Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data Article Swipe
Junlin Xu
,
Changcheng Lu
,
Shuting Jin
,
Yajie Meng
,
Xiangzheng Fu
,
Xiangxiang Zeng
,
Ruth Nussinov
,
Feixiong Cheng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/nar/gkaf138
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/nar/gkaf138
Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer’s disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/nar/gkaf138
- https://academic.oup.com/nar/article-pdf/53/5/gkaf138/62262865/gkaf138.pdf
- OA Status
- gold
- Cited By
- 17
- References
- 54
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408129839Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/nar/gkaf138Digital Object Identifier
- Title
-
Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-27Full publication date if available
- Authors
-
Junlin Xu, Changcheng Lu, Shuting Jin, Yajie Meng, Xiangzheng Fu, Xiangxiang Zeng, Ruth Nussinov, Feixiong ChengList of authors in order
- Landing page
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https://doi.org/10.1093/nar/gkaf138Publisher landing page
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https://academic.oup.com/nar/article-pdf/53/5/gkaf138/62262865/gkaf138.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/nar/article-pdf/53/5/gkaf138/62262865/gkaf138.pdfDirect OA link when available
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Biology, Gene, Computational biology, Gene regulatory network, Genetics, Cell, Gene expressionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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17Total citation count in OpenAlex
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2025: 17Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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