Unsupervised clustering and epigenetic classification of single cells

Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, the...

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Autores principales: Zamanighomi, Mahdi, Lin, Zhixiang, Daley, Timothy, Chen, Xi, Duren, Zhana, Schep, Alicia, Greenleaf, William J., Wong, Wing Hung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010417/
https://www.ncbi.nlm.nih.gov/pubmed/29925875
http://dx.doi.org/10.1038/s41467-018-04629-3
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author Zamanighomi, Mahdi
Lin, Zhixiang
Daley, Timothy
Chen, Xi
Duren, Zhana
Schep, Alicia
Greenleaf, William J.
Wong, Wing Hung
author_facet Zamanighomi, Mahdi
Lin, Zhixiang
Daley, Timothy
Chen, Xi
Duren, Zhana
Schep, Alicia
Greenleaf, William J.
Wong, Wing Hung
author_sort Zamanighomi, Mahdi
collection PubMed
description Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.
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spelling pubmed-60104172018-06-25 Unsupervised clustering and epigenetic classification of single cells Zamanighomi, Mahdi Lin, Zhixiang Daley, Timothy Chen, Xi Duren, Zhana Schep, Alicia Greenleaf, William J. Wong, Wing Hung Nat Commun Article Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity. Nature Publishing Group UK 2018-06-20 /pmc/articles/PMC6010417/ /pubmed/29925875 http://dx.doi.org/10.1038/s41467-018-04629-3 Text en © The Author(s) 2018 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zamanighomi, Mahdi
Lin, Zhixiang
Daley, Timothy
Chen, Xi
Duren, Zhana
Schep, Alicia
Greenleaf, William J.
Wong, Wing Hung
Unsupervised clustering and epigenetic classification of single cells
title Unsupervised clustering and epigenetic classification of single cells
title_full Unsupervised clustering and epigenetic classification of single cells
title_fullStr Unsupervised clustering and epigenetic classification of single cells
title_full_unstemmed Unsupervised clustering and epigenetic classification of single cells
title_short Unsupervised clustering and epigenetic classification of single cells
title_sort unsupervised clustering and epigenetic classification of single cells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010417/
https://www.ncbi.nlm.nih.gov/pubmed/29925875
http://dx.doi.org/10.1038/s41467-018-04629-3
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