ArticleNature communications2024
Benchmarking of methods for DNA methylome deconvolution.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Article
- Epigenome-wide profiling identifies distinct DNA methylation architecture underlying ME/CFS and fibromyalgia symptom burden.Journal of translational medicine · 2026Article
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.Nature biotechnology · 2026Article
- Placental molecular subtypes of severe preeclampsia reveal divergent aging trajectories and fetal growth outcomes.medRxiv : the preprint server for health sciences · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Longitudinal cell-free DNA methylome and fragmentome profiles in health uncover signatures of cell type and demographic origin.Genome medicine · 2026Article
- Immune Cell Type-Specific DNA Methylation Regions Associate With 24-Hour Blood Pressure Regulation in Black People.Journal of the American Heart Association · 2026Article
- A unified framework for multiomics deconvolution.Nature methods · 2026Article
- Guidelines on optimizing DNA methylation reference panels for cell-type deconvolution.Communications biology · 2026Article
- DNA methylation estimates of immune cell abundance have prognostic potential in triple negative breast cancer.Clinical epigenetics · 2026Article
- Non-invasive epidermis sampling for DNA methylation-based prediction of skin cancer phenotypes.NPJ precision oncology · 2026Article
- Circulating neuron-derived cfDNA for blood-based detection of Alzheimer's and other neurodegenerative conditions.Frontiers in neurology · 2026Article
- Analysis Methods for Diagnosing Rare Neurodevelopmental Diseases with Episignatures: A Systematic Review of the Literature.Biomedicines · 2025Review
- Expanding the scope of non-invasive prenatal screening.Nature genetics · 2025Review
- A ternary-code DNA methylome atlas of mouse tissues.Genome biology · 2025Article
- In silico purification improves DNA methylation-based classification rates of pediatric low-grade gliomas.Acta neuropathologica · 2025Article
- Computational analysis of DNA methylation from long-read sequencing.Nature reviews. Genetics · 2025Review
- Read-level DNA methylation deconvolution enhances circulating tumor DNA detection.Briefings in bioinformatics · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Defining the number and abundance of different cell types in tissues is important for understanding disease mechanisms as well as for diagnostic and prognostic purposes. Typically, this is achieved by immunohistological analyses, cell sorting, or single-cell RNA-sequencing. Alternatively, cell-specific DNA methylome information can be leveraged to deconvolve cell fractions from a bulk DNA mixture. However, comprehensive benchmarking of deconvolution methods and modalities was not yet performed. Here we evaluate 16 deconvolution algorithms, developed either specifically for DNA methylome data or more generically. We assess the performance of these algorithms, and the effect of normalization methods, while modeling variables that impact deconvolution performance, including cell abundance, cell type similarity, reference panel size, method for methylome profiling (array or sequencing), and technical variation. We observe differences in algorithm performance depending on each these variables, emphasizing the need for tailoring deconvolution analyses. The complexity of the reference, marker selection method, number of marker loci and, for sequencing-based assays, sequencing depth have a marked influence on performance. By developing handles to select the optimal analysis configuration, we provide a valuable source of information for studies aiming to deconvolve array- or sequencing-based methylation data.
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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.