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 .

中文翻译：

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APEC：一种基于 accesson 的单细胞染色质可及性分析方法

测序技术的发展促进了单细胞分辨率下全基因组染色质可及性的调查。然而，对单细胞表观基因组谱的综合分析仍然是一个挑战。在这里，我们介绍了一种基于可访问性模式的表观基因组聚类 (APEC) 方法，该方法通过具有称为“访问子”的协同信号模式的可访问区域组对每个细胞进行分类。这个基于 python 的包极大地提高了许多公共数据集的无监督单细胞聚类的准确性。它还预测基因表达，识别丰富的基序，发现超级增强子，并预测伪时间轨迹。APEC 可在 https://github.com/QuKunLab/APEC 获得。