APEC: an accesson-based method for single-cell chromatin accessibility analysis
Bin Li, Young Li, Kun Li, Lianbang Zhu, Qiaoni Yu, Pengfei Cai, Jingwen Fang, Wen Zhang, Pengcheng Du, Chen Jiang, Jun Lin, Kun Qu
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.