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Unsupervised clustering and epigenetic classification of single cells.

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.

SUBMITTER: Zamanighomi M

PROVIDER: S-EPMC6010417 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Unsupervised clustering and epigenetic classification of single cells.

Zamanighomi Mahdi M Lin Zhixiang Z Daley Timothy T Chen Xi X Duren Zhana Z Schep Alicia A Greenleaf William J WJ Wong Wing Hung WH

Nature communications 20180620 1

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 ...[more]

PMID: 29925875

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