scBasset: Sequence-based modeling of single cell ATAC-seq using convolutional neural networks
Open accessPreprint — published as 10.1038/s41592-022-01562-8
Abstract
1 Single cell ATAC-seq (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain significant challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single cell multiome datasets, including cell type identification, scATAC profile denoising, data integration across assays, and transcription factor activity inference.
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Convolutional neural networks can model, predict, and interpret enhancer accessibility or activity from sequence.
“we and others have recently shown the power of convolutional neural networks (CNN) to model, predict, and interpret an enhancer accessibility or activity from its sequence12,19–21.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [21]
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Zheng, Y., Schupp, J., Adams, T., Clair, G., Justet, A., Ahangari, F., Yan, X., Hansen, P., Carlon, M., Cortesi, E., et al. (2023) · doi:10.21203/rs.3.rs-3676579/v1 · Cited by 34 · see citing works
Enhancer grammar of liver cell types and hepatocyte zonation states
González-Blas, C. B., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2022) · doi:10.1101/2022.12.08.519575 · Cited by 5 · see citing works
- Authors
- Yuan, H., & Kelley, D. R.
- Affiliations
- Calico Life Sciences (US)
- Corresponding author
- Yuan, Han
- Kelley, David R
- Venue
- Nature Methods
- Publisher
- openRxiv
- Published
- 2021-09-10
- Volume
- 19
- Issue
- 9
- Pages
- 1088-1096
- ISSN
- 1548-7091 (ISSN-L)
- OpenAlex
- W4290672942
- Cited by
- 138
- References
- 0
- Citations by year
- 2026: 25 · 2025: 34 · 2024: 45 · 2023: 27 · 2022: 6
- FWCI
- 9.39
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- single-cell ATAC-seq, chromatin accessibility, convolutional neural networks, cell clustering, multi-omics integration, transcription factor activity inference, epigenetic heterogeneity
- MeSH terms
- Chromatin Immunoprecipitation Sequencing, Chromatin, Neural Networks, Computer, Sequence Analysis, DNA, Transposases, Epigenomics, Single-Cell Analysis
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