Network Metamodeling: Effect of Correlation Metric Choice on Phylogenomic and Transcriptomic Network Topology

Chapter
Part of the Advances in Biochemical Engineering/Biotechnology book series (ABE, volume 160)

Abstract

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.

Keywords

Network comparison Network topology Similarity metrics 

References

  1. 1.
    Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113CrossRefGoogle Scholar
  2. 2.
    Pearson K (1895) Note on regression and inheritance in the case of two parents. Proc R Soc Lond 58(347–352):240–242CrossRefGoogle Scholar
  3. 3.
    Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42(1):59–66CrossRefGoogle Scholar
  4. 4.
    Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72–101CrossRefGoogle Scholar
  5. 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. 6.
    Jaccard P (1912) The distribution of the flora in the alpine zone. 1. New Phytol 11(2):37–50Google Scholar
  7. 7.
    Lipkus AH (1999) A proof of the triangle inequality for the Tanimoto distance. J Math Chem 26(1–3):263–265CrossRefGoogle Scholar
  8. 8.
    Hamers L, Hemeryck Y, Herweyers G, Janssen M, Keters H, Rousseau R, Vanhoutte A (1989) Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula. Inform Process Manage 25(3):315–318CrossRefGoogle Scholar
  9. 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
  10. 10.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRefGoogle Scholar
  11. 11.
    Yoshioka PM (2008) Misidentification of the Bray-Curtis similarity index. Mar Ecol Prog Ser 368:309–310CrossRefGoogle Scholar
  12. 12.
    Bray JR, Curtis JT (1957) An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr 27(4):325–349CrossRefGoogle Scholar
  13. 13.
    Lance G, Williams W (1966) Computer programs for hierarchical polythetic classification (“similarity analyses”). Comput J 9(1):60–64CrossRefGoogle Scholar
  14. 14.
    Schubert A (2013) Measuring the similarity between the reference and citation distributions of journals. Scientometrics 96(1):305–313CrossRefGoogle Scholar
  15. 15.
    Schubert A, Telcs A (2014) A note on the Jaccardized Czekanowski similarity index. Scientometrics 98(2):1397–1399CrossRefGoogle Scholar
  16. 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. 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
  18. 18.
    Horvath S, Dong J (2008) Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol 4(8):e1000117CrossRefGoogle Scholar
  19. 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
  20. 20.
    Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–1555CrossRefGoogle Scholar
  21. 21.
    Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442CrossRefGoogle Scholar
  22. 22.
    Reijneveld JC, Ponten SC, Berendse HW, Stam CJ (2007) The application of graph theoretical analysis to complex networks in the brain. Clin Neurophysiol 118(11):2317–2331CrossRefGoogle Scholar
  23. 23.
    Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701CrossRefGoogle Scholar
  24. 24.
    Snijders TA (1981) The degree variance: an index of graph heterogeneity. Soc Networks 3(3):163–174CrossRefGoogle Scholar
  25. 25.
    Dong J, Horvath S (2007) Understanding network concepts in modules. BMC Syst Biol 1:24CrossRefGoogle Scholar
  26. 26.
    Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239CrossRefGoogle Scholar
  27. 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. 28.
    Van Dongen S (2000) Graph clustering by flow simulation. Ph.D. thesis, University of UtrechtGoogle Scholar
  29. 29.
    Van Dongen S (2008) Graph clustering via a discrete uncoupling process. SIAM J Matrix Anal Appl 30(1):121–141CrossRefGoogle Scholar
  30. 30.
    Wagner S, Wagner D (2007) Comparing clusterings: an overview. Universität Karlsruhe, Fakultät für InformatikGoogle Scholar
  31. 31.
    Meilă M (2007) Comparing clusterings - an information based distance. J Multivar Anal 98(5):873–895CrossRefGoogle Scholar
  32. 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
  33. 33.
    Bloom SA (1981) Similarity indices in community studies: potential pitfalls. Mar Ecol Prog Ser 5(2):125–128CrossRefGoogle Scholar
  34. 34.
    Qlucore (2008) http://www.qlucore.com/. Accessed 14 Feb 2013
  35. 35.
    Shannon P, Markiel A, Ozier O, Baliga N, Wang J, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504CrossRefGoogle Scholar
  36. 36.
    Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264CrossRefGoogle Scholar
  37. 37.
    Li L, Stoeckert C, Roos D (2003) Orthomcl: identification of ortholog groups for eukaryotic genomes. Genome Res 13(9):2178–2189CrossRefGoogle Scholar
  38. 38.
    Enright A, Van Dongen S, Ouzounis C (2002) An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 30(7):1575–1578CrossRefGoogle Scholar
  39. 39.
    Setati ME, Jacobson D, Andong UC, Bauer F (2012) The vineyard yeast microbiome, a mixed model microbial map. PLoS One 7(12):e52609CrossRefGoogle Scholar
  40. 40.
    Federhen S (2012) The NCBI taxonomy database. Nucleic Acids Res 40(D1):D136–D143CrossRefGoogle Scholar
  41. 41.
    Weighill DA (2014) Exploring the topology of complex phylogenomic and transcriptomic networks. Master’s thesis, Stellenbosch UniversityGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of AgriSciences, Institute for Wine BiotechnologyStellenbosch UniversityStellenboschSouth Africa
  2. 2.The Bredesen Center for Interdisciplinary Research and Graduate EducationUniversity of Tennessee, KnoxvilleKnoxvilleUSA
  3. 3.Biosciences DivisionOak Ridge National LaboratoryOak RidgeUSA

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