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

Elior Rahmani; Regev Schweiger; Brooke Rhead; Lindsey A. Criswell; Lisa F. Barcellos; Eleazar Eskin; Saharon Rosset; Sriram Sankararaman; Eran Halperin

doi:10.1038/s41467-019-11052-9

Nature Communications (Jul 2019)

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

Elior Rahmani,
Regev Schweiger,
Brooke Rhead,
Lindsey A. Criswell,
Lisa F. Barcellos,
Eleazar Eskin,
Saharon Rosset,
Sriram Sankararaman,
Eran Halperin

Affiliations

Elior Rahmani: Department of Computer Science, University of California, Los Angeles
Regev Schweiger: Blavatnik School of Computer Science, Tel Aviv University
Brooke Rhead: Computational Biology Graduate Group, University of California, Berkeley
Lindsey A. Criswell: Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California, San Francisco
Lisa F. Barcellos: School of Public Health, University of California, Berkeley
Eleazar Eskin: Department of Computer Science, University of California, Los Angeles
Saharon Rosset: Department of Statistics, Tel Aviv University
Sriram Sankararaman: Department of Computer Science, University of California, Los Angeles
Eran Halperin: Department of Computer Science, University of California, Los Angeles

DOI: https://doi.org/10.1038/s41467-019-11052-9
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 11

Abstract

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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.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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