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How Different Are Estimated Genetic Networks of Cancer Subtypes?

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Big and Complex Data Analysis

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

Genetic networks provide compact representations of interactions between genes, and offer a systems perspective into biological processes and cellular functions. Many algorithms have been developed to estimate such networks based on steady-state gene expression profiles. However, the estimated networks using different methods are often very different from each other. On the other hand, it is not clear whether differences observed between estimated networks in two different biological conditions are truly meaningful, or due to variability in estimation procedures. In this paper, we aim to answer these questions by conducting a comprehensive empirical study to compare networks obtained from different estimation methods and for different subtypes of cancer. We evaluate various network descriptors to assess complex properties of estimated networks, beyond their local structures, and propose a simple permutation test for comparing estimated networks. The results provide new insight into properties of estimated networks using different reconstruction methods, as well as differences in estimated networks in different biological conditions.

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References

  1. Schadt, E.E.: Molecular networks as sensors and drivers of common human diseases. Nature 461 (7261), 218–223 (2009)

    Article  Google Scholar 

  2. Janjić, V., Pržulj, N.: Biological function through network topology: a survey of the human diseasome. Brief. Funct. Genomics 11 (6), 522–532 (2012)

    Article  Google Scholar 

  3. Coulson, J.M.: Transcriptional regulation: cancer, neurons and the rest. Curr. Biol. 15 (17), R665–R668 (2005)

    Article  Google Scholar 

  4. Simpson, R.J., Dorow, D.S.: Cancer proteomics: from signaling networks to tumor markers. Trends Biotechnol. 19, 40–48 (2001)

    Article  Google Scholar 

  5. Osborn, O., Olefsky, J.M.: The cellular and signaling networks linking the immune system and metabolism in disease. Nat. Med. 18 (3), 363–374 (2012)

    Article  Google Scholar 

  6. Horvath, S., Zhang, B., Carlson, M., Lu, K.V., Zhu, S., Felciano, R.M., Laurance, M.F., Zhao, W., Qi, S., Chen, Z., et al.: Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc. Natl. Acad. Sci. 103 (46), 17402–17407 (2006)

    Article  Google Scholar 

  7. Ule, J., Jensen, K.B., Ruggiu, M., Mele, A., Ule, A., Darnell, R.B.: CLIP identifies Nova-regulated RNA networks in the brain. Science 302 (5648), 1212–1215 (2003)

    Article  Google Scholar 

  8. Luscombe, N.M., Babu, M.M., Yu, H., Snyder, M., Teichmann, S.A., Gerstein, M.: Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431 (7006), 308–312 (2004)

    Article  Google Scholar 

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

    Google Scholar 

  10. Zhang, B., Li, H., Riggins, R.B., Zhan, M., Xuan, J., Zhang, Z., Hoffman, E.P., Clarke, R., Wang, Y.: Differential dependency network analysis to identify condition-specific topological changes in biological networks. Bioinformatics 25 (4), 526–532 (2009)

    Article  Google Scholar 

  11. West, J., Bianconi, G., Severini, S., Teschendorff, A.E.: Differential network entropy reveals cancer system hallmarks. Sci. Rep. 2 (2012)

    Google Scholar 

  12. Henderson, J., Michailidis, G.: Network reconstruction using nonparametric additive ode models. PLoS One 9 (4), e94003 (2014)

    Article  Google Scholar 

  13. Langfelder, P., Horvath, S.: WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 9 (1), 559 (2008)

    Article  Google Scholar 

  14. Meinshausen, N., Bühlmann, P.: High-dimensional graphs and variable selection with the lasso. Ann. Stat. 34 (3), 1436–1462 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Friedman, J., Hastie, T., Tibshirani, R: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9 (3), 432–441 (2008)

    Google Scholar 

  16. Peng, J., Wang, P., Zhou, N., Zhu, J.: Partial correlation estimation by joint sparse regression models. J. Am. Stat. Assoc. 104 (486) (2009)

    Google Scholar 

  17. Zhao, T., Liu, H., Roeder, K., Lafferty, J., Wasserman, L.: The huge package for high-dimensional undirected graph estimation in R. J. Mach. Learn. Res. 13 (1), 1059–1062 (2012)

    MathSciNet  MATH  Google Scholar 

  18. Voorman, A., Shojaie, A., Witten, D.: Graph estimation with joint additive models. Biometrika 101 (1), 85–101 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Lauritzen, S.L.: Graphical Models. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  20. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT, Cambridge (2009)

    MATH  Google Scholar 

  21. Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303 (5659), 799–805 (2004)

    Article  Google Scholar 

  22. Markowetz, F., Spang, R.: Inferring cellular networks–a review. BMC Bioinf. 8 (Suppl 6), S5 (2007)

    Article  Google Scholar 

  23. Krämer, N., Schäfer, J., Boulesteix, A.-L.: Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinf. 10 (1), 384 (2009)

    Article  Google Scholar 

  24. Sedaghat, N., Saegusa, T., Randolph, T., Shojaie, A.: Comparative study of computational methods for reconstructing genetic networks of cancer-related pathways. Cancer Informat. 13 (Suppl 2), 55 (2014)

