DNA methylation, and specifically the reversible addition of methyl groups at CpG dinucleotides genome-wide, represents an important layer that is associated with the regulation of gene expression. In particular, aberrations in the methylation status have been noted across a diverse set of pathological states, including cancer. With the rapid development and uptake of large scale sequencing of short DNA fragments, there has been an explosion of data analytic methods for processing and discovering changes in DNA methylation across diverse data types. In this mini-review, we aim to condense many of the salient challenges, such as experimental design, statistical methods for differential methylation detection and critical considerations such as cell type composition and the potential confounding that can arise from batch effects, into a compact and accessible format. Our main interests, from a statistical perspective, include the practical use of empirical Bayes or hierarchical models, which have been shown to be immensely powerful and flexible in genomics and the procedures by which control of false discoveries are made. Of course, there are many critical platform-specific data preprocessing aspects that we do not discuss here. In addition, we do not make formal performance comparisons of the methods, but rather describe the commonly used statistical models and many of the pertinent issues; we make some recommendations for further study.
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