Skip to main content
Springer Nature Link
Log in

Non Parametric Differential Network Analysis for Biological Data

  • Conference paper
  • First Online:
Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1143))

Included in the following conference series:

  • 1010 Accesses

Abstract

Rewiring of molecular interactions under different conditions causes different phenotypic responses. Differential Network Analysis (also indicated as DNA) aims to investigate the rewiring of gene and protein networks. DNA algorithms combine statistical learning and graph theory to explore the changes in the interaction patterns starting from experimental observation. Despite there exist many methods to model rewiring in networks, we propose to use age and gender factors to guide rewiring algorithms. We present a novel differential network analysis method that consider the differential expression of genes by means of sex and gender attributes. We hypothesise that the expression of genes may be represented by using a non-gaussian process. We quantify changes in non-parametric correlations between gene pairs and changes in expression levels for individual genes. We apply our method to identify the differential networks between males and females in public expression datasets related to mellitus diabetes in liver tissue. Results show that this method can find biologically relevant differential networks.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Allen, G.I., Liu, Z.: A local poisson graphical model for inferring networks from sequencing data. IEEE Trans. Nanobiosci. 12(3), 189–198 (2013)

    Article  Google Scholar 

  2. Buccitelli, C., Selbach, M.: mRNAs, proteins and the emerging principles of gene expression control. Nat. Rev. Genet. 21(10), 630–644 (2020)

    Article  Google Scholar 

  3. Cannataro, M., Guzzi, P.H., Mazza, T., Tradigo, G., Veltri, P.: Using ontologies for preprocessing and mining spectra data on the grid. Futur. Gener. Comput. Syst. 23(1), 55–60 (2007)

    Article  Google Scholar 

  4. Cannistraci, C.V., Valsecchi, M.G., Capua, I.: Age-sex population adjusted analysis of disease severity in epidemics as a tool to devise public health policies for COVID-19. Sci. Rep. 11(1), 1–8 (2021)

    Article  Google Scholar 

  5. Chen, S., Witten, D.M., Shojaie, A.: Selection and estimation for mixed graphical models. Biometrika 102(1), 47–64 (2014)

    Article  MathSciNet  Google Scholar 

  6. Chiarella, G., et al.: Vestibular disorders in euthyroid patients with hashimoto’s thyroiditis: role of thyroid autoimmunity. Clin. Endocrinol. 81(4), 600–605 (2014)

    Article  Google Scholar 

  7. Cho, Y.R., Mina, M., Lu, Y., Kwon, N., Guzzi, P.H.: M-finder: uncovering functionally associated proteins from interactome data integrated with go annotations. Proteome Sci. 11(1), 1–12 (2013)

    Google Scholar 

  8. Cookson, W., Liang, L., Abecasis, G., Moffatt, M., Lathrop, M.: Mapping complex disease traits with global gene expression. Nat. Rev. Genet. 10(3), 184–194 (2009)

    Article  Google Scholar 

  9. Galicia, J.C., Guzzi, P.H., Giorgi, F.M., Khan, A.A.: Predicting the response of the dental pulp to SARS-CoV2 infection: a transcriptome-wide effect cross-analysis. Genes Immun. 21(5), 360–363 (2020)

    Article  Google Scholar 

  10. Grimes, T., Potter, S.S., Datta, S.: Integrating gene regulatory pathways into differential network analysis of gene expression data. Sci. Rep. 9(1), 1–12 (2019)

    Article  Google Scholar 

  11. Gu, S., Jiang, M., Guzzi, P.H., Milenković, T.: Modeling multi-scale data via a network of networks. Bioinformatics 38(9), 2544–2553 (2022)

    Article  Google Scholar 

  12. Guzzi, P.H., et al.: Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities. Sci. Rep. 13(1), 10372 (2023)

    Article  Google Scholar 

  13. Guzzi, P.H., et al.: Differential network analysis between sex of the genes related to comorbidities of type 2 mellitus diabetes. Appl. Network Sci. 8(1), 1–16 (2023)

