylcytosine immunoprecipitation (IP) D) Methylation-sensitive restriction digestion using McrBC.