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APEC: an accesson-based method for single-cell chromatin accessibility analysis.

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.

SUBMITTER: Li B

PROVIDER: S-EPMC7218568 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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APEC: an accesson-based method for single-cell chromatin accessibility analysis.

Li Bin B Li Young Y Li Kun K Zhu Lianbang L Yu Qiaoni Q Cai Pengfei P Fang Jingwen J Zhang Wen W Du Pengcheng P Jiang Chen C Lin Jun J Qu Kun K

Genome biology 20200512 1

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-c ...[more]

PMID: 32398051

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