    Article  Google Scholar 

  14. Ideker, T., Krogan, N.J.: Differential network biology. Mol. Syst. Biol. 8(1), 565 (2012)

    Article  Google Scholar 

  15. Lauritzen, S.L.: Graphical Models, vol. 17. Clarendon Press (1996)

    Google Scholar 

  16. Lonsdale, J., et al.: The genotype-tissue expression (GTEx) project. Nat. Genet. 45(6), 580–585 (2013)

    Article  Google Scholar 

  17. Mangoni, M., et al.: Investigating mitochondrial gene expression patterns in drosophila melanogaster using network analysis to understand aging mechanisms. Appl. Sci. 13(12), 7342 (2023)

    Article  Google Scholar 

  18. Mercatelli, D., Pedace, E., Veltri, P., Giorgi, F.M., Guzzi, P.H.: Exploiting the molecular basis of age and gender differences in outcomes of SARS-CoV-2 infections. Comput. Struct. Biotechnol. J. 19, 4092–4100 (2021)

    Article  Google Scholar 

  19. Milano, M., et al.: An extensive assessment of network alignment algorithms for comparison of brain connectomes. BMC Bioinf. 18, 31–45 (2017)

    Article  Google Scholar 

  20. Ortuso, F., Mercatelli, D., Guzzi, P.H., Giorgi, F.M.: Structural genetics of circulating variants affecting the SARS-CoV-2 spike/human ace2 complex. J. Biomol. Struct. Dyn. 40(14), 6545–6555 (2021)

    Article  Google Scholar 

  21. Pressler, M.P., Horvath, A., Entcheva, E.: Sex-dependent transcription of cardiac electrophysiology and links to acetylation modifiers based on the GTEx database. Front. Cardiovasc. Med. 9, 941890 (2022)

    Article  Google Scholar 

  22. Roy, A., Dunson, D.B.: Nonparametric graphical model for counts. J. Mach. Learn. Res. 21(1), 9353–9373 (2020)

    MathSciNet  Google Scholar 

  23. Roy, S., Manners, H.N., Jha, M., Guzzi, P.H., Kalita, J.K.: Soft computing approaches to extract biologically significant gene network modules. In: Purohit, H.J., Kalia, V.C., More, R.P. (eds.) Soft Computing for Biological Systems, pp. 23–37. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7455-4_3

    Chapter  Google Scholar 

  24. Shojaie, A.: Differential network analysis: a statistical perspective. Wiley Interdiscip. Rev. Comput. Stat. 13(2), e1508 (2021)

    Article  MathSciNet  Google Scholar 

  25. Stark, R., Grzelak, M., Hadfield, J.: RNA sequencing: the teenage years. Nat. Rev. Genet. 20(11), 631–656 (2019)

    Article  Google Scholar 

  26. Succurro, E., et al.: Sex-specific differences in prevalence of nonalcoholic fatty liver disease in subjects with prediabetes and type 2 diabetes. Diabetes Res. Clin. Pract. 190, 110027 (2022)

    Article  Google Scholar 

  27. Wainwright, M.J., Lafferty, J.D., Ravikumar, P.K.: High-dimensional graphical model selection using \(\ell _1\) regularized logistic regression. In: Advances in Neural Information Processing Systems, pp. 1465–1472 (2007)

    Google Scholar 

  28. Zimmerman, D.W.: Comparative power of student T test and Mann-Whitney U test for unequal sample sizes and variances. J. Exp. Educ. 55(3), 171–174 (1987)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Magna Graecia University, Catanzaro, Italy

    Pietro Hiram Guzzi & Francesca Cortese

  2. University of Florida, Gainesville, USA

    Arkaprava Roy

  3. DIMES, University of Calabria, Cosenza, Italy

    Pierangelo Veltri

Authors
  1. Pietro Hiram Guzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arkaprava Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesca Cortese
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pierangelo Veltri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pietro Hiram Guzzi .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  3. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  4. Department of Economics, Yildiz Technical University, Istanbul, Türkiye

    Murat Donduran

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guzzi, P.H., Roy, A., Cortese, F., Veltri, P. (2024). Non Parametric Differential Network Analysis for Biological Data. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-031-53472-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-53472-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53471-3

  • Online ISBN: 978-3-031-53472-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics