ArticleBriefings in bioinformatics2022
A systematic assessment of cell type deconvolution algorithms for DNA methylation data.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- A robust workflow to benchmark deconvolution of multi-omic data.Genome biology · 2025Article
- A development and validation of predictive model based on novel immune-related gene-based subtypes for the risk assessment of cutaneous melanoma.Translational cancer research · 2025Article
- An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data.Nature communications · 2025Article
- Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data.Genomics, proteomics & bioinformatics · 2025Article
- A systematic evaluation of cell-type-specific differential methylation analysis in bulk tissue.Briefings in bioinformatics · 2025Article
- EpipwR: efficient power analysis for EWAS with continuous outcomes.Bioinformatics advances · 2025Article
- Unifying DNA methylation-basedBioinformatics advances · 2025Article
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- Targeting the hypothalamus for modeling age-related DNA methylation and developing OXT-GnRH combinational therapy against Alzheimer's disease-like pathologies in male mouse model.Nature communications · 2024Article
- Neural-net-based cell deconvolution from DNA methylation reveals tumor microenvironment associated with cancer prognosis.NAR cancer · 2024Article
- Benchmarking of methods for DNA methylome deconvolution.Nature communications · 2024Article
- 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
- Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology.Nature communications · 2024Article
- Challenges and perspectives in computational deconvolution of genomics data.Nature methods · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.
Indexed as
Identifiers
What OpenQuestion holds
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.