Systematic Evaluation of Cell Type Deconvolution Methods for Plasma Cell-free DNA

Abstract

AbstractPlasma cell-free DNA (cfDNA) is derived from cellular death in various tissues. Investigating the origin of cfDNA through tissue/cell type deconvolution allows us to detect changes in tissue homeostasis that occur during disease progression or in response to treatment. Consequently, cfDNA has emerged as a valuable noninvasive biomarker for disease detection and treatment monitoring. Although there are numerous methylation-based methods of cfDNA cell type deconvolution available, a comprehensive and systematic evaluation of these methods has yet to be conducted. In this study, we thoroughly benchmarked five previously published methods: MethAtlas, cfNOMe, CelFiE, CelFEER, and UXM. Utilizing deep whole-genome bisulfite sequencing data from 35 human cell types, we generated cfDNA mixtures with known ground truth to assess the deconvolution performance under various scenarios. Our findings indicate that different factors, including sequencing depth, reference marker selection, and reference completeness, influence cell type deconvolution performance. Notably, omitting cell types present in a mixture from the reference leads to suboptimal results. Despite each method exhibited distinct performances under various scenarios, CelFEER and UXM exhibit overall superior performance compared to the others. In summary, we comprehensively evaluated factors influencing methylation-based cfDNA cell type deconvolution and proposed general guidelines to maximize the performance.