A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10229)


Genome-wide DNA methylation levels measured from a target tissue across a population have become ubiquitous over the last few years, as methylation status is suggested to hold great potential for better understanding the role of epigenetics. Different cell types are known to have different methylation profiles. Therefore, in the common scenario where methylation levels are collected from heterogeneous sources such as blood, convoluted signals are formed according to the cell type composition of the samples. Knowledge of the cell type proportions is important for statistical analysis, and it may provide novel biological insights and contribute to our understanding of disease biology. Since high resolution cell counting is costly and often logistically impractical to obtain in large studies, targeted methods that are inexpensive and practical for estimating cell proportions are needed. Although a supervised approach has been shown to provide reasonable estimates of cell proportions, this approach leverages scarce reference methylation data from sorted cells which are not available for most tissues and are not appropriate for any target population. Here, we introduce BayesCCE, a Bayesian semi-supervised method that leverages prior knowledge on the cell type composition distribution in the studied tissue. As we demonstrate, such prior information is substantially easier to obtain compared to appropriate reference methylation levels from sorted cells. Using real and simulated data, we show that our proposed method is able to construct a set of components, each corresponding to a single cell type, and together providing up to 50% improvement in correlation when compared with existing reference-free methods. We further make a design suggestion for future data collection efforts by showing that results can be further improved using cell count measurements for a small subset of individuals in the study sample or by incorporating external data of individuals with measured cell counts. Our approach provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.


DNA methylation Epigenetics Bayesian model Cell type composition Cell type proportions Tissue heterogeneity 



We would like to thank Lana Martin for feedback on the manuscript. This research was partially supported by the Edmond J. Safra Center for Bioinformatics at Tel Aviv University. E.H., E.R., L.S. and R.S. were supported in part by the Israel Science Foundation (Grant 1425/13), E.H., L.S. and R.S. by the United States Israel Binational Science Foundation grant 2012304. E.R. and L.S. were supported by Len Blavatnik and the Blavatnik Research Foundation. R.S. was supported by the Colton Family Foundation. E.E. was supported by National Science Foundation grants 1065276, 1302448, 1320589 and 1331176, and National Institutes of Health grants R01-GM083198, R01-ES021801, R01-MH101782, R01-ES022282 and U54EB020403.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Tel Aviv UniversityTel AvivIsrael
  2. 2.University of California Los AngelesLos AngelesUSA

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