Unsupervised clustering and epigenetic classification of single cells

Mahdi Zamanighomi, Zhixiang Lin, Timothy Daley, Xi Chen, Zhana Duren, Alicia Schep, William J. Greenleaf and Wing Hung Wong ()
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Mahdi Zamanighomi: Stanford University
Zhixiang Lin: Stanford University
Timothy Daley: Stanford University
Xi Chen: Stanford University
Zhana Duren: Stanford University
Alicia Schep: Stanford University School of Medicine
William J. Greenleaf: Stanford University School of Medicine
Wing Hung Wong: Stanford University

Nature Communications, 2018, vol. 9, issue 1, 1-8

Abstract: Abstract 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.

Date: 2018
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DOI: 10.1038/s41467-018-04629-3

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