Abstract
Gene co-expression networks (GCNs) specify binary relationships between genes and are of biological interest because significant network relationships suggest that two co-expressed genes rise and fall together across different cellular conditions. GCNs are built by (i) calculating a co-expression measure between each pair of genes and (ii) selecting a significance threshold to remove spurious relationships among genes. This paper introduces a threshold criterion based on the underlying topology of the network. More specifically, the criterion considers both the rate at which isolated nodes are added to the network and the density of its components when the threshold varies. In addition to Pearson’s correlation measure, the biweight midcorrelation, the distance correlation, and the maximal information coefficient are used to build different GCNs from the same data and showcase the advantages of the proposed approach. Finally, a case study presents a comparison of the predictive performance of the different networks when trying to predict gene functional annotations using hierarchical multi-label classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aoki, K., Ogata, Y., Shibata, D.: Approaches for extracting practical information from gene co-expression networks in plant biology. Plant Cell Physiol. 48, 381–90 (2007)
Barrett, T., Wilhite, S.E., Ledoux, P., Evangelista, C., Kim, I.F., Tomashevsky, M., Marshall, K.A., Phillippy, K.H., Sherman, P.M., Holko, M., Yefanov, A., Lee, H., Zhang, N., Robertson, C.L., Serova, N., Davis, S., Soboleva, A.: NCBI GEO: archive for functional genomics data sets-update. Nucl. Acids Res. 41(D1), D991–D995 (2012). https://doi.org/10.1093/nar/gks1193
Carter, S.L., Brechbühler, C.M., Griffin, M., Bond, A.T.: Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20(14), 2242–2250 (2004). https://doi.org/10.1093/bioinformatics/bth234
Couto, C.M.V., Comin, C.H., Costa, L.D.F.: Effects of threshold on the topology of gene co-expression networks. Molecular Biosyst. 13, 2024–2035 (2017)
Gene Ontology Consortium: The gene ontology resource: 20 years and still going strong. Nucl. Acids Res. 47(D1), D330–D338 (2019)
Hou, J., Ye, X., Feng, W., Zhang, Q., Han, Y., Liu, Y., Li, Y., Wei, Y.: Distance correlation application to gene co-expression network analysis. BMC Bioinform. 23, 81 (2022)
Huang, D.W., Sherman, B.T., Lempicki, R.A.: Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nature Prot. 4, 44–57 (2009)
Langfelder, P., Horvath, S.: WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008)
Levatić, J., Kocev, D., Džeroski, S.: The importance of the label hierarchy in hierarchical multi-label classification. J. Intell. Inform. Syst. 45(2), 247–271 (2015). http://link.springer.com/10.1007/s10844-014-0347-y
Margolin, A.A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., Califano, A.: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7(Suppl 1), S7 (2006)
Rau, C., Wisniewski, N., Orozco, L., Bennett, B., Weiss, J., Lusis, A.: Maximal information component analysis: a novel non-linear network analysis method. Front. Genet. 4 (2013). https://www.frontiersin.org/article/10.3389/fgene.2013.00028
Reshef, D.N., Reshef, Y.A., Finucane, H.K., Grossman, S.R., McVean, G., Turnbaugh, P.J., Lander, E.S., Mitzenmacher, M., Sabeti, P.C.: Detecting novel associations in large data sets. Science (New York, N.Y.) 334, 1518–1524 (2011)
Schaefer, R.J., Michno, J.M., Jeffers, J., Hoekenga, O., Dilkes, B., Baxter, I., Myers, C.L.: Integrating co-expression networks with gwas to prioritize causal genes in maize. The Plant Cell 30, 2922–2942 (2018)
Silverman, E.K., Schmidt, H.H.H.W., Anastasiadou, E., Altucci, L., Angelini, M., Badimon, L., Balligand, J.L., Benincasa, G., Capasso, G., Conte, F., Di Costanzo, A., Farina, L., Fiscon, G., Gatto, L., Gentili, M., Loscalzo, J., Marchese, C., Napoli, C., Paci, P., Petti, M., Quackenbush, J., Tieri, P., Viggiano, D., Vilahur, G., Glass, K., Baumbach, J.: Molecular networks in network medicine: development and applications. Wiley Interdisciplinary Reviews. Syst. Biol. Med. 12, e1489 (2020)
Song, L., Langfelder, P., Horvath, S.: Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinform. 13, 328 (2012)
Vens, C., Struyf, J., Schietgat, L., Džeroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Mach. Learn. 73(2), 185–214 (2008)
van Verk, M.C., Bol, J.F., Linthorst, H.J.M.: Prospecting for genes involved in transcriptional regulation of plant defenses, a bioinformatics approach. BMC Plant Biol. 11, 88 (2011)
Wang, Y.X.R., Li, L., Li, J.J., Huang, H.: Network modeling in biology: statistical methods for gene and brain networks. Stati. Sci. Rev. J. Inst. Math. Stat. 36, 89–108 (2021)
Zhang, L., Yu, S., Zuo, K., Luo, L., Tang, K.: Identification of gene modules associated with drought response in rice by network-based analysis. PLOS ONE 7, e33748 (2012)
Acknowledgments
This work was funded by the OMICAS program: Optimización Multiescala In-silico de Cultivos Agrícolas Sostenibles (Infraestructura y Validación en Arroz y Caña de Azúcar), anchored at the Pontificia Universidad Javeriana in Cali and funded within the Colombian Scientific Ecosystem by The World Bank, the Colombian Ministry of Science, Technology and Innovation, the Colombian Ministry of Education, and the Colombian Ministry of Industry and Tourism, and ICETEX, under GRANT ID: FP44842-217-2018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
López-Rozo, N., Romero, M., Finke, J., Rocha, C. (2023). A Network-based Approach for Inferring Thresholds in Co-expression Networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-21127-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21126-3
Online ISBN: 978-3-031-21127-0
eBook Packages: EngineeringEngineering (R0)