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Graph neural network approaches for single-cell data: a recent overview

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Abstract

Graph neural networks (GNNs) are reshaping our understanding of biomedicine and diseases by revealing the deep connections among genes and cells. As both algorithmic and biomedical technologies have advanced significantly, we are entering a transformative phase of personalized medicine. While pioneering tools like graph attention networks (GATs) and graph convolutional neural networks (Graph CNN) are advancing graph-based learning, the rise of single-cell sequencing techniques is reshaping our insights on cellular diversity and function. Numerous studies have combined GNNs with single-cell data, showing promising results. In this work, we highlight the GNN methodologies tailored for single-cell data over the recent years. We outline the diverse range of graph deep learning architectures that center on GAT methodologies. Furthermore, we underscore the several objectives of GNN strategies in single-cell data contexts, ranging from cell-type annotation, data integration and imputation, gene regulatory network reconstruction, clustering and many others. This review anticipates a future where GNNs become central to single-cell analysis efforts, particularly as vast omics datasets are continuously generated and the interconnectedness of cells and genes enhances our depth of knowledge in biomedicine.

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References

  1. Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequencing: new developments and medical applications. Cell Biosci 9:1–9

    Article  Google Scholar 

  2. Rao A, Barkley D, França GS, Yanai I (2021) Exploring tissue architecture using spatial transcriptomics. Nature 596(7871):211–220

    Article  Google Scholar 

  3. Wang R-S, Maron BA, Loscalzo J (2023) Multiomics network medicine approaches to precision medicine and therapeutics in cardiovascular diseases. Arterioscler Thromb Vasc Biol 43(4):493–503

    Article  Google Scholar 

  4. Blencowe M, Arneson D, Ding J, Chen Y-W, Saleem Z, Yang X (2019) Network modeling of single-cell omics data: challenges, opportunities, and progresses. Emerg Topics Life Sci 3(4):379–398

    Article  Google Scholar 

  5. Wu, L., Cui, P., Pei, J., Zhao, L., Song, L.: (2022) Graph neural networks. Springer, Singapore. pp. 27–37. https://doi.org/10.1007/978-981-16-6054-2_3

  6. Georgousis S, Kenning MP, Xie X (2021) Graph deep learning: state of the art and challenges. IEEE Access 9:22106–22140

    Article  Google Scholar 

  7. Ruiz L, Gama F, Ribeiro A (2020) Gated graph recurrent neural networks. IEEE Trans Signal Process 68:6303–6318

    Article  MathSciNet  Google Scholar 

  8. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  9. Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)

  10. Mingshuo, N., Dongming, C., Dongqi, W.: Reinforcement learning on graph: A survey. arXiv e-prints, 2204 (2022)

  11. Chen, L., Li, J., Peng, J., Xie, T., Cao, Z., Xu, K., He, X., Zheng, Z., Wu, B.: A survey of adversarial learning on graphs. arXiv preprint arXiv:2003.05730 (2020)

  12. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)

  13. Labonne, M.: Hands-On Graph Neural Networks Using Python. Packt, ??? (2023)

  14. Wang J, Ma A, Chang Y, Gong J, Jiang Y, Qi R, Wang C, Fu H, Ma Q, Xu D (2021) scgnn is a novel graph neural network framework for single-cell rna-seq analyses. Nature commun 12(1):1882

    Article  Google Scholar 

  15. Shao X, Yang H, Zhuang X, Liao J, Yang P, Cheng J, Lu X, Chen H, Fan X (2021) scdeepsort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic acids research 49(21):122–122

    Article  Google Scholar 

  16. Hou W, Ji Z, Ji H, Hicks SC (2020) A systematic evaluation of single-cell rna-sequencing imputation methods. Genome biology 21:1–30

    Article  Google Scholar 

  17. Rao, J., Zhou, X., Lu, Y., Zhao, H., Yang, Y.: Imputing single-cell rna-seq data by combining graph convolution and autoencoder neural networks. iscience (2021)

