Network Metamodeling: Effect of Correlation Metric Choice on Phylogenomic and Transcriptomic Network Topology
We explore the use of a network meta-modeling approach to compare the effects of similarity metrics used to construct biological networks on the topology of the resulting networks. This work reviews various similarity metrics for the construction of networks and various topology measures for the characterization of resulting network topology, demonstrating the use of these metrics in the construction and comparison of phylogenomic and transcriptomic networks.
KeywordsNetwork comparison Network topology Similarity metrics
Author’s Contributions and Acknowledgments
D. Weighill and D. Jacobson conceived of and designed the methods, D. Weighill wrote the code and created the networks, D. Weighill and D. Jacobson discussed and interpreted the networks, D. Weighill drafted the manuscript, and D. Jacobson critically revised and edited the manuscript.
The research reported in this chapter was performed at Stellenbosch University, South Africa as part of a Master's thesis , and subsequent editing for publication in this book was performed at Oak Ridge National Laboratory and University of Tennessee, Knoxville.
Competing Interests The authors declare that they have no competing financial interests.
- 5.Pinto da Costa J, Soares C (2005) A weighted rank measure of correlation. Aust N Z J Stat 47(4):515–529Google Scholar
- 6.Jaccard P (1912) The distribution of the flora in the alpine zone. 1. New Phytol 11(2):37–50Google Scholar
- 9.Sørensen T (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter 5:1–34Google Scholar
- 16.Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. Detecting novel associations in large data sets - supplementary material. http://www.sciencemag.org/content/334/6062/1518/suppl/DC1. Accessed Feb 2013
- 17.Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC (2011) Detecting novel associations in large data sets. Science 334(6062):1518–1524Google Scholar
- 19.Zhang B, Horvath S et al (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4(1):5144–6115Google Scholar
- 27.Meilă M (2005) Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd international conference on machine learning, ACM, pp 577–584Google Scholar
- 28.Van Dongen S (2000) Graph clustering by flow simulation. Ph.D. thesis, University of UtrechtGoogle Scholar
- 30.Wagner S, Wagner D (2007) Comparing clusterings: an overview. Universität Karlsruhe, Fakultät für InformatikGoogle Scholar
- 32.Berlingerio M, Koutra D, Eliassi-Rad T, Faloutsos C. A scalable approach to size-independent network similarity. Available: http://arxiv.org/pdf/1209.2684.pdf
- 34.Qlucore (2008) http://www.qlucore.com/. Accessed 14 Feb 2013
- 41.Weighill DA (2014) Exploring the topology of complex phylogenomic and transcriptomic networks. Master’s thesis, Stellenbosch UniversityGoogle Scholar