ArticleNature communications2018
Unsupervised clustering and epigenetic classification of single cells.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 2026Article
- Clustering Single-Cell RNA-Seq Data with Low-Rank Matrix Factorization and Local Graph Regularization.Interdisciplinary sciences, computational life sciences · 2026Article
- Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN.Genome research · 2026Article
- Metformin Restores Mitochondrial Function and Neurogenesis in POLG Patient-Derived Brain Organoids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic information in a deep diffusive transformer.Briefings in bioinformatics · 2025Article
- scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data.Briefings in bioinformatics · 2025Article
- Chromatin accessibility provides a window into the genetic etiology of human brain disease.Trends in genetics : TIG · 2025Review
- Mixed-Supervised Learning for Cell Classification.Sensors (Basel, Switzerland) · 2025Article
- Topological identification and interpretation for single-cell epigenetic regulation elucidation in multi-tasks using scAGDE.Nature communications · 2025Article
- INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation.Nature communications · 2025Article
- Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases.Briefings in functional genomics · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Statistical relationships across epigenomes using large-scale hierarchical clustering.Bioinformatics advances · 2025Article
- Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations.Genome biology · 2024Article
- Best practices for differential accessibility analysis in single-cell epigenomics.Nature communications · 2024Article
- Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data.Briefings in bioinformatics · 2024Article
- Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.Genomics, proteomics & bioinformatics · 2024Article
- Hallmark Molecular and Pathological Features of POLG Disease are Recapitulated in Cerebral Organoids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.