Benchmarking of Methods for DNA Methylome Deconvolution

Abstract

Abstract Defining the number and abundance of different cell types in tissues is important for understanding disease mechanisms as well as for diagnostic and prognostic purposes. Typically, this is achieved by immunohistological analyses, cell sorting, or single-cell RNA-sequencing. Alternatively, cell-specific DNA methylome information can be leveraged to deconvolute cell fractions from a bulk DNA mixture. However, comprehensive benchmarking of deconvolution methods and modalities was not yet performed. Here we evaluated 13 deconvolution algorithms, developed either specifically for DNA methylome data or more generically. We assessed the performance of these algorithms, and the effect of normalization methods, while modelling variables that impact deconvolution performance, including cell abundance, cell type similarity, reference panel size, method for methylome profiling (array or sequencing), and technical variation. We observed differences in algorithm performance depending on each these variables, emphasizing the need for tailoring deconvolution analyses. The complexity of the reference, the number of marker loci and, for sequencing-based assays, the sequencing depth have a marked influence on performance. By developing handles to select the optimal analysis configuration, we provide valuable source of information for studies aiming to deconvolute array- or sequencing-based methylation data.