ArticleScientific reports2021
EMeth: An EM algorithm for cell type decomposition based on DNA methylation data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Immune Cell Type-Specific DNA Methylation Regions Associate With 24-Hour Blood Pressure Regulation in Black People.Journal of the American Heart Association · 2026Article
- Computational analysis of DNA methylation from long-read sequencing.Nature reviews. Genetics · 2025Review
- Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations.STAR protocols · 2025Review
- Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.eLife · 2024Article
- A novel method for cell deconvolution using DNA methylation in PCA space.BMC genomics · 2024Article
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- Benchmarking of methods for DNA methylome deconvolution.Nature communications · 2024Article
- Computational deconvolution of DNA methylation data from mixed DNA samples.Briefings in bioinformatics · 2024Review
- Challenges and perspectives in computational deconvolution of genomics data.Nature methods · 2024Review
- Deconvolution of tumor composition using partially available DNA methylation data.BMC bioinformatics · 2022Article
- Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes.Briefings in bioinformatics · 2022Article
- DAISM-DNNPatterns (New York, N.Y.) · 2022Article
- Mechanisms of DNA Methylation in Virus-Host Interaction in Hepatitis B Infection: Pathogenesis and Oncogenetic Properties.International journal of molecular sciences · 2021Review
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Authors and funding
4 authors.
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Abstract
We introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.
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