BACKGROUND: Intra-sample cellular heterogeneity presents numerous challenges to 
the identification of biomarkers in large Epigenome-Wide Association Studies 
(EWAS). While a number of reference-based deconvolution algorithms have emerged, 
their potential remains underexplored and a comparative evaluation of these 
algorithms beyond tissues such as blood is still lacking.
RESULTS: Here we present a novel framework for reference-based inference, which 
leverages cell-type specific DNAse Hypersensitive Site (DHS) information from 
the NIH Epigenomics Roadmap to construct an improved reference DNA methylation 
database. We show that this leads to a marginal but statistically significant 
improvement of cell-count estimates in whole blood as well as in mixtures 
involving epithelial cell-types. Using this framework we compare a widely used 
state-of-the-art reference-based algorithm (called constrained projection) to 
two non-constrained approaches including CIBERSORT and a method based on robust 
partial correlations. We conclude that the widely-used constrained projection 
technique may not always be optimal. Instead, we find that the method based on 
robust partial correlations is generally more robust across a range of different 
tissue types and for realistic noise levels. We call the combined algorithm 
which uses DHS data and robust partial correlations for inference, EpiDISH 
(Epigenetic Dissection of Intra-Sample Heterogeneity). Finally, we demonstrate 
the added value of EpiDISH in an EWAS of smoking.
CONCLUSIONS: Estimating cell-type fractions and subsequent inference in EWAS may 
benefit from the use of non-constrained reference-based cell-type deconvolution 
methods.