A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference
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- Rahmani E., Schweiger R., Shenhav L., Eskin E., Halperin E. (2017) A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference. In: Sahinalp S. (eds) Research in Computational Molecular Biology. RECOMB 2017. Lecture Notes in Computer Science, vol 10229. Springer, Cham
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.