ArticleGenome biology2020
APEC: an accesson-based method for single-cell chromatin accessibility analysis.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Spatial Transcriptomics of Human Decidua Identifies Molecular Signatures in Recurrent Pregnancy Loss.Genomics, proteomics & bioinformatics · 2026Article
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Dynamics and regulation of mitotic chromatin accessibility bookmarking at single-cell resolution.Science advances · 2023Article
- Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA.Nature communications · 2022Article
- Review
- TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes.Cell reports · 2021Article
- Development of double-positive thymocytes at single-cell resolution.Genome medicine · 2021Article
- simATAC: a single-cell ATAC-seq simulation framework.Genome biology · 2021Article
- Profiling chromatin regulatory landscape: insights into the development of ChIP-seq and ATAC-seq.Molecular biomedicine · 2020Review
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Authors and funding
12 authors.
Funding
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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.
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Registered trials
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