    Google Scholar 

  25. Allen, J.D., Xie, Y., Chen, M., Girard, L., Xiao, G.: Comparing statistical methods for constructing large scale gene networks. PLoS One 7 (1), e29348 (2012)

    Article  Google Scholar 

  26. Wille, A., Zimmermann, P., Vranová, E., Fürholz, A., Laule, A., Bleuler, S., Hennig, L., Prelic, A., von Rohr, P., Thiele, L., et al.: Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana. Genome Biol. 5 (11), R92 (2004)

    Article  Google Scholar 

  27. Chu, J.-H., Weiss, S.T., Carey, V.J., Raby, B.A.: A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism. BMC Syst. Biol. 3 (1), 55 (2009)

    Article  Google Scholar 

  28. Strimmer, K., Moulton, V.: Likelihood analysis of phylogenetic networks using directed graphical models. Mol. Biol. Evol. 17 (6), 875–881 (2000)

    Article  Google Scholar 

  29. Yu, J., Smith, V.A., Wang, P.P., Hartemink, A.J., Jarvis, E.D.: Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20 (18), 3594–3603 (2004)

    Article  Google Scholar 

  30. Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Learning From Data, pp. 121–130. Springer, Berlin (1996)

    Google Scholar 

  31. Shojaie, A., Jauhiainen, A., Kallitsis, M., Michailidis, G.: Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles. PLoS One 9 (2), e82393 (2014)

    Article  Google Scholar 

  32. Markowetz, F.: How to understand the cell by breaking it: network analysis of gene perturbation screens. PLoS Comput. Biol. 6 (2), e1000655 (2010)

    Article  Google Scholar 

  33. Shojaie, A., Michailidis, G.: Discovering graphical granger causality using the truncating lasso penalty. Bioinformatics 26 (18), i517–i523 (2010)

    Article  Google Scholar 

  34. Shojaie, A., Basu, S., Michailidis, G.: Adaptive thresholding for reconstructing regulatory networks from time-course gene expression data. Stat. Biosci. 4 (1), 66–83 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (2012)

    MATH  Google Scholar 

  37. 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 334 (6062), 1518–1524 (2011)

    Article  MATH  Google Scholar 

  38. Margolin, A.A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R.D., Califano, A.: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinf. 7 (Suppl 1), S7 (2006)

    Article  Google Scholar 

  39. Montes, R.A., Coello, G., González-Aguilera, K.L., Marsch-Martínez, N., de Folter, S., Alvarez-Buylla, E.R.: ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks. BMC Plant Biol. 14 (1), 97 (2014)

    Article  Google Scholar 

  40. Ravikumar, P., Wainwright, M.J., Raskutti, G., Yu, B., et al.: High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence. Electron. J. Stat. 5, 935–980 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  41. Liu, H., Lafferty, J., Wasserman, L.: The nonparanormal: semiparametric estimation of high dimensional undirected graphs. J. Mach. Learn. Res. 10, 2295–2328 (2009)

    MathSciNet  MATH  Google Scholar 

  42. Li, A., Horvath, S.: Network neighborhood analysis with the multi-node topological overlap measure. Bioinformatics 23 (2), 222–231 (2007)

    Article  Google Scholar 

  43. Yip, A.M., Horvath, S.: Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinf. 8 (1), 22 (2007)

    Article  Google Scholar 

  44. Xue, L., Zou, H., et al.: Regularized rank-based estimation of high-dimensional nonparanormal graphical models. Ann. Stat. 40 (5), 2541–2571 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  45. Liu, H., Han, F., Yuan, M., Lafferty, J., Wasserman, L., et al.: High-dimensional semiparametric Gaussian copula graphical models. Ann. Stat. 40 (4), 2293–2326 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  46. Jacob, L., Obozinski, G., Vert, J.-P..: Group lasso with overlap and graph lasso. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 433–440. ACM, New York (2009)

    Google Scholar 

  47. Meier, L., Van De Geer, S., Bühlmann, P.: The group lasso for logistic regression. J. R. Stat. Soc. Ser. B Stat. Methodol. 70 (1), 53–71 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  48. Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E 64 (1), 016132 (2001)

    Google Scholar 

  49. Nikolić, S., Kovačević, G., Miličević, A., Trinajstić, N.: The Zagreb indices 30 years after. Croat. Chem. Acta 76 (2), 113–124 (2003)

    Google Scholar 

  50. Diudea, M.V., Gutman, I., Jantschi, L.: Molecular Topology. Nova Science Publishers, Huntington, NY (2001)

    Google Scholar 

  51. Albert, R.: Scale-free networks in cell biology. J. Cell Sci. 118 (21), 4947–4957 (2005)

    Article  Google Scholar 

  52. Gutman, I., Li, X., Zhang, J.: Graph energy. In: Analysis of Complex Networks. From Biology to Linguistics. Wiley–VCH, Weinheim (2009)

    Book  MATH  Google Scholar 

  53. Pavlopoulos, G.A., Secrier, M., Moschopoulos, C.N., Soldatos, T.G., Kossida, S., Aerts, J., Schneider, R., Bagos, P.G., et al.: Using graph theory to analyze biological networks. BioData Min. 4 (1), 10 (2011)

