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A Closed-Form Solution for Transcription Factor Activity Estimation Using Network Component Analysis

  • Amina Noor
  • Aitzaz Ahmad
  • Bilal Wajid
  • Erchin Serpedin
  • Mohamed Nounou
  • Hazem Nounou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8542)

Abstract

Non-iterative network component analysis (NINCA), proposed by Jacklin at.al, employs convex optimization methods to estimate the transcription factor control strengths and transcription factor activities. While NINCA provides good estimation accuracy and higher consistency, the costly optimization routine used therein renders a high computational complexity. This correspondence presents a closed form solution to estimate the connectivity matrix which is tens of times faster, and provides similar accuracy and consistency, thus making the closed form NINCA (CFNINCA) algorithm useful for large data sets encountered in practice. The proposed solution is assessed for accuracy and consistency using synthetic and yeast cell cycle data sets by comparing with the existing state-of-the-art algorithms. The robustness of the algorithm to the possible inaccuracies in prior information is also analyzed and it is observed that CFNINCA and NINCA are much more robust to erroneous prior information as compared to FastNCA.

Keywords

Gene Regulatory Network transcription factor activity convex optimization 

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References

  1. 1.
    Cai, X., Wang, X.: Stochastic modeling and simulation of gene networks. IEEE Signal Process. Mag. 24(1), 27–36 (2007)CrossRefGoogle Scholar
  2. 2.
    Shmulevich, I., Saarinen, A., Yli-Harja, O., Astola, J.: Inference of genetic regulatory networks via best-fit extensions. In: Computational and Statistical Approaches to Genomics, pp. 197–210 (2003)Google Scholar
  3. 3.
    Lähdesmäki, H., Shmulevich, I., Yli-Harja, O.: On learning gene regulatory networks under the boolean network model. Machine Learning 52(1), 147–167 (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Noor, A., Serpedin, E., Nounou, M.N., Nounou, H.N.: Inferring gene regulatory networks via nonlinear state-space models and exploiting sparsity. IEEE/ACM Trans. Comput. Biology Bioinform. 9(4), 1203–1211 (2012)CrossRefGoogle Scholar
  5. 5.
    Yang, Y.L., Suen, J., Brynildsen, M.P., Galbraith, S.J., Liao, J.C.: Inferring yeast cell cycle regulators and interactions using transcription factor activities. BMC Genomics 6(1), 90 (2005)CrossRefGoogle Scholar
  6. 6.
    Meng, J., Zhang, J.M., Chen, Y., Huang, Y.: Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks. Proteome Science 9, 1–14 (2011)CrossRefGoogle Scholar
  7. 7.
    Liao, J., Boscolo, R., Yang, Y., Tran, L., Sabatti, C., Roychowdhury, V.: Network component analysis: Reconstruction of regulatory signals in biological systems. Proceedings of the National Academy of Sciences 100(26), 15522–15527 (2003)CrossRefGoogle Scholar
  8. 8.
    Chang, C., Ding, Z., Hung, Y., Fung, P.: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data. Bioinformatics 24(11), 1349–1358 (2008)CrossRefGoogle Scholar
  9. 9.
    Jolliffe, I.T.: Principal component analysis, vol. 487. Springer, New York (1986)Google Scholar
  10. 10.
    Comon, P.: Independent component analysis. Higher-Order Statistics, 29–38 (1992)Google Scholar
  11. 11.
    Tan, M., Alshalalfa, M., Alhajj, R., Polat, F.: Influence of prior knowledge in constraint-based learning of gene regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(1), 130–142 (2011)CrossRefGoogle Scholar
  12. 12.
    Noor, A., Ahmad, A., Serpedin, E., Nounou, M., Nounou, H.: Robnca: Robust network component analysis for recovering transcription factor activities. Bioinformatics (2013)Google Scholar
  13. 13.
    Tran, L.M., Brynildsen, M.P., Kao, K.C., Suen, J.K., Liao, J.C.: gNCA: A framework for determining transcription factor activity based on transcriptome: Identifiability and numerical implementation. Metabolic Engineering 7(2), 128–141 (2005)CrossRefGoogle Scholar
  14. 14.
    Tran, L., Hyduke, D., Liao, J.: Trimming of mammalian transcriptional networks using network component analysis. BMC Bioinformatics 11(1), 511 (2010)CrossRefGoogle Scholar
  15. 15.
    Galbraith, S.J., Tran, L.M., Liao, J.C.: Transcriptome network component analysis with limited microarray data. Bioinformatics 22(15), 1886–1894 (2006)CrossRefGoogle Scholar
  16. 16.
    Jacklin, N., Ding, Z., Chen, W., Chang, C.: Noniterative convex optimization methods for network component analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9(5), 1472–1481 (2012)CrossRefGoogle Scholar
  17. 17.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004)Google Scholar
  18. 18.
    Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., et al.: Transcriptional regulatory networks in saccharomyces cerevisiae. Science Signalling 298(5594), 799 (2002)Google Scholar
  19. 19.
    Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle–regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9(12), 3273–3297 (1998)CrossRefGoogle Scholar
  20. 20.
    Wang, C., Xuan, J., Shih, I.M., Clarke, R., Wang, Y.: Regulatory component analysis: A semi-blind extraction approach to infer gene regulatory networks with imperfect biological knowledge. Signal Processing 92(8), 1902–1915 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Amina Noor
    • 1
  • Aitzaz Ahmad
    • 2
  • Bilal Wajid
    • 1
  • Erchin Serpedin
    • 1
  • Mohamed Nounou
    • 3
  • Hazem Nounou
    • 4
  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Corporate Research & DevelopmentQualcomm Technologies Inc.San DiegoUSA
  3. 3.Department of Chemical EngineeringTexas A&M University at QatarDohaQatar
  4. 4.Department of Electrical EngineeringTexas A&M University at QatarDohaQatar

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