Benchmarking computational methods for single-cell chromatin data analysis
Our 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.
Authors
- Ferdinand von Meyenn (ORCID: https://orcid.org/0000-0001-9920-3075)
- Siyuan Luo (ORCID: https://orcid.org/0009-0007-6404-3244)
- Pierre‐Luc Germain (ORCID: https://orcid.org/0000-0003-3418-4218)
- Mark D. Robinson
Institutions
- SIB Swiss Institute of Bioinformatics (CH)
- University of Zurich (CH)
- ETH Zurich (CH)
- Life Science Zurich (CH)
Publication Details
- Journal
- Genome biology
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1186/s13059-024-03356-x
- Citations
- 15
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 3.51
Funders
- Eidgenössische Technische Hochschule Zürich
- Universität Zürich