scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data
Open accessAbstract
BACKGROUND: Exploring the cellular processes of genes from the aspects of biological networks is of great interest to understanding the properties of complex diseases and biological systems. Biological networks, such as protein-protein interaction networks and gene regulatory networks, provide insights into the molecular basis of cellular processes and often form functional clusters in different tissue and disease contexts. RESULTS: We present scGraph2Vec, a deep learning framework for generating informative gene embeddings. scGraph2Vec extends the variational graph autoencoder framework and integrates single-cell datasets and gene-gene interaction networks. We demonstrate that the gene embeddings are biologically interpretable and enable the identification of gene clusters representing functional or tissue-specific cellular processes. By comparing similar tools, we showed that scGraph2Vec clearly distinguished different gene clusters and aggregated more biologically functional genes. scGraph2Vec can be widely applied in diverse biological contexts. We illustrated that the embeddings generated by scGraph2Vec can infer disease-associated genes from genome-wide association study data (e.g., COVID-19 and Alzheimer's disease), identify additional driver genes in lung adenocarcinoma, and reveal regulatory genes responsible for maintaining or transitioning melanoma cell states. CONCLUSIONS: scGraph2Vec not only reconstructs tissue-specific gene networks but also obtains a latent representation of genes implying their biological functions.
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scGraph2Vec uses graph neural networks to extract biologically meaningful gene embeddings from single-cell omics, elucidating tissue-specific regulatory mechanisms.
“Additionally, scGraph2Vec uses graph neural networks to extract biologically meaningful gene embeddings from single-cell omics, elucidating tissue-specific regulatory mechanisms265.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [265]
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Williams, J. B., Cao, Q., & Yan, Z. (2021) · Brain Communications · doi:10.1093/braincomms/fcab123 · Cited by 72 · see citing works
Modularity-aware graph autoencoders for joint community detection and link prediction
Salha-Galvan, G., Lutzeyer, J. F., Dasoulas, G., Hennequin, R., & Vazirgiannis, M. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.06.021 · Cited by 69 · see citing works
Neutrophilic airways inflammation in lung cancer: the role of exhaled LTB-4 and IL-8
Carpagnano, G. E., Palladino, G. P., Lacedonia, D., Koutelou, A., Orlando, S., & Barbaro, M. P. F. (2011) · BMC Cancer · doi:10.1186/1471-2407-11-226 · Cited by 61 · see citing works
CHCHD2 Is Coamplified with EGFR in NSCLC and Regulates Mitochondrial Function and Cell Migration
Wei, Y., Vellanki, R. N., Coyaud, É., Ignatchenko, V., Li, L., Krieger, J. R., Taylor, P. P., Tong, J., Pham, N., Liu, G., et al. (2015) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-14-0165-t · Cited by 55 · see citing works
siVAE: interpretable deep generative models for single-cell transcriptomes
Choi, Y., Li, R., & Quon, G. (2023) · Genome biology · doi:10.1186/s13059-023-02850-y · Cited by 48 · see citing works
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miR-1238 inhibits cell proliferation by targeting LHX2 in non-small cell lung cancer
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ABCE1 plays an essential role in lung cancer progression and metastasis
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Dirichlet Graph Variational AutoencoderPreprint
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High-Sulfated Glycosaminoglycans Prevent Coronavirus Replication
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Gaussian embedding for large-scale gene set analysis
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Gene Regulatory Network Inference through Link Prediction using Graph Neural Network
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Flagged first, then by citations
1 of the 102 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [104]Lin S, Jia P. scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data [Data set]. Zenodo. 2024. 10.5281/zenodo.12092871. Accessed June 18 2024. PMC11659981 39704704 · DOI
- [106]DOME registry: scGraph2Vec. https://registry.dome-ml.org/review/3c9pv95v5g. Accessed June 18 2024.
- [107]Lin S, Jia P. Supporting data for “scGraph2Vec: A Deep Generative Model for Gene Embedding Augmented by Graph Neural Network and Single-Cell Omics Data.” GigaScience Database. 2024. 10.5524/102624 PMC11659981 39704704 · DOI
Cited by
Sorted by citations
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
SCSEQ: A web tool for analyzing single-cell RNA-seq data
Du, S., Sun, P., Shen, L., & He, J. (2026) · GigaScience · doi:10.1093/gigascience/giag029 · Cited by 1 · see citing works
Ng, Y.-L., Wang, X., Li, Y., Huang, J., Lai, H. M., Chan, J. Y. K., NG, B. K. W., Ko, H., & Kwok, K. (2026) · GigaScience · doi:10.1093/gigascience/giag056
- Authors
- Lin, S., & Jia, P.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Jia, Peilin
- Venue
- GigaScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2024
- Volume
- 13
- ISSN
- 2047-217X (ISSN-L)
- PMC
- PMC11659981
- OpenAlex
- W4405624435
- Cited by
- 4
- References
- 102
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- GigaScience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (92374103, 32270706)
- Chinese Academy of Sciences (XDB38010400)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- gene embeddings, variational graph autoencoder, single-cell omics, gene interaction networks, gene regulatory networks, protein-protein interaction network, gene clustering, disease-associated genes, COVID-19 pandemic, Alzheimer's disease, biological networks
- MeSH terms
- Deep Learning, COVID-19, Algorithms, Alzheimer Disease, Humans, Lung Neoplasms, Neural Networks, Computer, Computational Biology, Gene Regulatory Networks, Genome-Wide Association Study, Single-Cell Analysis, Protein Interaction Maps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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