  18. Feng X, Fang F, Long H, Zeng R, Yao Y (2022) Single-cell rna-seq data analysis using graph autoencoders and graph attention networks. Fronti Genet 13:1003711

    Article  Google Scholar 

  19. Xu C, Cai L, Gao J (2021) An efficient scrna-seq dropout imputation method using graph attention network. BMC Bioinform 22:1–18

    Article  Google Scholar 

  20. Feng X, Zhang H, Lin H, Long H (2023) Single-cell rna-seq data analysis based on directed graph neural network. Methods 211:48–60

    Article  Google Scholar 

  21. Gu H, Cheng H, Ma A, Li Y, Wang J, Xu D, Ma Q (2022) scgnn 20: a graph neural network tool for imputation and clustering of single-cell rna-seq data. Bioinformatics 38(23):5322–5325

    Article  Google Scholar 

  22. Wu X, Zhou Y (2022) Ge-impute: graph embedding-based imputation for single-cell rna-seq data. Brief Bioinform 23(5):313

    Article  Google Scholar 

  23. Chen G, Liu Z-P (2022) Graph attention network for link prediction of gene regulations from single-cell rna-sequencing data. Bioinformatics 38(19):4522–4529

    Article  Google Scholar 

  24. Buterez D, Bica I, Tariq I, Andrés-Terré H, Liò P (2022) Cellvgae: an unsupervised scrna-seq analysis workflow with graph attention networks. Bioinformatics 38(5):1277–1286

    Article  Google Scholar 

  25. Baul S, Ahmed KT, Filipek J, Zhang W (2022) omicsgat: graph attention network for cancer subtype analyses. Int J Mol Sci 23(18):10220

    Article  Google Scholar 

  26. Cheng Y, Ma X (2022) scgac: a graph attentional architecture for clustering single-cell rna-seq data. Bioinformatics 38(8):2187–2193

    Article  Google Scholar 

  27. Huo Y, Guo Y, Wang J, Xue H, Feng Y, Chen W, Li X (2023) Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network. J Genet Genom 50(9):720

    Article  Google Scholar 

  28. Dong K, Zhang S (2022) Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nature Commun 13(1):1739

    Article  Google Scholar 

  29. Abadi SAR, Laghaee SP, Koohi S (2023) An optimized graph-based structure for single-cell rna-seq cell-type classification based on non-linear dimension reduction. BMC Genom 24(1):1–13

    Article  Google Scholar 

  30. Luo Z, Xu C, Zhang Z, Jin W (2021) A topology-preserving dimensionality reduction method for single-cell rna-seq data using graph autoencoder. Sci Rep 11(1):20028

    Article  Google Scholar 

  31. Wang S, Zhang Y, Zhang Y, Wu W, Ye L, Li Y, Su J, Pang S (2023) scasgc: an adaptive simplified graph convolution model for clustering single-cell rna-seq data. Comput Biol Med 163:107152

    Article  Google Scholar 

  32. Zhao J, Wang N, Wang H, Zheng C, Su Y (2021) Scdrha: a scrna-seq data dimensionality reduction algorithm based on hierarchical autoencoder. Front Genet 12:733906

    Article  Google Scholar 

  33. So, E., Hayat, S., Kadambat Nair, S., Wang, B., Haibe-Kains, B.: (2023) Graphcomm: a graph-based deep learning method to predict cell-cell communication in single-cell rnaseq data. bioRxiv, 2023–04

  34. Wang K, Li Z, You Z-H, Han P, Nie R (2023) Adversarial dense graph convolutional networks for single-cell classification. Bioinformatics 39(2):043

    Article  Google Scholar 

  35. Shahir, J.A., Stanley, N., Purvis, J.E.: (2023) Cellograph: a semi-supervised approach to analyzing multi-condition single-cell rna-sequencing data using graph neural networks. bioRxiv, 2023–02

  36. Ma A, Wang X, Li J, Wang C, Xiao T, Liu Y, Cheng H, Wang J, Li Y, Chang Y et al (2023) Single-cell biological network inference using a heterogeneous graph transformer. Nature Commun 14(1):964

