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..

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:21

Enthalten in:

Genome biology - 21(2020), 1 vom: 12. Mai

Sprache:

Englisch

Beteiligte Personen:

Li, Bin [VerfasserIn]
Li, Young [VerfasserIn]
Li, Kun [VerfasserIn]
Zhu, Lianbang [VerfasserIn]
Yu, Qiaoni [VerfasserIn]
Cai, Pengfei [VerfasserIn]
Fang, Jingwen [VerfasserIn]
Zhang, Wen [VerfasserIn]
Du, Pengcheng [VerfasserIn]
Jiang, Chen [VerfasserIn]
Lin, Jun [VerfasserIn]
Qu, Kun [VerfasserIn]

Links:

Volltext [kostenfrei]

BKL:

42.13 / Molekularbiologie / Molekularbiologie

42.20 / Genetik / Genetik

Themen:

Accesson
Cell clustering
Pseudotime trajectory
Regulome
ScATAC-seq

Anmerkungen:

© The Author(s) 2020

doi:

10.1186/s13059-020-02034-y

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2117624642