ArticleGenome biology2024
Benchmarking computational methods for single-cell chromatin data analysis.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- SpaDC enables sequence-based integrative analysis and regulatory inference of spatial chromatin accessibility data.Communications biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues.Genome biology · 2026Article
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- Advances in scCUT&Tag and computational analysis for single-cell gene regulatory element mapping.Briefings in bioinformatics · 2026Review
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- Review
- On metrics for subpopulation detection in single-cell and spatial omics data.Nucleic acids research · 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
- Targeted screening and single-cell analysis of genetic variants in melanoma using Mendelian randomization.Discover oncology · 2025Article
- dbscATAC: a resource of single-cell super-enhancers/enhancers and gene markers derived from scATAC-seq data.Bioinformatics (Oxford, England) · 2025Article
- Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas.bioRxiv : the preprint server for biology · 2025Article
- Enhancer Dynamics and Spatial Organization Drive Anatomically Restricted Cellular States in the Human Spinal Cord.bioRxiv : the preprint server for biology · 2025Article
- TGF-β-driven T-cell exclusion in ovarian cancer: single-cell and spatial transcriptomic views of immune low-response states.Frontiers in immunology · 2025Review
- Epigenetic control of antigen presentation failure in osteosarcoma: from single-cell chromatin maps to therapeutic strategies.Frontiers in immunology · 2025Review
- Going beyond cell clustering and feature aggregation: Is there single cell level information in single-cell ATAC-seq data?bioRxiv : the preprint server for biology · 2024Article
- Benchmarking computational methods for single-cell chromatin data analysis.Genome biology · 2024Article
- Cofea: correlation-based feature selection for single-cell chromatin accessibility data.Briefings in bioinformatics · 2023Article
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4 authors.
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Abstract
backgroundSingle-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices.
resultsWe benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection.
conclusionsOur analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.