    Article  Google Scholar 

  37. Wang G, Zhao J, Yan Y, Wang Y, Wu AR, Yang C: (2023) Construction of a 3d whole organism spatial atlas by joint modeling of multiple slices. bioRxiv, 2023–02

  38. Tang Z, Zhang T, Yang B, Su J, Song Q (2023) spaci: deciphering spatial cellular communications through adaptive graph model. Brief Bioinform 24(1):563

    Article  Google Scholar 

  39. Dai X, Xu F, Wang S, Mundra PA, Zheng J (2021) Pike-r2p: Protein-protein interaction network-based knowledge embedding with graph neural network for single-cell rna to protein prediction. BMC Bioinform 22(6):1–16

    Google Scholar 

  40. Shan Y, Yang J, Li X, Zhong X, Chang Y (2023) Glae: A graph-learnable auto-encoder for single-cell rna-seq analysis. Inform Sci 621:88–103

    Article  Google Scholar 

  41. Yu Z, Lu Y, Wang Y, Tang F, Wong K-C, Li X (2022) Zinb-based graph embedding autoencoder for single-cell rna-seq interpretations. Proc AAAI Conf Artif Intell 36:4671–4679

    Google Scholar 

  42. Shao X, Yang H, Zhuang X, Liao J, Yang Y, Yang P, Cheng J, Lu X, Chen H, Fan X: (2020) Reference-free cell-type annotation for single-cell transcriptomics using deep learning with a weighted graph neural network. BioRxiv 2020–05

  43. Lee J, Kim S, Hyun D, Lee N, Kim Y, Park C (2023) Deep single-cell rna-seq data clustering with graph prototypical contrastive learning. Bioinformatics 39(6):342

    Article  Google Scholar 

  44. Alghamdi N, Chang W, Dang P, Lu X, Wan C, Gampala S, Huang Z, Wang J, Ma Q, Zang Y et al (2021) A graph neural network model to estimate cell-wise metabolic flux using single-cell rna-seq data. Genome Res 31(10):1867–1884

    Article  Google Scholar 

  45. Lin E, Liu B, Lac L, Fung D, Leung C, Hu P: (2023) scgmm-vgae: a gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell rna-seq data. Mach Learn Sci Technol

  46. Bhadani R, Chen Z, An L (2023) Attention-based graph neural network for label propagation in single-cell omics. Genes 14(2):506

    Article  Google Scholar 

  47. Yuan M, Chen L, Deng M (2022) scmra: a robust deep learning method to annotate scrna-seq data with multiple reference datasets. Bioinformatics 38(3):738–745

    Article  Google Scholar 

  48. Yin Q, Liu Q, Fu Z, Zeng W, Zhang B, Zhang X, Jiang R, Lv H (2022) scgraph: a graph neural network-based approach to automatically identify cell types. Bioinformatics 38(11):2996–3003

    Article  Google Scholar 

  49. Cao Z-J, Gao G (2022) Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nature Biotechnol 40(10):1458–1466

    Article  Google Scholar 

  50. Li H, Sun Y, Hong H, Huang X, Tao H, Huang Q, Wang L, Xu K, Gan J, Chen H et al (2022) Inferring transcription factor regulatory networks from single-cell atac-seq data based on graph neural networks. Nature Mach Intell 4(4):389–400

    Article  Google Scholar 

  51. Song Q, Su J, Zhang W (2021) scgcn is a graph convolutional networks algorithm for knowledge transfer in single cell omics. Nature Commun 12(1):3826

    Article  Google Scholar 

  52. Liu Y, Zhang J, Wang S, Zhang W, Zeng X, Kwoh CK (2022) A heterogeneous graph cross-omics attention model for single-cell representation learning. In: 2022 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 270–275. IEEE

