Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2003 | 13 | 3 | 337-345

Tytuł artykułu

Properties of a singular value decomposition based dynamical model of gene expression data


Treść / Zawartość

Warianty tytułu

Języki publikacji



Recently, data on multiple gene expression at sequential time points were analyzed using the Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by the fitting of a linear discrete-time dynamical system in which the expression values at a given time point are linear combinations of the values at a previous time point. We attempt to address several aspects of the method. To obtain the model, we formulate a nonlinear optimization problem and present how to solve it numerically using the standard MATLAB procedures. We use freely available data to test the approach. We discuss the possible consequences of data regularization, called sometimes ``polishing'', on the outcome of the analysis, especially when the model is to be used for prediction purposes. Then, we investigate the sensitivity of the method to missing measurements and its abilities to reconstruct the missing data. Summarizing, we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics, but may also lead to unexpected difficulties, like overfitting problems.








Opis fizyczny




  • Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland


  • Alter O., Brown P.O., and Botstein D. (2000): Singular value decomposition for genome-wide expression data processing and modeling. - Proc. Natl. Acad. Sci., Vol. 97, No. 18, pp. 10101-10106.
  • Alter O., Brown P.O. and Botstein D. (2001): Processing and modelinggenome-wide expression data using singular value decomposition. - Proc. SPIE , Vol. 4266, No. 2, pp. 171-186.
  • Bellman R. (1960): Introduction to Matrix Analysis. - New York: McGraw-Hill.
  • Branch M.A. and Grace A. (1996): Matlab Optimization Toolbox. User's Guide. - Natick, MA: MathWorks.
  • Everitt B.S. and Dunn G. (2001): Applied Multivariate Data Analysis. - NewYork: Oxford University Press.
  • Golub G.H. and van Loan C.F. (1996): Matrix Computations. -Baltimore: Johns Hopkins University Press.
  • Holter N.S., Mitra M., Maritan A., Cieplak M., Banavar J.R. and Fedoroff N.V. (2000): Fundamental patterns underlying gene expression profiles: Simplicity from complexity. - Proc. Natl. Acad. Sci., Vol. 97, No. 15, pp. 8409-8414.
  • Holter N.S., Mitra M., Maritan A., Cieplak M., Fedoroff N.V. and Banavar J.R. (2001): Dynamic modeling of gene expression data. - Proc. Natl. Acad. Sci, Vol. 98, No. 4, pp. 1693-1698.
  • Jackson J.E. (1991): A User's Guide to Principal Components. - NewYork: Wiley.
  • Kim S., Dougherty E.R., Bittner M.L., Chen Y., Krishnamoorthy S., Meltzer P. and Trent J.M. (2001): General nonlinear framework for the analysis of gene interaction via multivariate expression arrays. - J. Biomed. Optics, Vol. 5, No. 4, pp. 411-424.
  • Radmacher M.D., Simon R., Desper R., Taetle R., Schaffer A.A. and Nelson M.A. (2001): Graph models of oncogenesis with an application to melanoma. - J. Theor. Biol., Vol. 212, No. 4, pp. 535-548.
  • Raychaudhuri S., Stuart J.M. and Altman R. (2000): Principal componentsanalysis to summarize microarray experiments: Application to sporulation timeseries. - Proc. Pac. Symp. Biocomput'2000, Singapore: World Scientific, pp. 455-466.
  • Spellman P.T., Sherlock G., Zhang M.Q., Iyer V.R., Anders K., Eisen M.B., Brown P.O., Botstein D. and Futcher B. (1998): Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae bymicroarray hybridization. - Mol. Biol. Cell, Vol. 9, No. 12, pp. 3273-3297.
  • Velculescu V.E., Zhang L., Vogelstein B. and Kinzler K.W. (1995): Serial analysis of gene expression. - Science, Vol. 270, No. 5235, pp. 484-487.
  • Vogelstein B., Fearon E.R., Hamilton S.R., Kern S.E., Preisinger A.C., Leppert M., Nakamura Y., White R., Smits A.M. and Bos J.L. (1988): Genetic alterations during colorectal-tumor development. - N. Engl. J. Med., Vol. 319, No. 9, pp. 525-532.
  • Wall M.E., Dyck P.A. and Brettin T.S. (2001): SVDMAN-singular valuede composition analysis of microarray data. - Bioinformatics, Vol. 17, No. 6, pp. 566-568.
  • Watkins D.S. (1991): Fundamentals of Matrix Computations. - New York: Wiley.

Typ dokumentu



Identyfikator YADDA

JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.