ArticleFrontiers in genetics2020
DeconPeaker, a Deconvolution Model to Identify Cell Types Based on Chromatin Accessibility in ATAC-Seq Data of Mixture Samples.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Integrated inference of cellular compositions and gene expression programs by deconvolution.Cell regeneration (London, England) · 2026Article
- STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.Briefings in bioinformatics · 2026Article
- An epigenomic investigation of atrial fibrillation in a matched left and right atrial human cohort.Clinical epigenetics · 2026Article
- FENGC: Targeted, Multiplexed, and Integrated Single-Molecule Profiling of Chromatin Accessibility and DNA Methylation.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Unico: a unified model for cell-type resolution genomics from heterogeneous omics data.Genome biology · 2025Article
- Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data.Journal of proteome research · 2025Article
- Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data.Genomics, proteomics & bioinformatics · 2025Article
- Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.Nature reviews. Molecular cell biology · 2025Review
- FOXN3-AS1: A Candidate Prognostic Marker and Epigenetic Target with Immunotherapeutic Implications in Acute Myeloid Leukemia.Current medicinal chemistry · 2025Article
- Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.eLife · 2024Article
- Mitochondrial genetic variations in leukemia: a comprehensive overview.Blood science (Baltimore, Md.) · 2024Review
- Scanpro is a tool for robust proportion analysis of single-cell resolution data.Scientific reports · 2024Article
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- Challenges and perspectives in computational deconvolution of genomics data.Nature methods · 2024Review
- Gene-regulation modules in nonalcoholic fatty liver disease revealed by single-nucleus ATAC-seq.Life science alliance · 2023Article
- Linking environmental risk factors with epigenetic mechanisms in Parkinson's disease.NPJ Parkinson's disease · 2023Review
- Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature.Nature communications · 2023Article
- Computational analysis of heat shock proteins and ferroptosis-associated lncRNAs to predict prognosis in acute myeloid leukemia patients.Frontiers in genetics · 2023Article
- scMethBank: a database for single-cell whole genome DNA methylation maps.Nucleic acids research · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While our understanding of cellular and molecular processes has grown exponentially, issues related to the cell microenvironment and cellular heterogeneity have sparked a new debate concerning the cell identity. Cell composition (chromatin and nuclear architecture) poses a strong risk for dynamic changes in the diseased condition. Since chromatin accessibility patterns play a major role in human diseases, it is therefore anticipated that a deconvolution tool based on open chromatin data will provide better performance in identifying cell composition. Herein, we have designed the deconvolution tool "DeconPeaker," which can precisely define the uniqueness among subpopulations of cells using open chromatin datasets. Using this tool, we simultaneously evaluated chromatin accessibility and gene expression datasets to estimate cell types and their respective proportions in a mixture of samples. In comparison to other known deconvolution methods, we observed the lowest average root-mean-square error (RMSE = 0.042) and the highest average correlation coefficient (
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