  53. Wu AP-Y, Singh R, Walsh CA, Berger B (2023) An econometric lens resolves cell-state parallax. bioRxiv, 2023

  54. Yuan Y, Bar-Joseph Z (2020) Gcng: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol 21(1):1–16

    Article  Google Scholar 

  55. Li Y, Luo Y (2023) Spatial transcriptomic cell-type deconvolution using graph neural networks. bioRxiv

  56. Cang Z, Ning X, Nie A, Xu M, Zhang J (2021) Scan-it: domain segmentation of spatial transcriptomics images by graph neural network. In: BMVC: proceedings of the british machine vision conference. british machine vision conference. vol 32. NIH Public Access

  57. Kiselev VY, Andrews TS, Hemberg M (2019) Challenges in unsupervised clustering of single-cell rna-seq data. Nature Rev Genet 20(5):273–282

    Article  Google Scholar 

  58. Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F et al (2023) Best practices for single-cell analysis across modalities. Nature Rev Genet 24(8):550

    Article  Google Scholar 

  59. Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11):104

    Google Scholar 

  60. McInnes L, Healy J, Melville J (2018) Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426

  61. Lopez R, Regier J, Cole MB, Jordan MI, Yosef N (2018) Deep generative modeling for single-cell transcriptomics. Nature Methods 15(12):1053–1058

    Article  Google Scholar 

  62. Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P (2018) A general and flexible method for signal extraction from single-cell rna-seq data. Nature Commun 9(1):284

    Article  Google Scholar 

  63. Clarke ZA, Andrews TS, Atif J, Pouyabahar D, Innes BT, MacParland SA, Bader GD (2021) Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nature Protoc 16(6):2749–2764

    Article  Google Scholar 

  64. Aalto A, Viitasaari L, Ilmonen P, Mombaerts L, Gonçalves J (2020) Gene regulatory network inference from sparsely sampled noisy data. Nature Commun 11(1):3493

    Article  Google Scholar 

  65. Badia-i-Mompel P, Wessels L, Müller-Dott S, Trimbour R, Ramirez Flores RO, Argelaguet R, Saez-Rodriguez J (2023) Gene regulatory network inference in the era of single-cell multi-omics. Nature Rev Genet 1–16

  66. Liu Z, Sun D, Wang C (2022) Evaluation of cell-cell interaction methods by integrating single-cell rna sequencing data with spatial information. Genome Biol 23(1):1–38

    Article  Google Scholar 

  67. Xie Z, Li X, Mora A (2023) A comparison of cell-cell interaction prediction tools based on scrna-seq data. Biomolecules 13(8):1211

    Article  Google Scholar 

  68. Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali T (2020) Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods 17(2):147–154

    Article  Google Scholar 

  69. Tarashansky AJ, Xue Y, Li P, Quake SR, Wang B (2019) Self-assembling manifolds in single-cell rna sequencing data. Elife 8:48994

    Article  Google Scholar 

  70. Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J et al (2017) Massively parallel digital transcriptional profiling of single cells. Nature Commun 8(1):14049

    Article  Google Scholar 

  71. Guo X, Zhang Y, Zheng L, Zheng C, Song J, Zhang Q, Kang B, Liu Z, Jin L, Xing R et al (2018) Global characterization of t cells in non-small-cell lung cancer by single-cell sequencing. Nature Med 24(7):978–985

    Article  Google Scholar 

  72. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5):1187–1201

    Article  Google Scholar 

  73. Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al (2016) A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure. Cell Syst 3(4):346–360

    Article  Google Scholar 

  74. Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, Chen H, Wang J, Tang H, Ge W et al (2020) Construction of a human cell landscape at single-cell level. Nature 581(7808):303–309

    Article  Google Scholar 

  75. Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, Saadatpour A, Zhou Z, Chen H, Ye F et al (2018) Mapping the mouse cell atlas by microwell-seq. Cell 172(5):1091–1107

    Article  Google Scholar 

  76. Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams SR, Catallini JL, Tran MN, Besich Z, Tippani M et al (2021) Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature Neurosci 24(3):425–436

