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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. Here we propose ASTER, an ensemble learning-based tool for accurately estimating the number of cell types in scCAS data.