Cell type deconvolution of methylated cell-free DNA at the resolution of individual reads
P. Keukeleire (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Stavros Makrodimitris – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
M.J.T. Reinders – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
Megha Khosla – Coach (TU Delft - Multimedia Computing)
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Abstract
Cell-free DNA (cfDNA) are DNA fragments originating from dying cells that enter the plasma. Uncontrolled cell death, for example caused by cancer, induces an elevated concentration of cfDNA. As a result, determining the cell type origins of cfDNA can provide information about an individual's health. This research looks into how to increase the sensitivity of a methylation-based cell type deconvolution method. We do this by adapting an existing method, CelFiE, which uses the methylation values of individual CpG sites to estimate cell type proportions. Our new method, named CelFEER, instead differentiates cell types by the average methylation values of individual reads. We additionally improved the originally reported performance of CelFiE by using a new approach for finding marker regions in the genome that are differentially methylated between cell types. This approach compares the methylation values over 500 bp regions instead of at single CpG sites and solely takes hypomethylated regions into account. We show that CelFEER estimates cell type proportions with a higher correlation than CelFiE on simulated mixtures of cfDNA. Moreover, we found that it can find a significant difference between skeletal muscle cfDNA in ALS patients (n=4) and a control group (n=4).