PT  - JOURNAL ARTICLE
AU  - Mark D Robinson
AU  - Abdullah Kahraman
AU  - Charity W Law
AU  - Helen Lindsay
AU  - Malgorzata Nowicka
AU  - Lukas M Weber
AU  - Xiaobei Zhou
TI  - MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions
AID  - 10.1101/007120
DP  - 2014 Jan 01
TA  - bioRxiv
PG  - 007120
4099  - http://biorxiv.org/content/early/2014/08/29/007120.short
4100  - http://biorxiv.org/content/early/2014/08/29/007120.full
AB  - DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.