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Updated: Aug 15, 2025
Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq
Published on: November 13, 2017
ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data
Shengquan Chen1, Rongxiang Wang2, Wenxin Long1
1School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.
Summary:
Recent 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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