ArticleNature communications2024
Best practices for differential accessibility analysis in single-cell epigenomics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis.bioRxiv : the preprint server for biology · 2026Article
- Systematic mapping of chromatin dysregulation driven by viral transcriptional regulators at scale.bioRxiv : the preprint server for biology · 2026Article
- BART-spatial unravels biologically significant transcriptional regulators from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease.Genome biology · 2026Article
- Multi-omics Data Integration.Advances in experimental medicine and biology · 2026Review
- Modulation of Peripheral Immune Cells Following Vitamin DNutrients · 2025Article
- Building a continuous benchmarking ecosystem in bioinformatics.PLoS computational biology · 2025Article
- A hierarchical, count-based model highlights challenges in scATAC-seq data analysis and points to opportunities to extract finer-resolution information.Genome biology · 2025Article
- annATAC: automatic cell type annotation for scATAC-seq data based on language model.BMC biology · 2025Article
- Capturing cell-type-specific activities of cis-regulatory elements from peak-based single-cell ATAC-seq.Cell genomics · 2025Article
- Bridging epigenomics and tumor immunometabolism: molecular mechanisms and therapeutic implications.Molecular cancer · 2025Review
- Reducing batch effects in single cell chromatin accessibility measurements by pooled transposition with MULTI-ATAC.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices.
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