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Unsupervised clustering and epigenetic classification of single cells

By Mahdi Zamanighomi, Zhixiang Lin, Timothy Daley, Xi Chen, Zhana Duren, Alicia Schep, William J Greenleaf, Wing Hung Wong

Posted 30 May 2017
bioRxiv DOI: 10.1101/143701 (published DOI: 10.1038/s41467-018-04629-3)

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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