ArticleBioinformatics (Oxford, England)2023
ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- A sequence knowledge-guided deep learning method for single-cell multi-omics translation.Genome biology · 2026Article
- CEMUSA: a graph-based integrative metric for evaluating clusters in spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders.Nature communications · 2024Article
- EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data.Bioinformatics (Oxford, England) · 2024Article
- Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance.Quantitative biology (Beijing, China) · 2024Article
- scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data.Nature communications · 2024Article
- scIBD: a self-supervised iterative-optimizing model for boosting the detection of heterotypic doublets in single-cell chromatin accessibility data.Genome biology · 2023Article
- SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains.Cells · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
summaryRecent innovations in single-cell chromatin accessibility sequencing (scCAS) have revolutionized the characterization of epigenomic heterogeneity. Estimation of the number of cell types is a crucial step for downstream analyses and biological implications. However, efforts to perform estimation specifically for scCAS data are limited. Here, we propose ASTER, an ensemble learning-based tool for accurately estimating the number of cell types in scCAS data. ASTER outperformed baseline methods in systematic evaluation on 27 datasets of various protocols, sizes, numbers of cell types, degrees of cell-type imbalance, cell states and qualities, providing valuable guidance for scCAS data analysis. AVAILABILITY AND IMPLEMENTATION: ASTER along with detailed documentation is freely accessible at https://aster.readthedocs.io/ under the MIT License. It can be seamlessly integrated into existing scCAS analysis workflows. The source code is available at https://github.com/biox-nku/aster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.