Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, 230001, Anhui, China.
HanGene Biotech, Xiaoshan Innovation Polis, Hangzhou, 310000, Zhejiang, China.
Genome Biol. 2020 May 12;21(1):116. doi: 10.1186/s13059-020-02034-y.
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.
测序技术的发展推动了在单细胞分辨率下对全基因组染色质可及性的研究。然而，全面分析单细胞表观基因组图谱仍然是一个挑战。在这里，我们介绍了一种基于可及性模式的表观基因组聚类（APEC）方法，该方法通过协同信号模式的可及区域组对每个细胞进行分类，这些模式被称为“accessons”。这个基于 Python 的软件包大大提高了许多公共数据集的无监督单细胞聚类的准确性。它还可以预测基因表达，识别富集的基序，发现超级增强子，并投射伪时间轨迹。APEC 可在 https://github.com/QuKunLab/APEC 上获取。
