Computational Analyses and Challenges of Single-cell ATAC-seq

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) has emerged as a powerful technique to study cell-specific epigenetic landscapes and to provide a multidimensional portrait of gene regulation. However, low genomic coverage per cell results in intrinsic data sparsity and missing-data issues, presenting unique methodological challenges. Consequently, numerous computational methods and techniques have been developed to address these challenges. This review provides a concise overview of published workflows for scATAC-seq analysis, covering preprocessing through downstream analysis including quality control, alignment, peak calling, dimensionality reduction, clustering, gene regulation score calculation, cell type annotation, and multiomics integration. Additionally, we survey key scATAC-seq databases that offer curated, accessible resources; discuss emerging deep-learning methods and Artificial Intelligence (AI) foundation models tailored to scATAC-seq data; and highlight recent advances in spatial ATAC-seq technologies and associated computational approaches. Our objective is to equip readers with a clear understanding of current scATAC-seq methodologies so they can select appropriate tools and construct customized workflows for exploring gene regulation and cellular diversity.