Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology

Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology

Compared to bulk data, cell-type-specific DNA methylation data provide higher resolution of epigenetic variation. Here, the authors introduce Tensor Composition Analysis, a novel computational approach for learning cell-type-specific DNA methylation from tissue-level bulk data, and show its applicat...

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Main Authors: Elior Rahmani, Regev Schweiger, Brooke Rhead, Lindsey A. Criswell, Lisa F. Barcellos, Eleazar Eskin, Saharon Rosset, Sriram Sankararaman, Eran Halperin
Format: Article
Language:English
Published: Nature Publishing Group 2019-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-019-11052-9
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spelling doaj-120d561a07024d518d2de3464a531e812021-05-11T12:30:50ZengNature Publishing GroupNature Communications2041-17232019-07-0110111110.1038/s41467-019-11052-9Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biologyElior Rahmani0Regev Schweiger1Brooke Rhead2Lindsey A. Criswell3Lisa F. Barcellos4Eleazar Eskin5Saharon Rosset6Sriram Sankararaman7Eran Halperin8Department of Computer Science, University of California, Los AngelesBlavatnik School of Computer Science, Tel Aviv UniversityComputational Biology Graduate Group, University of California, BerkeleyRussell/Engleman Rheumatology Research Center, Department of Medicine, University of California, San FranciscoSchool of Public Health, University of California, BerkeleyDepartment of Computer Science, University of California, Los AngelesDepartment of Statistics, Tel Aviv UniversityDepartment of Computer Science, University of California, Los AngelesDepartment of Computer Science, University of California, Los AngelesCompared to bulk data, cell-type-specific DNA methylation data provide higher resolution of epigenetic variation. Here, the authors introduce Tensor Composition Analysis, a novel computational approach for learning cell-type-specific DNA methylation from tissue-level bulk data, and show its application in epigenome-wide association studies.https://doi.org/10.1038/s41467-019-11052-9
collection DOAJ
language English
format Article
sources DOAJ
author Elior Rahmani
Regev Schweiger
Brooke Rhead
Lindsey A. Criswell
Lisa F. Barcellos
Eleazar Eskin
Saharon Rosset
Sriram Sankararaman
Eran Halperin
spellingShingle Elior Rahmani
Regev Schweiger
Brooke Rhead
Lindsey A. Criswell
Lisa F. Barcellos
Eleazar Eskin
Saharon Rosset
Sriram Sankararaman
Eran Halperin
Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
Nature Communications
author_facet Elior Rahmani
Regev Schweiger
Brooke Rhead
Lindsey A. Criswell
Lisa F. Barcellos
Eleazar Eskin
Saharon Rosset
Sriram Sankararaman
Eran Halperin
author_sort Elior Rahmani
title Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
title_short Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
title_full Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
title_fullStr Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
title_full_unstemmed Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
title_sort cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2019-07-01
description Compared to bulk data, cell-type-specific DNA methylation data provide higher resolution of epigenetic variation. Here, the authors introduce Tensor Composition Analysis, a novel computational approach for learning cell-type-specific DNA methylation from tissue-level bulk data, and show its application in epigenome-wide association studies.
url https://doi.org/10.1038/s41467-019-11052-9
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