    Article  Google Scholar 

  77. Sunkin SM, Ng L, Lau C, Dolbeare T, Gilbert TL, Thompson CL, Hawrylycz M, Dang C (2012) Allen brain atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res 41(D1):996–1008

    Article  Google Scholar 

  78. Stickels RR, Murray E, Kumar P, Li J, Marshall JL, Di Bella DJ, Arlotta P, Macosko EZ, Chen F (2021) Highly sensitive spatial transcriptomics at near-cellular resolution with slide-seqv2. Nature Biotechnol 39(3):313–319

    Article  Google Scholar 

  79. He S, Bhatt R, Brown C, Brown EA, Buhr DL, Chantranuvatana K, Danaher P, Dunaway D, Garrison RG, Geiss G et al (2022) High-plex imaging of rna and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nature Biotechnol 40(12):1794–1806

    Article  Google Scholar 

  80. Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N, Vesuna S, Evans K, Liu C, Ramakrishnan C, Liu J et al (2018) Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361(6400):5691

    Article  Google Scholar 

  81. Moffitt JR, Bambah-Mukku D, Eichhorn SW, Vaughn E, Shekhar K, Perez JD, Rubinstein ND, Hao J, Regev A, Dulac C et al (2018) Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362(6416):5324

    Article  Google Scholar 

  82. 13 B.W.H..H.M.S.C.L...P.P.J..K.R., Medicine Creighton Chad J. 22 23 Donehower Lawrence A. 22 23 24 25, G., Systems Biology Reynolds Sheila 31 Kreisberg Richard B. 31 Bernard Brady 31 Bressler Ryan 31 Erkkila Timo 32 Lin Jake 31 Thorsson Vesteinn 31 Zhang Wei 33 Shmulevich Ilya 31, I., et al.: Comprehensive molecular portraits of human breast tumours. Nature 490(7418), 61–70 (2012)

  83. Network CGAR et al (2014) Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507(7492):315

    Article  Google Scholar 

  84. Luecken MD, Burkhardt DB, Cannoodt R, Lance C, Agrawal A, Aliee H, Chen AT, Deconinck L, Detweiler AM, Granados AA et al (2021) A sandbox for prediction and integration of dna, rna, and proteins in single cells. In: 35th conference on neural information processing systems (NeurIPS 2021) track on datasets and benchmarks

  85. Buus TB, Herrera A, Ivanova E, Mimitou E, Cheng A, Herati RS, Papagiannakopoulos T, Smibert P, Odum N, Koralov SB (2021) Improving oligo-conjugated antibody signal in multimodal single-cell analysis. Elife 10:61973

    Article  Google Scholar 

  86. Cao J, O’Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al (2020) A human cell atlas of fetal gene expression. Science 370(6518):7721

    Article  Google Scholar 

  87. Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Information Fusion 42:146–157

    Article  Google Scholar 

  88. Longo SK, Guo MG, Ji AL, Khavari PA (2021) Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nature Rev Genet 22(10):627–644

    Article  Google Scholar 

  89. Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? arXiv preprint arXiv:2105.14491 (2021)

  90. Joshi-Tope, G., Gillespie, M., Vastrik, I., D’Eustachio, P., Schmidt, E., Bono, B., Jassal, B., Gopinath, G., Wu, G., Matthews, L., et al.: Reactome: a knowledgebase of biological pathways. Nucleic acids research 33(suppl_1), 428–432 (2005)

  91. Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J (2019) Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res 29(8):1363–1375

    Article  Google Scholar 

  92. Han H, Cho J-W, Lee S, Yun A, Kim H, Bae D, Yang S, Kim CY, Lee M, Kim E et al (2018) Trrust v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 46(D1):380–386

    Article  Google Scholar 

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  1. Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100, Corfu, Greece

    Konstantinos Lazaros, Dimitris E. Koumadorakis, Panagiotis Vlamos & Aristidis G. Vrahatis

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  1. Konstantinos Lazaros
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Lazaros, K., Koumadorakis, D.E., Vlamos, P. et al. Graph neural network approaches for single-cell data: a recent overview. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09662-6

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