Skip to main content
SpringerLink
Log in

Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs

  • Original research article
  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

Since gene regulation is a complex process in which multiple genes act simultaneously, accurately inferring gene regulatory networks (GRNs) is a long-standing challenge in systems biology. Although graph neural networks can formally describe intricate gene expression mechanisms, current GRN inference methods based on graph learning regard only transcription factor (TF)–target gene interactions as pairwise relationships, and cannot model the many-to-many high-order regulatory patterns that prevail among genes. Moreover, these methods often rely on limited prior regulatory knowledge, ignoring the structural information of GRNs in gene expression profiles. Therefore, we propose a multi-view hierarchical hypergraphs GRN (MHHGRN) inference model. Specifically, multiple heterogeneous biological information is integrated to construct multi-view hierarchical hypergraphs of TFs and target genes, using hypergraph convolution networks to model higher order complex regulatory relationships. Meanwhile, the coupled information diffusion mechanism and the cross-domain messaging mechanism facilitate the information sharing between genes to optimise gene embedding representations. Finally, a unique channel attention mechanism is used to adaptively learn feature representations from multiple views for GRN inference. Experimental results show that MHHGRN achieves better results than the baseline methods on the E. coli and S. cerevisiae benchmark datasets of the DREAM5 challenge, and it has excellent cross-species generalization, achieving comparable or better performance on scRNA-seq datasets from five mouse and two human cell lines.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

The source code and data are available at https://github.com/lcwmp333/MHHGRN.

References

  1. Stumpf MPH (2021) Inferring better gene regulation networks from single-cell data. Curr Opin Syst Biol 27:100342. https://doi.org/10.1016/j.coisb.2021.05.003

    Article  CAS  Google Scholar 

  2. Du Z, Wu Y, Huang Y et al (2022) GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions. Brief Bioinform 23(3):bbac48. https://doi.org/10.1093/bib/bbac148

    Article  CAS  Google Scholar 

  3. Xing P, Chen Y, Gao J et al (2017) A fast approach to detect gene–gene synergy. Sci Rep 7(1):16437. https://doi.org/10.1038/s41598-017-16748-w

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  4. Graudenzi A, Serra R, Villani M et al (2011) Dynamical properties of a boolean model of gene regulatory network with memory. J Comput Biol 18(10):1291–1303. https://doi.org/10.1089/cmb.2010.0069

    Article  MathSciNet  PubMed  CAS  Google Scholar 

  5. Bansal M, Gatta GD, Bernardo D (2006) Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22(7):815–822. https://doi.org/10.1093/bioinformatics/btl003

    Article  PubMed  CAS  Google Scholar 

  6. Usadel B, Obayashi T, Mutwil M et al (2009) Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ 32(12):1633–1651. https://doi.org/10.1111/j.1365-3040.2009.02040.x

    Article  PubMed  CAS  Google Scholar 

  7. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4(1):17. https://doi.org/10.2202/1544-6115.1128

    Article  MathSciNet  Google Scholar 

  8. Margolin AA, Nemenman I, Basso K et al (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform 7(Suppl 1):S7. https://doi.org/10.1186/1471-2105-7-S1-S7

    Article  CAS  Google Scholar 

  9. Faith JJ, Hayete B, Thaden JT et al (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLOS Biol 5(1):e8. https://doi.org/10.1371/journal.pbio.0050008

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Huynh-Thu VA, Irrthum A, Wehenkel L et al (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5(9):e12776. https://doi.org/10.1371/journal.pone.0012776

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  11. Moerman T, Aibar Santos S, Bravo González-Blas C et al (2019) GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics 35(12):2159–2161. https://doi.org/10.1093/bioinformatics/bty916

    Article  PubMed  CAS  Google Scholar 

  12. Zheng R, Li M, Chen X et al (2019) BiXGBoost: a scalable, flexible boosting-based method for reconstructing gene regulatory networks. Bioinformatics 35(11):1893–1900. https://doi.org/10.1093/bioinformatics/bty908

    Article  PubMed  CAS  Google Scholar 

  13. Shu H, Zhou J, Lian Q et al (2021) Modeling gene regulatory networks using neural network architectures. Nat Comput Sci 1(7):491–501. https://doi.org/10.1038/s43588-021-00099-8

    Article  PubMed  Google Scholar 

  14. Yang Y, Fang Q, Shen HB (2019) Predicting gene regulatory interactions based on spatial gene expression data and deep learning. PLOS Comput Biol 15(9):e1007324. https://doi.org/10.1371/journal.pcbi.1007324

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  15. Yuan Y, Bar-Joseph Z (2019) Deep learning for inferring gene relationships from single-cell expression data. Proc Natl Acad Sci 116(52):27151–27158. https://doi.org/10.1073/pnas.1911536116

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  16. Chen G, Liu ZP (2022) Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data. Bioinformatics 38(19):4522–4529. https://doi.org/10.1093/bioinformatics/btac559

    Article  PubMed  CAS  Google Scholar 

  17. Wang J, Ma A, Ma Q et al (2020) Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks. Comput Struct Biotechnol J 18:3335–3343. https://doi.org/10.1016/j.csbj.2020.10.022

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Yu H, Luscombe NM, Qian J et al (2003) Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet 19(8):422–427. https://doi.org/10.1016/S0168-9525(03)00175-6

    Article  PubMed  CAS  Google Scholar 

  19. Feng Y, You H, Zhang Z et al (2019) Hypergraph neural networks. Proc AAAI Conf Artif Intell 33(1):3558–3565. https://doi.org/10.1609/aaai.v33i01.33013558

