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EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data
Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk ti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7650528/ https://www.ncbi.nlm.nih.gov/pubmed/32883324 http://dx.doi.org/10.1186/s13059-020-02126-9 |
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author | Teschendorff, Andrew E. Zhu, Tianyu Breeze, Charles E. Beck, Stephan |
author_facet | Teschendorff, Andrew E. Zhu, Tianyu Breeze, Charles E. Beck, Stephan |
author_sort | Teschendorff, Andrew E. |
collection | PubMed |
description | Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types. |
format | Online Article Text |
id | pubmed-7650528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76505282020-11-16 EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data Teschendorff, Andrew E. Zhu, Tianyu Breeze, Charles E. Beck, Stephan Genome Biol Method Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types. BioMed Central 2020-09-04 /pmc/articles/PMC7650528/ /pubmed/32883324 http://dx.doi.org/10.1186/s13059-020-02126-9 Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://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 Teschendorff, Andrew E. Zhu, Tianyu Breeze, Charles E. Beck, Stephan EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data |
title | EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data |
title_full | EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data |
title_fullStr | EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data |
title_full_unstemmed | EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data |
title_short | EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data |
title_sort | episcore: cell type deconvolution of bulk tissue dna methylomes from single-cell rna-seq data |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7650528/ https://www.ncbi.nlm.nih.gov/pubmed/32883324 http://dx.doi.org/10.1186/s13059-020-02126-9 |
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