无监督聚类和单细胞表观遗传分类。
Unsupervised clustering and epigenetic classification of single cells.
机构信息
Department of Statistics, Stanford University, Stanford, CA, 94305, USA.
Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
出版信息
Nat Commun. 2018 Jun 20;9(1):2410. 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.
在现代基因组学时代，描述细胞水平的表观遗传异质性是一个关键问题。单细胞 ATAC-seq（scATAC-seq）等检测方法为通过开放染色质的可变性模式来研究细胞水平的表观遗传异质性提供了机会。然而，这些检测方法存在技术变异性，这使得在异质群体中进行明确的分类和细胞类型鉴定变得复杂。我们提出了 scABC，这是一个用于单细胞表观遗传数据无监督聚类的 R 包，用于对 scATAC-seq 数据进行分类，并发现特定于细胞身份的开放染色质区域。
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