Methods for sparse and high-dimensional measurements of DNA methylation
Innovative technologies now allow us to probe the epigenome in more dimensions and at higher resolution than ever before. However, meaningful biological insights are challenging to uncover in these high-dimensional settings where classical statistics fail, and relevant signals can be masked by technical artifacts and systematic biases unique to each specialized assay. In this talk I will outline the major hurdles and advantages in analyzing DNA methylation data at single base and single cell resolution, and highlight examples from recent and ongoing work. In the first example, we show that a novel statistical method is able to detect a strong relationship between DNA methylation and gene expression despite the failure of previous approaches to do so. In the second example, we show how machine learning techniques can be applied to detect changes in the methylation of cell-free DNA that correlate with cancer status. Finally, I’ll discuss ongoing work on a model that pools sparse information across single cells to infer variably methylated regions informative of cell type.