    Article  Google Scholar 

  54. Wilhelm, T., Hollunder, J.: Information theoretic description of networks. Phys. A Stat. Mech. Appl. 385 (1), 385–396 (2007)

    Article  MathSciNet  Google Scholar 

  55. Kim, J., Wilhelm, T.: What is a complex graph? Phys. A Stat. Mech. Appl. 387 (11), 2637–2652 (2008)

    Article  MathSciNet  Google Scholar 

  56. Pahl-Wostl, C.: The Dynamic Nature of Ecosystems: Chaos and Order Entwined. Wiley (1995)

    Google Scholar 

  57. Okamoto, K., Chen, W., Li, X.-Y.: Ranking of closeness centrality for large-scale social networks. In: Frontiers in Algorithmics, pp. 186–195. Springer, Berlin (2008)

    Google Scholar 

  58. Raychaudhury, C., Ray, S.K., Ghosh, J.J., Roy, A.B., Basak, S.C.: Discrimination of isomeric structures using information theoretic topological indices. J. Comput. Chem. 5 (6), 581–588 (1984)

    Article  Google Scholar 

  59. MacArthur, B.D., Sánchez-García, R.J., Anderson, J.W.: Symmetry in complex networks. Discrete Appl. Math. 156 (18), 3525–3531 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  60. Mowshowitz, A., Dehmer, M.: A symmetry index for graphs. J. Math. Biophys. 30, 533–546 (2010)

    Article  MATH  Google Scholar 

  61. Gross, J.L., Yellen, J.: Handbook of Graph Theory. CRC, Boca Raton, FL (2004)

    MATH  Google Scholar 

  62. Bonchev, D.G., Rouvray, D.H.: Complexity: Introduction and Fundamentals, vol. 7. CRC, Boca Raton, FL (2003)

    MATH  Google Scholar 

  63. Bertz, S.H.: The first general index of molecular complexity. J. Am. Chem. Soc. 103 (12), 3599–3601 (1981)

    Article  Google Scholar 

  64. Devillers, J., Balaban, A.T.: Topological indices and related descriptors in QSAR and QSPAR. CRC, Boca Raton, FL (2000)

    Google Scholar 

  65. Mowshowitz, A.: Entropy and the complexity of graphs: I. an index of the relative complexity of a graph. Bull. Math. Biophys. 30 (1), 175–204 (1968)

    Google Scholar 

  66. Kim, W., Li, M., Wang, J., Pan, Y.: Biological network motif detection and evaluation. BMC Systems Biol. 5 (Suppl 3), S5 (2011)

    Article  Google Scholar 

  67. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298 (5594), 824–827 (2002)

    Article  Google Scholar 

  68. Middendorf, M., Ziv, E., Wiggins, C.H.: Inferring network mechanisms: the drosophila melanogaster protein interaction network. Proc. Natl. Acad. Sci. U. S. A. 102 (9), 3192–3197 (2005)

    Article  Google Scholar 

  69. Albert, I., Albert, R.: Conserved network motifs allow protein–protein interaction prediction. Bioinformatics 20 (18), 3346–3352 (2004)

    Article  Google Scholar 

  70. Butts, C.T., Carley, K.: Multivariate methods for interstructural analysis CASOS Working Paper. Carnegie Mellon University (2001)

    Google Scholar 

  71. Butts, C.T.: Social network analysis with sna. J. Stat. Softw. 24 (6), 1–51 (2008)

    Article  Google Scholar 

  72. Witten, D.M., Friedman, J.H., Simon, N.: New insights and faster computations for the graphical lasso. J. Comput. Graph. Stat. 20 (4), 892–900 (2011)

    Article  MathSciNet  Google Scholar 

  73. Mazumder, R., Hastie, T.: Exact covariance thresholding into connected components for large-scale graphical lasso. J. Mach. Learn. Res. 13 (1), 781–794 (2012)

    MathSciNet  MATH  Google Scholar 

  74. Luo, S., Song, R., Witten, D.: Sure screening for Gaussian graphical models (2014). arXiv preprint arXiv:1407.7819

    Google Scholar 

  75. Erdös, P., Renyi, A.: On random graphs I. Publ. Math. Debr. 6, 290–297 (1959)

    MATH  Google Scholar 

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Acknowledgements

This work was partially supported by grants from the US National Science Foundation (DMS-1161565) and the National Institute of Health (1K01HL124050-01A1).

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Authors and Affiliations

  1. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Ali Shojaie

  2. Computer Engineering Department, Iran University of Science and Technology, Tehran, Iran

    Nafiseh Sedaghat

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  1. Ali Shojaie
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  2. Nafiseh Sedaghat
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Correspondence to Ali Shojaie .

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  1. Department of Mathematics & Statistics, Brock University, St. Catherines, Ontario, Canada

    S. Ejaz Ahmed

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Shojaie, A., Sedaghat, N. (2017). How Different Are Estimated Genetic Networks of Cancer Subtypes?. In: Ahmed, S. (eds) Big and Complex Data Analysis. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-41573-4_9

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