Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

Abstract

DNA methylation sequencing is becoming increasingly popular, yielding genome-wide methylome data at single-base pair resolution through the novel cost- and labor-optimized protocols. It has tremendous potential for cell-type heterogeneity analysis, particularly in tumors, due to intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, their systematic evaluation has not been performed so far. Here, we thoroughly review and evaluate five previously published deconvolution methods: Bayesian epiallele detection (BED), PRISM, csmFinder + coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation. Accordingly, we individually assessed the performance of each step and demonstrated the impact of the former step upon the performance of the following one. In conclusion, we demonstrate the best method showing the highest accuracy in different samples, and infer factors affecting cell-type deconvolution performance according to the number of cell types in the mixture. We found that cell-type deconvolution performance is influenced by different factors according to the number of components in the mixture. Whereas selecting similar genomic regions to DMRs generally contributed to increasing the performance in bi-component mixtures, the uniformity of cell-type distribution showed a high correlation with the performance in five cell-type bulk analyses.

Decoding single-cell multiomics: scMaui - A deep learning framework for uncovering cellular heterogeneity in presence of batch Effects and missing data

“…scMaui can explain both mouse embryo development and cellular heterogeneity based on DNA methylation and gene expression DNA methylation, especially occuring at CpG sites, involves cell-type-specific signals as well as carries tumour heterogeneity in mammals [29][30][31] , and many computational methods have been published for such analysis based on bisulfite sequencing (BS-seq) data 32 . In particular, hypermethylation at gene promoters commonly behaves as a gene suppressor and can lead to dysregulated gene expression associated with tumour 33,34 .…”