NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources

  • Kriti Puniyani
  • Eric P. Xing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7821)

Abstract

Inference of gene interaction networks from expression data usually focuses on either supervised or unsupervised edge prediction from a single data source. However, in many real world applications, multiple data sources, such as microarray and ISH measurements of mRNA abundances, are available to offer multi-view information about the same set of genes. We propose NP-MuScL (nonparanormal multi-source learning) to estimate a gene interaction network that is consistent with such multiple data sources, which are expected to reflect the same underlying relationships between the genes. NP-MuScL casts the network estimation problem as estimating the structure of a sparse undirected graphical model. We use the semiparametric Gaussian copula to model the distribution of the different data sources, with the different copulas sharing the same precision (i.e., inverse covariance) matrix, and we present an efficient algorithm to estimate such a model in the high dimensional scenario. Results are reported on synthetic data, where NP-MuScL outperforms baseline algorithms significantly, even in the presence of noisy data sources. Experiments are also run on two real-world scenarios: two yeast microarray data sets, and three Drosophila embryonic gene expression data sets, where NP-MuScL predicts a higher number of known gene interactions than existing techniques.

Keywords

interaction networks gene expression multi-source learning sparsity Gaussian graphical models nonparanormal copula 

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References

  1. 1.
    Segal, E., Koller, D., Friedman, N.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics 34, 166–176 (2003)CrossRefGoogle Scholar
  2. 2.
    Basso, K., Magolin, A., Califano, A.: Reverse engineering of regulatory networks in human b cells. Nature Genetics 37, 382–390 (2005)CrossRefGoogle Scholar
  3. 3.
    Morrissey, E.R., Juárez, M.A., Denby, K.J., Burroughs, N.J.: On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics 26(18), 2305–2312 (2010)CrossRefGoogle Scholar
  4. 4.
    Carro, M.S., Califano, A., Iavarone, A.: The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318–325 (2010)CrossRefGoogle Scholar
  5. 5.
    Wang, K., Saito, M., Califano, A.: Genome-wide identification of post-translational modulators of transcription factor activity in human b-cells. Nature Biotechnology 27(9), 829–839 (2009)CrossRefGoogle Scholar
  6. 6.
    Meinshausen, N., Bühlmann, P.: High-dimensional graphs and variable selection with the lasso. Annals of Statistics (2006)Google Scholar
  7. 7.
    Banerjee, O., Ghaoui, L.E., d’Aspremont, A., Natsoulis, G.: Convex optimization techniques for fitting sparse gaussian graphical models. In: ICML (2006)Google Scholar
  8. 8.
    Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics (2007)Google Scholar
  9. 9.
    Ben-Hur, A., Noble, W.S.: Kernel methods for predicting protein–protein interactions. In: ISMB, vol. 21, pp. i38–i46 (2005)Google Scholar
  10. 10.
    Wang, Y., Joshi, T., Zhang, X.S., Xu, D., Chen, L.: Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 22(19), 2413–2420 (2006)CrossRefGoogle Scholar
  11. 11.
    Ahmed, A., Xing, E.P.: Tesla: Recovering time-varying networks of dependencies in social and biological studies. Proc. Natl. Acad. Sci. 106, 11878–11883 (2009)CrossRefGoogle Scholar
  12. 12.
    Xu, Q., Hu, D.H., Yang, Q., Xue, H.: Simpletrppi: A simple method for transferring knowledge between interaction networks for ppi prediction. In: Bioinformatics and Biomedicine Workshops (2012)Google Scholar
  13. 13.
    Katenka, N., Kolaczyk, E.D.: Inference and characterization of multi-attribute networks with application to computational biology. Arxiv (2012)Google Scholar
  14. 14.
    Honorio, J., Samaras, D.: Multi-task learning of gaussian graphical models. In: ICML (2011)Google Scholar
  15. 15.
    Rothman, A.J., Bickel, P.J., Levina, E., Zhu, J.: Sparse permutation invariant covariance estimation. Electronic Journal of Statistics 2 (2008)Google Scholar
  16. 16.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Statist. Soc. B 58(1), 267–288 (1996)MathSciNetMATHGoogle Scholar
  17. 17.
    Ravikumar, P., Liu, H., Lafferty, J., Wasserman, L.: Spam: Sparse additive models. In: NIPS (2007)Google Scholar
  18. 18.
    Liu, H., Lafferty, J., Wasserman, L.: The nonparanormal: Semiparametric estimation of high dimensional undirected graphs. Journal of Machine Learning Research 10, 2295–2328 (2009)MathSciNetMATHGoogle Scholar
  19. 19.
    Balakrishnan, S., Puniyani, K., Lafferty, J.: Sparse additive functional and kernel cca. In: ICML (2012)Google Scholar
  20. 20.
    Cho, R., Campbell, M., Winzeler, E., Davis, R.: A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2(1), 65–73 (1998)CrossRefGoogle Scholar
  21. 21.
    Hughes, T., Marton, M., Jones, A., Roberts, C., Friend, S.: Functional discovery via a compendium of expression profiles. Cell 102(1) (2000)Google Scholar
  22. 22.
    Hibbs, M., Hess, D., Myers, C., Troyanskaya, O.: Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics (2007)Google Scholar
  23. 23.
    Stark, C., Breitkreutz, B., Chatr-Aryamontri, A., Boucher, L., Tyers, M.: The biogrid interaction database: update. Nucleic Acids Res. 39(D), 698–704 (2011)Google Scholar
  24. 24.
    Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: CIKM (2003)Google Scholar
  25. 25.
    Tomancak, P., Beaton, A., Weiszmann, R., Kwan, E., Shu, S., Lewis, S., Richards, S., Celniker, S., Rubin, G.: Systematic determination of patterns of gene expression during drosophila embryogenesis. Genome Biol. 3(2), 14 (2002)CrossRefGoogle Scholar
  26. 26.
    Puniyani, K., Xing, E.P.: Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 72–85. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kriti Puniyani
    • 1
  • Eric P. Xing
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityUSA

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