Distinguishing Biological from Technical Sources of Variation Using a Combination of Methylation Datasets

摘要

DNA methylation remains one of the most widely studied epigenetic markers. One of the major challenges in population studies of methylation is the presence of global methylation effects that may mask local signals [1, 2]. Such global effects may be due to either technical effects (e.g., batch effects) or biological effects (e.g., cell type composition, genetics). Many methods have been developed for the detection of such global effects, typically in the context of Epigenome-wide association studies [3-9]. However, current unsupervised methods do not distinguish between biological and technical effects, resulting in a loss of highly relevant information. Though supervised methods can be used to estimate known biological effects, it remains difficult to identify and estimate unknown biological effects that globally affect the methylome.