Single-cell ATAC-seq is a powerful approach for examining cellular epigenetic variation and gene regulation mechanisms. However, low genomic coverage per cell creates inherent data sparsity and missing-value challenges that require specialized computational solutions. This review synthesizes published analytical workflows spanning preprocessing through downstream analysis, including quality control, alignment, peak identification, dimensionality reduction, clustering, regulatory scoring, cell classification, and data integration. The authors additionally survey key databases, emerging deep-learning techniques, AI foundation models designed for this modality, and recent spatial ATAC-seq advances with corresponding computational tools.