Unsupervised clustering and epigenetic classification of single cells.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 29925875.
- Also identified by DOI 10.1038/s41467-018-04629-3 and PMC identifier 6010417.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Epigenomics
- Models, Statistical
- Single-Cell Analysis