Aim: Gene set analysis has commonly been used to interpret DNA methylome data. However, summarizing the DNA methylation level of a gene is challenging due to variability in the number, density and methylation levels of CpG sites, and the numerous intergenic CpGs. Instead, we propose to use region sets to annotate the DNA methylome. Methods: We developed single sample region-set enrichment analysis for DNA methylome (methyl-ssRSEA) to conduct sample-wise, region-set enrichment analysis. Results: Methyl-ssRSEA can handle both microarray- and sequencing-based platforms and reproducibly recover the known biology from the methylation profiles of peripheral blood cells and breast cancers. The performance was superior to existing tools for region-set analysis in discriminating blood cell types. Conclusion: Methyl-ssRSEA offers a novel way to functionally interpret the DNA methylome in the cell.
Keywords: DNA methylome; functional annotation; gene set analysis; region set analysis; sample wise analysis; transcription factor binding site.
Lay abstract Gene set analysis has been a common way to understand the meaning of DNA methylome data. However, organizing the DNA methylation level of a gene is challenging due to variation in the number, density and extent of methylation, of methylation sites, and the substantial number of methylation sites between genes. Instead, we propose to use region sets for the organization. We developed single sample region-set enrichment analysis for DNA methylome (methyl-ssRSEA) to conduct region-set analysis for every sample. Methyl-ssRSEA can handle both microarray- and sequencing-based methods and repeatedly find the known characters from the methylation patterns of peripheral blood cells and breast cancers. The performance was better than existing tools for region-set analysis in differentiating blood cell types. Methyl-ssRSEA offers a novel way to find the features of DNA methylome in the cell.