    Article  Google Scholar 

  20. Marbach D, Costello JC, Küffner R et al (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9(8):796–804. https://doi.org/10.1038/nmeth.2016

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. https://doi.org/10.48550/arXiv.1609.02907

  22. Hao X, Li J, Guo Y et al (2021) Hypergraph neural network for skeleton-based action recognition. IEEE Trans Image Process 30:2263–2275. https://doi.org/10.1109/TIP.2021.3051495

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  23. Liu S, Lv P, Zhang Y et al (2021) Semi-dynamic hypergraph neural network for 3D pose estimation. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp 782–788. https://doi.org/10.24963/ijcai.2020/109

  24. Sawhney R, Agarwal S, Wadhwa A et al (2021) Stock selection via spatiotemporal hypergraph attention network: a learning to rank approach. Proc AAAI Conf Artif Intell 35(1):497–504. https://doi.org/10.1609/aaai.v35i1.16127

    Article  Google Scholar 

  25. Pio-Lopez L, Valdeolivas A, Tichit L et al (2021) MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach. Sci Rep 11(1):8794. https://doi.org/10.1038/s41598-021-87987-1

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  26. Zeng C, Lu L, Liu H et al (2022) Multiplex network disintegration strategy inference based on deep network representation learning. Chaos Interdiscip J Nonlinear Sci 32(5):053109. https://doi.org/10.1063/5.0075575

    Article  MathSciNet  Google Scholar 

  27. Xiao Y, Zhang L, Li Q et al (2019) MM-SIS: model for multiple information spreading in multiplex network. Phys Stat Mech Its Appl 513:135–146. https://doi.org/10.1016/j.physa.2018.08.169

    Article  Google Scholar 

  28. Sahneh FD, Scoglio C (2014) Competitive epidemic spreading over arbitrary multilayer networks. Phys Rev E 89(6):062817. https://doi.org/10.1103/PhysRevE.89.062817

    Article  ADS  CAS  Google Scholar 

  29. Watkins NJ, Nowzari C, Preciado VM et al (2018) Optimal resource allocation for competitive spreading processes on bilayer networks. IEEE Trans Control Netw Syst 5(1):298–307. https://doi.org/10.1109/TCNS.2016.2607838

    Article  MathSciNet  Google Scholar 

  30. Zhang H, Chen X, Peng Y et al (2022) The interaction of multiple information on multiplex social networks. Inf Sci 605:366–380. https://doi.org/10.1016/j.ins.2022.05.036

    Article  Google Scholar 

  31. He C, Xie T, Rong Y et al (2019) Bipartite graph neural networks for efficient node representation learning. arXiv preprint arXiv:1906.11994v2. http://arxiv.org/abs/1906.11994v2. Accessed 1 Oct 2019

  32. Wang Q, Wu B, Zhu P et al (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 11531–11539. https://doi.org/10.1109/CVPR42600.2020.01155

  33. Moore JE, Purcaro MJ, Pratt HE et al (2020) Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583(7818):699–710. https://doi.org/10.1038/s41586-020-2493-4

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  34. Oki S, Ohta T, Shioi G et al (2018) ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep 19(12):e46255. https://doi.org/10.15252/embr.201846255

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Xu H, Baroukh C, Dannenfelser R et al (2013) ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells. Database 2013:bat045. https://doi.org/10.1093/database/bat045

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Pratapa A, Jalihal AP, Law JN et al (2020) Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat Methods 17(2):147–154. https://doi.org/10.1038/s41592-019-0690-6

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Osorio D, Zhong Y, Li G et al (2020) scTenifoldNet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data. Patterns 1(9):100139. https://doi.org/10.1016/j.patter.2020.100139

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Maetschke SR, Madhamshettiwar PB, Davis MJ et al (2014) Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Brief Bioinform 15(2):195–211. https://doi.org/10.1093/bib/bbt034

    Article  PubMed  Google Scholar 

  39. Kc K, Li R, Cui F et al (2019) GNE: a deep learning framework for gene network inference by aggregating biological information. BMC Syst Biol 13(Suppl 2):38. https://doi.org/10.1186/s12918-019-0694-y

    Article  PubMed  PubMed Central  Google Scholar 

  40. Matsumoto H, Kiryu H, Furusawa C et al (2017) SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics 33(15):2314–2321. https://doi.org/10.1093/bioinformatics/btx194

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Zhao L, Akoglu L (2019) PairNorm: Tackling oversmoothing in gnns. arXiv preprint arXiv:1909.12223. https://doi.org/10.48550/arXiv.1909.12223

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61862067); Applied Basic Research Project in Yunnan Province (No.202101AT070132); Yunnan Science Fund (202105AF150028).

Funding

This work was supported by the Engineering Research Center of Sustainable Development and Utilization of Biomass Energy, Ministry of Education; Key Lab of Yunnan Province for Biomass Energy and Environmental Biotechnology.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China

    Songyang Wu, Kui Jin, Yuelong Xia & Wei Gao

  2. School of Life Science, Yunnan Normal University, Kunming, 650500, China

    Mingjing Tang

  3. Engineering Research Center of Sustainable Development and Utilization of Biomass Energy, Ministry of Education, Yunnan Normal University, Kunming, 650500, China

    Mingjing Tang

Authors
  1. Songyang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kui Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingjing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuelong Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingjing Tang.

Ethics declarations

Conflict of interest

On behalf of my co-authors, I wish to state that the work described is original research that has not been previously published or considered for publication elsewhere, in whole or in part. All authors have approved the accompanying manuscript and agreed to its publication. On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, S., Jin, K., Tang, M. et al. Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00604-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12539-024-00604-3

Keywords

Search

Navigation