Abstract
We consider a systems biology problem of reconstructing gene regulatory network from time-course gene expression microarray data, a special blind source separation problem for which conventional methods cannot be applied. Network component analysis (NCA), which makes use of the structural information of the mixing matrix, is a tailored method for this specific blind source separation problem. In this paper, a new NCA method called nonnegative NCA (nnNCA) is proposed to take into account of the non-negativity constraint on the mixing matrix that is based on a reasonable biological assumption. The nnNCA problem is formulated as a linear programming problem which can be solved effectively. Simulation results on spectroscopy data and experimental results on time-course microarray data of yeast cell cycle demonstrate the effectiveness and anti-noise robustness of the proposed nnNCA method.
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References
Savageau, M.A.: Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology. Addison-Wesley, Reading (1976)
Liebermeister, W.: Linear modes of gene expression determined by independent component analysis. Bioinformatics 18(1), 51–60 (2002)
Lee, S.I., Batzoglou, S.: Application of independent component analysis to microarrays. Genome Biology 4(11), 76 (2003)
Abrard, F., Deville, Y.: Blind separation of dependent sources using the “time-frequency ratio of mixtures” approach. In: Proceedings of Seventh International Symposium on Signal Processing and Its Applications, pp. 81–84 (July 2003)
Chang, C.Q., Ren, J., Fung, P., Hung, Y., Shen, J., Chan, F.: Novel sparse component analysis approach to free radical EPR spectra decomposition. Journal of Magnetic Resonance 175(2), 242–255 (2005)
Liao, J.C., Boscolo, R., Yang, Y.L., Tran, L.M., Sabatti, C., Roychowdhury, V.P.: Network component analysis: Reconstruction of regulatory signals in biological systems. Proceedings of the National Academy of Sciences of the United States of America 100(26), 15522–15527 (2003)
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, 128–141 (2005)
Chang, C.Q., Ding, Z., Hung, Y.S., Fung, P.C.W.: Fast network component analysis for gene regulation networks. In: Proc. 2007 IEEE International Workshop on Machine Learning for Signal Processing, Thesaloniki, Greece (2007)
Chang, C.Q., Ding, Z., Hung, Y.S., Fung, P.C.W.: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data. Bioinformatics 24, 1349 (2008)
Alon, U.: An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC (2007)
Golub, G.H., van Loan, C.F.: Matrix Computation, 3rd edn. The Johns Hopkins University Press (1996)
Dantzig, G.: Linear Programming and Extensions. Princeton Univ. Pr., Princeton (1963)
Luenberger, D.: Introduction to Linear and Nonlinear Programming. Addison-Wesley Pub. Co., Reading (1973)
GNU: GLPK (GNU Linear Programming Kit) [web page and software] (2008), http://www.gnu.org/software/glpk/glpk.html
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botsein, 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)
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., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K., Young, R.A.: Transcriptional regulatory networks in saccharomyces cerevisiae. Science 298(5594), 799–804 (2002)
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Chang, C., Ding, Z., Hung, Y.S. (2009). Nonnegative Network Component Analysis by Linear Programming for Gene Regulatory Network Reconstruction. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_50
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DOI: https://doi.org/10.1007/978-3-642-00599-2_50
Publisher Name: Springer, Berlin, Heidelberg
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