Abstract
This paper proposes a statistical method for significance analysis of time-course gene expression profiles, called SATgene. The SATgene models time-dependent gene expression profiles by autoregressive equations plus Gaussian noises, and time-independent gene expression profiles by constant numbers plus Gaussian noises. The statistical F-testing for regression analysis is used to calculate the confidence probability (significance level) that a time-course gene expression profile is not time-independent. The user can use this confidence probability to select significantly expressed genes from a time-course gene expression dataset. Both one synthetic dataset and one biological dataset were employed to evaluate the performance of the SATgene, compared to traditional gene selection methods: the pairwise R-fold change method and the standard deviation method. The results show that the SATgene outperforms the traditional methods.
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References
Iyer, V.R., et al.: The transcript-ional program in the response of human fibroblasts to serum. Science 283, 83–87 (1999)
Gasch, A.P., et al.: Genomic expression programs in the response of yeast cells to environmental changes. Molecular Biology of the Cell 11, 4241–4257 (2000)
Spellman, P.T., et al.: Comprehensive identification of cell cycle-regulated genes of the Yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9, 3273–3297 (1998)
Whitfield, M.L., et al.: Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Molecular Biology of the Cell 13, 1977–2000 (2002)
Laub, M.T., et al.: Global analysis of the genetic network controlling a bacteria cell cycle. Science 290, 2144–2148 (2000)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Eisen, M.B., et al.: Cluster Analysis and Display of Genome-Wide Expression Patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)
Bar-Joseph, Z.: Analyzing time series gene expression data. Bioinformatics 20, 2493–2503 (2004)
Schliep, A., et al.: Analyzing gene expression time-course. IEEE/ACM Trans. on Computational Biology and Bioinformatics 2, 179–193 (2005)
Baldi, P., Hatfield, G.W.: DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling. Cambridge University Press, Cambridge (2002)
Alizadeh, A.A., et al.: Distinct types of diffuse large B-cell Lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Claverie, J.M.: Computational methods for the identification of differential and coordinated gene expression. Human Molecular Genetic 8, 1821–1832 (1999)
Tusher, V.G., et al.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001)
Smyth, G.K., Michaud, J., Scott, H.S.: Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21, 2067–2075 (2005)
Ly, D.H., et al.: Mitotic misregulation and human aging. Science 287, 2486–2492 (2000)
Melnik, R.V.N.: Dynamic system evolution and Markov chain approximation. Discrete Dynamics in Nature and Society 2, 7–39 (1998)
Ramoni, M.F., et al.: Cluster analysis of gene expression dynamics. Proc. Natl. Acad. Sci. USA 99, 9121–9912 (2002)
Wu, F.X., Zhang, W.J., Kusalik, A.J.: Dynamic model-based clustering for time-course gene expression data. Journal of Bioinformatics and Computational Biology 3, 821–836 (2005)
Harvey, A.C.: Time Series Models. MIT Press, Cambridge (1993)
Seber, G.A.F., Lee, A.J.: Linear Regression Analysis, 2nd edn. Wiley Interscience, Hoboken (2003)
Yeung, K.Y.: Model-Based clustering and data transformations for gene expression data. Bioinformatics 17, 977–987 (2001)
Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach, 2nd edn. The MIT Press, Cambridge (2001)
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Wu, FX. (2007). Significance Analysis of Time-Course Gene Expression Profiles. In: Măndoiu, I., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science(), vol 4463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72031-7_2
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DOI: https://doi.org/10.1007/978-3-540-72031-7_2
Publisher Name: Springer, Berlin, Heidelberg
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