| Literature DB >> 32398051 |
Bin Li1, Young Li1, Kun Li1, Lianbang Zhu1, Qiaoni Yu1, Pengfei Cai1, Jingwen Fang1,2, Wen Zhang1, Pengcheng Du1, Chen Jiang1, Jun Lin1, Kun Qu3,4.
Abstract
The development of sequencing technologies has promoted the survey of genome-wide chromatin accessibility at single-cell resolution. However, comprehensive analysis of single-cell epigenomic profiles remains a challenge. Here, we introduce an accessibility pattern-based epigenomic clustering (APEC) method, which classifies each cell by groups of accessible regions with synergistic signal patterns termed "accessons". This python-based package greatly improves the accuracy of unsupervised single-cell clustering for many public datasets. It also predicts gene expression, identifies enriched motifs, discovers super-enhancers, and projects pseudotime trajectories. APEC is available at https://github.com/QuKunLab/APEC.Entities:
Keywords: Accesson; Cell clustering; Pseudotime trajectory; Regulome; scATAC-seq
Mesh:
Year: 2020 PMID: 32398051 PMCID: PMC7218568 DOI: 10.1186/s13059-020-02034-y
Source DB: PubMed Journal: Genome Biol ISSN: 1474-7596 Impact factor: 13.583