  DNA methylation is a significant driver of cell-type heterogeneity and has
been implicated in various regulatory processes ranging from cell
differentiation to imprinting. As the methyl group is embedded in the DNA
molecule, assessing DNA methylation is particularly promising in
liquid-biopsy-based approaches, as cell-free DNA retains information related to
its cell of origin. In this work, I leverage a recently profiled collection of
cell-sorted whole genome bisulfite profiles of 44 healthy cell types. The high
quality and purity of such data provide an ideal basis for discovering and
characterizing discriminative DNA methylation regions that could serve as a
reference for computational deconvolution. First, I characterize differentially
methylated regions between every pair of cell types, obtaining a meaningful
measure of divergence. Pairwise differences were then aggregated to identify a
set of uniquely (de)methylated regions (UMRs) for each cell type. Identified
UMRs are predominantly hypomethylated and their numbers vary significantly
across cell types. They are mostly located in enhancer regions and strongly
support cell-type-specific characteristics. As mapping onto UMRs has proven
unsuitable for deconvolution, I developed a novel approach utilizing the set
cover algorithm to select discriminative regions for this purpose. Based on
these regions, deconvolution was performed in two distinct approaches: a
beta-value-based and a read-level one. Both approaches outperform an existing
deconvolution software modeled on the same data 3-fold in terms of total
deconvolution error. Surprisingly, the beta-based approach slightly
outperformed the read-level one. Overall, I present an adaptable, end-to-end
software solution (methylcover) for obtaining accurate cell type deconvolution,
with possible future applications to non-invasive assays for disease detection
and monitoring.

Quantitative Methods;Genomics