Nonnegative Network Component Analysis by Linear Programming for Gene Regulatory Network Reconstruction
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.
Unable to display preview. Download preview PDF.
- 4.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)Google Scholar
- 8.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)Google Scholar
- 10.Alon, U.: An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC (2007)Google Scholar
- 11.Golub, G.H., van Loan, C.F.: Matrix Computation, 3rd edn. The Johns Hopkins University Press (1996)Google Scholar
- 14.GNU: GLPK (GNU Linear Programming Kit) [web page and software] (2008), http://www.gnu.org/software/glpk/glpk.html
- 15.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)CrossRefGoogle Scholar
- 16.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)CrossRefGoogle Scholar