A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies
By Andrew E. Teschendorff, Charles E. Breeze, Shijie C. Zheng, Stephan Beck. BMC Bioinformatics, 2017
416 citations recorded by the source corpus.
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. 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. Estimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/fea164ab54ccae27ca5716971ce3514b42e33cef
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In Molecular Biology
Citations in the corpus
- MethylNet: an automated and modular deep learning approach for DNA methylation analysis (2019, methodology): “EPIC [56] using two different deconvolution methods Robust Partial Correlations (RPC) and Cibersort implemented in EpiDISH [57].”