Unsupervised clustering and epigenetic classification of single cells.
Mahdi ZamanighomiZhixiang LinTimothy DaleyXi ChenZhana DurenAlicia SchepWilliam J GreenleafWing Hung WongPublished in: Nature communications (2018)
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.
Keyphrases
- single cell
- rna seq
- gene expression
- high throughput
- machine learning
- dna methylation
- genome wide
- big data
- minimally invasive
- deep learning
- electronic health record
- induced apoptosis
- dna damage
- artificial intelligence
- stem cells
- cell cycle arrest
- oxidative stress
- endoplasmic reticulum stress
- cell proliferation
- signaling pathway