scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks - PubMed
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. 2022 Sep;19(9):1088-1096.
doi: 10.1038/s41592-022-01562-8.
Epub 2022 Aug 8.
scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks
Han Yuan 
1
, David R Kelley 
2
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1 Calico Life Sciences, South San Francisco, CA, USA. yuanh@calicolabs.com.
2 Calico Life Sciences, South San Francisco, CA, USA. drk@calicolabs.com.
PMID:
35941239
DOI:
10.1038/s41592-022-01562-8
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scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks
Han Yuan et al.
Nat Methods.
2022 Sep.
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. 2022 Sep;19(9):1088-1096.
doi: 10.1038/s41592-022-01562-8.
Epub 2022 Aug 8.
Authors
Han Yuan 
1
, David R Kelley 
2
Affiliations
1 Calico Life Sciences, South San Francisco, CA, USA. yuanh@calicolabs.com.
2 Calico Life Sciences, South San Francisco, CA, USA. drk@calicolabs.com.
PMID:
35941239
DOI:
10.1038/s41592-022-01562-8
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Author Correction: scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks.
Yuan H, Kelley DR.
Yuan H, et al.
Nat Methods. 2023 Jan;20(1):162. doi: 10.1038/s41592-022-01754-2.
Nat Methods. 2023.
PMID: 36564580
No abstract available.
Abstract
Single-cell assay for transposase-accessible chromatin using sequencing (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain important 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 clustering, scATAC profile denoising, data integration across assays and transcription factor activity inference.
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.
PubMed Disclaimer
Comment in
How regulatory sequences learn cell representations.
Aerts S.
Aerts S.
Nat Methods. 2022 Sep;19(9):1041-1043. doi: 10.1038/s41592-022-01570-8.
Nat Methods. 2022.
PMID: 35941240
No abstract available.
References
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
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DOI
Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).
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DOI
Miao, Z. et al. Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and renal disease targets. Nat. Commun. 12, 2277 (2021).
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DOI
Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018).
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DOI
Bravo González-Blas, C. et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. Methods 16, 397–400 (2019).
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DOI
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Chromatin Immunoprecipitation Sequencing*
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Epigenomics
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Neural Networks, Computer
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Sequence Analysis, DNA / methods
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