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scTCA: a hybrid Transformer-CNN architecture for imputation and denoising of scDNA-seq data
Open accessAbstract
Single-cell DNA sequencing (scDNA-seq) has been widely used to unmask tumor copy number alterations (CNAs) at single-cell resolution. Despite that arm-level CNAs can be accurately detected from single-cell read counts, it is difficult to precisely identify focal CNAs as the read counts are featured with high dimensionality, high sparsity and low signal-to-noise ratio. This gives rise to a desperate demand for reconstructing high-quality scDNA-seq data. We develop a new method called scTCA for imputation and denoising of single-cell read counts, thus aiding in downstream analysis of both arm-level and focal CNAs. scTCA employs hybrid Transformer-CNN architectures to identify local and non-local correlations between genes for precise recovery of the read counts. Unlike conventional Transformers, the Transformer block in scTCA is a two-stage attention module containing a stepwise self-attention layer and a window Transformer, and can efficiently deal with the high-dimensional read counts data. We showcase the superior performance of scTCA through comparison with the state-of-the-arts on both synthetic and real datasets. The results indicate it is highly effective in imputation and denoising of scDNA-seq data.
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Sorted by citations
Li, X., He, Y., Wu, J., Wang, Z., Zhao, X., Zhang, Y., Pan, B., Tang, Y., & Han, J. (2026) · PubMed · doi:10.1126/sciadv.aed6373 · Cited by 1
“…for genomic sequence analysis, demonstrating superior computational performance in modeling long-range dependencies within biological sequences and interpathway regulatory networks, representing the current state of the art in deep learning applications for precision immunotherapy ( 32 , 33 ).”
Metadata
- Authors
- Yu, Z., Liu, F., & Li, Y.
- Affiliations
- Ningxia University (CN)
- Corresponding author
- Yu, Zhenhua
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-09-23
- Volume
- 25
- Issue
- 6
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC11551055
- OpenAlex
- W4404219624
- Cited by
- 1
- References
- 2
- Citations by year
- 2026: 1
- FWCI
- 0.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Natural Science Foundation of Ningxia Province (2023AAC05006)
- Key Research and Development Program of Ningxia (2023BEG02009)
- 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 Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell DNA sequencing, focal copy number alterations, arm-level copy number alterations, hybrid transformer-CNN architecture, self-attention
- MeSH terms
- Algorithms, Humans, Software, Neural Networks, Computer, Sequence Analysis, DNA, DNA Copy Number Variations, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Signal-To-Noise Ratio
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