A method of sample-wise region-set enrichment analysis for DNA methylomics

Abstract

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.

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.

Keywords::

• DNA methylome
• functional annotation
• gene set analysis
• region set analysis
• sample wise analysis
• transcription factor binding site

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.tandfonline.com/doi/suppl/10.2217/epi-2021-0065

Acknowledgments

The authors thank K Inaki, Y Uemura, N Fukui, M Araki, T Hagio, T Kaneyasu, S Konno, N Matsumura, K Kiyotani, Y Nakamura, Y Miki and T Noda for helpful discussions. The authors also thank Minako Hoshida for administrative assistance and R Jackson for editing a draft of this manuscript.

Financial&competing interests disclosure

This work was supported by JSPS KAKENHI; grant numbers JP20K09634 (O Gotoh), JP18K07338 (S Mori), JP17K18337 (O Gotoh) and JP15K06861 (S Mori), by the Vehicle Racing Commemorative Foundation; grant numbers 5144, 5274 and 5393 (S Mori), and by Princess Takamatsu Cancer Research Fund; grant number 11-24317 (S Mori). JT Chang was funded by grant RP170668 from the Cancer Prevention and Research Institute of Texas. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained institutional review board approval from Japanese Foundation for Cancer Research for the research described. Since the current work is to develop a methodological framework and employed publicly available data, there was no need to obtain informed consent from the patients.

Data sharing statement

The source code of Methyl_ssRSEA.R and the example data will be available from the GitHub repository (https://github.com/jfcr-genome/Methyl-ssRSEA) after acceptance of the manuscript. The code for reviewer is uploaded through the manuscript submission system.