mEthAE: an Explainable AutoEncoder for methylation data

In the quest to unravel the mysteries of our epigenetic landscape, researchers are continually challenged by the relationships among CpG sites. Traditional approaches are often limited by the immense complexity and high dimensionality of DNA methylation data. To address this problem, deep learning algorithms, such as autoencoders, are increasingly applied to capture the complex patterns and reduce dimensionality into latent space. In this pioneering study, we introduce an innovative chromosome-wise autoencoder, termed mEthAE, specifically designed for the interpretive reduction of methylation data. mEthAE achieves an impressive 400-fold reduction in data dimensions without compromising on reconstruction accuracy or predictive power in the latent space. In attempt to go beyond mere data compression, we developed a perturbation-based method for interpretation of latent dimensions. Through our approach we identified clusters of CpG sites that exhibit strong connections across all latent dimensions, which we refer to as ‘global CpGs’. Remarkably, these global CpGs are more frequently highlighted in epigenome-wide association studies (EWAS), suggesting our method’s ability to pinpoint biologically significant CpG sites. Our findings reveal a surprising lack of correlation patterns, or even physical proximity on the chromosome among these connected CpGs. This leads us to propose an intriguing hypothesis: our autoencoder may be detecting complex, long-range, non-linear interaction patterns among CpGs. These patterns, largely uncharacterised in current epigenetic research, hold the potential to shed new light on our understanding of epigenetics. In conclusion, this study not only showcases the power of autoencoders in untangling the complexities of epigenetic data but also opens up new avenues for understanding the hidden connections within CpGs. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes