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Statistical challenges of high‐dimensional methylation data

With the fast growing field of epigenetics comes the need to better understand the intricacies of DNA methylation data analysis. High‐throughput profiling using techniques, such as Illumina's BeadArray assay, enable the quantitative assessment of methylation. Challenges arise from the fact that resulting methylation levels (so‐called beta values) are proportions between 0 and 1, often from an asymmetric, bimodal distribution with peaks close to 0 and 1. Therefore, the majority of standard statistical approaches do not apply. The logit transformation into so‐called M‐values is a common approach to circumvent this problem and aims to allow the use of common statistical methods. However, it can be observed that the transformation from beta to M‐values does not necessarily result in an approximately homoscedastic distribution. Often, bimodality, asymmetry and heteroscedasticity are conserved even after transformation.