ArticleBioinformatics (Oxford, England)2021
Complete deconvolution of DNA methylation signals from complex tissues: a geometric approach.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Epigenetic biomarkers in inflammatory bowel diseases-computational challenges and opportunities.Journal of Crohn's & colitis · 2026Review
- Scalable screening of ternary-code DNA methylation dynamics associated with human traits.Cell genomics · 2025Article
- Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations.STAR protocols · 2025Review
- A Multicellular In Vitro Model of the Human Intestine with Immunocompetent Features Highlights Host-Pathogen Interactions During Early Salmonella Typhimurium Infection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Reference-free deconvolution of complex samples based on cross-cell-type differential analysis: Systematic evaluations with various feature selection options.Frontiers in genetics · 2025Article
- Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies.Scientific reports · 2024Article
- Computational deconvolution of DNA methylation data from mixed DNA samples.Briefings in bioinformatics · 2024Review
- CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.Bioinformatics (Oxford, England) · 2024Article
- Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationIt is a common practice in epigenetics research to profile DNA methylation on tissue samples, which is usually a mixture of different cell types. To properly account for the mixture, estimating cell compositions has been recognized as an important first step. Many methods were developed for quantifying cell compositions from DNA methylation data, but they mostly have limited applications due to lack of reference or prior information.
resultsWe develop Tsisal, a novel complete deconvolution method which accurately estimate cell compositions from DNA methylation data without any prior knowledge of cell types or their proportions. Tsisal is a full pipeline to estimate number of cell types, cell compositions and identify cell-type-specific CpG sites. It can also assign cell type labels when (full or part of) reference panel is available. Extensive simulation studies and analyses of seven real datasets demonstrate the favorable performance of our proposed method compared with existing deconvolution methods serving similar purpose. AVAILABILITY AND IMPLEMENTATION: The proposed method Tsisal is implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.