||January 14th, 2019
||Kelly Engineering Center, Room 1001
||This seminar is free and open to the public.
Technology-independent estimation of cell type composition using differentially methylated regions
High-resolution genome-wide measurement of DNA methylation (DNAm) has become a widely used assay in biomedical research. A major challenge in measuring DNAm is variability introduced from intra-sample cellular heterogeneity, which is a convolution of DNAm profiles across cell types. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. This is particularly problematic in epigenome-wide association studies for human disease performed on whole blood, a heterogeneous tissue. To account for this source of variability, a first step is to determine the actual cell proportions of each sample. Currently, the most effective approach is based on fitting a linear model in which one assumes the DNAm profiles of the representative cell types are known. However, we can only make this assumption when a dedicated experiment is performed to provide a plug-in estimate for these profiles. Although this method works well in practice, technology-specific biases lead to platform-dependent plug-in profiles. As a result, to apply the current methods across technologies we are required to repeat these costly experiments for each platform. Here, we present a method that accurately estimates cell proportions agnostic to platform by first using experimental data to identify regions in which each cell type is clearly methylated or unmethlyated and model these as latent states. While the continuous measurements used in the linear model approaches are affected by platform-specific biases, the latent states are biologically driven and therefore technology independent, implying that experimental data only needs to be collected once. We demonstrate that our method accurately estimates the cell composition from whole blood samples and is applicable across multiple platforms, including microarray and sequencing platforms.
For more information about Stephanie Hicks, click here.