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scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information a...

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Autores principales: Kapourani, Chantriolnt-Andreas, Argelaguet, Ricard, Sanguinetti, Guido, Vallejos, Catalina A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056718/
https://www.ncbi.nlm.nih.gov/pubmed/33879195
http://dx.doi.org/10.1186/s13059-021-02329-8
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author Kapourani, Chantriolnt-Andreas
Argelaguet, Ricard
Sanguinetti, Guido
Vallejos, Catalina A.
author_facet Kapourani, Chantriolnt-Andreas
Argelaguet, Ricard
Sanguinetti, Guido
Vallejos, Catalina A.
author_sort Kapourani, Chantriolnt-Andreas
collection PubMed
description High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02329-8).
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spelling pubmed-80567182021-04-21 scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution Kapourani, Chantriolnt-Andreas Argelaguet, Ricard Sanguinetti, Guido Vallejos, Catalina A. Genome Biol Method High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02329-8). BioMed Central 2021-04-20 /pmc/articles/PMC8056718/ /pubmed/33879195 http://dx.doi.org/10.1186/s13059-021-02329-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Kapourani, Chantriolnt-Andreas
Argelaguet, Ricard
Sanguinetti, Guido
Vallejos, Catalina A.
scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution
title scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution
title_full scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution
title_fullStr scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution
title_full_unstemmed scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution
title_short scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution
title_sort scmet: bayesian modeling of dna methylation heterogeneity at single-cell resolution
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056718/
https://www.ncbi.nlm.nih.gov/pubmed/33879195
http://dx.doi.org/10.1186/s13059-021-02329-8
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