Abstract
In this paper we present a functional Bayesian method for detecting genes which are temporally differentially expressed between several conditions. We identify the nature of differential expression (e.g., gene is differentially expressed between the first and the second sample but is not differentially expressed between the second and the third) and subsequently we estimate gene expression temporal profiles. The proposed procedure deals successfully with various technical difficulties which arise in microarray time-course experiments such as a small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements. The procedure allows to account for various types of errors, thus, offering a good compromise between nonparametric and normality assumption based techniques. In addition, all evaluations are carried out using analytic expressions, hence, the entire procedure requires very small computational effort. The performance of the procedure is studied using simulated data.
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References
Abramovich, F., Angelini, C.: Bayesian maximum a posteriori multiple testing procedure. Sankhya 68, 436–460 (2006)
Angelini, C., De Canditiis, D., Mutarelli, M., Pensky, M.: A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments. Stat. Appl. Gen. Mol. Bio. 6, Art. 24 (2007)
Angelini, C., De Canditiis, D., Pensky, M.: Bayesian Models for the Two-Sample Time-course Microarray Experiments. CSDA 53, 1547–1565 (2009)
Bar–Joseph, Z.: Analyzing time series gene expression data. Bioinformatics 20, 2493–2503 (2004)
Berger, O.J.: Statistical Decision Theory and Bayesian Analysis. Springer Series in Statistics (1985)
Conesa, A., Nueda, M.J., Ferrer, A., Talon, M.: MaSigPro: a method to identify significantly differential expression profiles in time-course microarray-experiments. Bioinformatics 22, 1096–1102 (2006)
Cui, X., Kerr, M.K., Churchill, G.A.: Transformation for cDNA Microarray Data. Stat. Appl. Gen. Mol. Bio. 2 (2002)
Gradshteyn, I.S., Ryzhik, I.M.: Tables of Integrals, Series, and Products. Academic Press, New York (1980)
Heard, N.A., Holmes, C.C., Stephens, D.A.: A quantitative study of gene regulation involved in the Immune response of Anopheline Mosquitoes: An application of Bayesian hierarchical clustering of curves. JASA 101, 18–29 (2006)
McLachlan, G., Do, K.A., Ambroise, C.: Analyzing microarray gene expression data. Wiley Series in Probability and Statistics (2004)
Müller, U., Schick, A., Wefelmeyer, W.: Estimating the error variance in nonparametric regression by a covariate-matched U-statistic. Statistics 37, 179–188 (2003)
Storey, J.D., Xiao, W., Leek, J.T., Tompkins, R.G., Davis, R.W.: Significance analysis of time course microarray experiments. PNAS 12, 12837–12842 (2005)
Tai, Y.C., Speed, T.P.: On gene ranking using replicated microarray time course data. Biometrics 65, 40–51 (2009)
Vinciotti, V., Yu, K.: M-quantile regression analysis of temporal gene expression data. Stat. Stat. Appl. Gen. Mol. Bio. 8, Art. 41 (2009)
Wit, E., McClure, J.: Statistics for Microarrays: Design, Analysis and Inference. Wiley (2004)
Yang, Y.H., Dudoit, S., Luu, P., Lin, M.D., Peng, V., Ngai, J., Speed, T.P.: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 30 (2002)
Yuan, M., Kendziorski, C.: Hidden Markov Models for microarray time course data in multiple biological conditions. JASA 101, 1323–1340 (2006)
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Angelini, C., De Canditiis, D., Pensky, M., Brownstein, N. (2012). Bayesian Models for the Multi-sample Time-Course Microarray Experiments. In: Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2011. Lecture Notes in Computer Science(), vol 7548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35686-5_3
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DOI: https://doi.org/10.1007/978-3-642-35686-5_3
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