Skip to main content
SpringerLink
Log in
SpringerLink
Log in

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

  • Chapter
  • First Online:
Network Biology

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((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.

This work was supported by the Plant-Microbe Interfaces Scientific Focus Area (http://pmi.ornl.gov) in the Genomic Science Program, the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science, and the BERs BioEnergy Science Center (BESC) at the Oak Ridge National Laboratory (contract DE-PS02-06ER64304). Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the chapter for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy provides public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). The authors would also like to acknowledge the Centre for High Performance Computing and the Stellenbosch High Performance Computing Cluster for computing resources, and the South African National Research Foundation (www.nrf.ac.za) Technology and Human Resources Programme and Winetech. The financial assistance of the National Research Foundation (NRF) toward this research is hereby acknowledged. Opinions expressed and conclusions reached are those of the author and are not necessarily to be attributed to the NRF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113

    Article  CAS  Google Scholar 

  2. Pearson K (1895) Note on regression and inheritance in the case of two parents. Proc R Soc Lond 58(347–352):240–242

    Article  Google Scholar 

  3. Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42(1):59–66

    Article  Google Scholar 

  4. Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72–101

    Article  Google Scholar 

  5. Pinto da Costa J, Soares C (2005) A weighted rank measure of correlation. Aust N Z J Stat 47(4):515–529

    Google Scholar 

  6. Jaccard P (1912) The distribution of the flora in the alpine zone. 1. New Phytol 11(2):37–50

    Google Scholar 

  7. Lipkus AH (1999) A proof of the triangle inequality for the Tanimoto distance. J Math Chem 26(1–3):263–265

    Article  Google Scholar 

  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):315–318

    Article  Google 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 5:1–34

    Google Scholar 

  10. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  11. Yoshioka PM (2008) Misidentification of the Bray-Curtis similarity index. Mar Ecol Prog Ser 368:309–310

    Article  Google Scholar 

  12. Bray JR, Curtis JT (1957) An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr 27(4):325–349

    Article  Google Scholar 

  13. Lance G, Williams W (1966) Computer programs for hierarchical polythetic classification (“similarity analyses”). Comput J 9(1):60–64

    Article  Google Scholar 

  14. Schubert A (2013) Measuring the similarity between the reference and citation distributions of journals. Scientometrics 96(1):305–313

    Article  Google Scholar 

  15. Schubert A, Telcs A (2014) A note on the Jaccardized Czekanowski similarity index. Scientometrics 98(2):1397–1399

    Article  Google 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):1518–1524

    Google Scholar 

  18. Horvath S, Dong J (2008) Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol 4(8):e1000117

    Article  Google 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):5144–6115

    Google Scholar 

  20. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–1555

    Article  CAS  Google Scholar 

  21. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442

    Article  CAS  Google Scholar 

  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):2317–2331

    Article  Google Scholar 

  23. Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701

    Article  CAS  Google Scholar 

  24. Snijders TA (1981) The degree variance: an index of graph heterogeneity. Soc Networks 3(3):163–174

    Article  Google Scholar 

  25. Dong J, Horvath S (2007) Understanding network concepts in modules. BMC Syst Biol 1:24

    Article  Google Scholar 

  26. Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

    Article  Google Scholar 

  27. Meilă M (2005) Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd international conference on machine learning, ACM, pp 577–584

    Google Scholar 

  28. Van Dongen S (2000) Graph clustering by flow simulation. Ph.D. thesis, University of Utrecht

    Google Scholar 

  29. Van Dongen S (2008) Graph clustering via a discrete uncoupling process. SIAM J Matrix Anal Appl 30(1):121–141

    Article  Google Scholar 

  30. Wagner S, Wagner D (2007) Comparing clusterings: an overview. Universität Karlsruhe, Fakultät für Informatik

    Google Scholar 

  31. Meilă M (2007) Comparing clusterings - an information based distance. J Multivar Anal 98(5):873–895

    Article  Google 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

  33. Bloom SA (1981) Similarity indices in community studies: potential pitfalls. Mar Ecol Prog Ser 5(2):125–128

    Article  Google Scholar 

  34. Qlucore (2008) http://www.qlucore.com/. Accessed 14 Feb 2013

  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):2498–2504

    Article  CAS  Google Scholar 

  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):249–264

    Article  Google Scholar 

  37. Li L, Stoeckert C, Roos D (2003) Orthomcl: identification of ortholog groups for eukaryotic genomes. Genome Res 13(9):2178–2189

    Article  CAS  Google Scholar 

  38. Enright A, Van Dongen S, Ouzounis C (2002) An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 30(7):1575–1578

    Article  CAS  Google Scholar 

  39. Setati ME, Jacobson D, Andong UC, Bauer F (2012) The vineyard yeast microbiome, a mixed model microbial map. PLoS One 7(12):e52609

    Article  CAS  Google Scholar 

  40. Federhen S (2012) The NCBI taxonomy database. Nucleic Acids Res 40(D1):D136–D143

    Article  CAS  Google Scholar 

  41. Weighill DA (2014) Exploring the topology of complex phylogenomic and transcriptomic networks. Master’s thesis, Stellenbosch University

    Google Scholar 

Download references

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 [41], 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.

Author information

Authors and Affiliations

  1. Faculty of AgriSciences, Institute for Wine Biotechnology, Stellenbosch University, JH Neethling Building, Victoria Street, 7600, Stellenbosch, South Africa

    Deborah A. Weighill & Daniel Jacobson

  2. The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, 444 Greve Hall, 821 Volunteer Blvd., Knoxville, TN, 37996-3394, USA

    Deborah A. Weighill & Daniel Jacobson

  3. Biosciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA

    Deborah A. Weighill & Daniel Jacobson

Authors
  1. Deborah A. Weighill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Jacobson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Jacobson .

Editor information

Editors and Affiliations

  1. Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Science, Little Rock, Arkansas, USA

    Intawat Nookaew

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Weighill, D.A., Jacobson, D. (2016). Network Metamodeling: Effect of Correlation Metric Choice on Phylogenomic and Transcriptomic Network Topology. In: Nookaew, I. (eds) Network Biology. Advances in Biochemical Engineering/Biotechnology, vol 160. Springer, Cham. https://doi.org/10.1007/10_2016_46

Download citation

Publish with us

Policies and ethics

Search

Navigation