The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.02 - March/April (2012 vol.9)
pp: 451-466
O. ElBakry , Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC, Canada
M. O. Ahmad , Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC, Canada
M. N. S. Swamy , Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC, Canada
ABSTRACT
The regulation of gene expression is a dynamic process, hence it is of vital interest to identify and characterize changes in gene expression over time. We present here a general statistical method for detecting changes in microarray expression over time within a single biological group and is based on repeated measures (RM) ANOVA. In this method, unlike the classical F-statistic, statistical significance is determined taking into account the time dependency of the microarray data. A correction factor for this RM F-statistic is introduced leading to a higher sensitivity as well as high specificity. We investigate the two approaches that exist in the literature for calculating the p-values using resampling techniques of gene-wise p-values and pooled p-values. It is shown that the pooled p-values method compared to the method of the gene-wise p-values is more powerful, and computationally less expensive, and hence is applied along with the introduced correction factor to various synthetic data sets and a real data set. These results show that the proposed technique outperforms the current methods. The real data set results are consistent with the existing knowledge concerning the presence of the genes. The algorithms presented are implemented in R and are freely available upon request.
INDEX TERMS
Decision support systems, Analysis of variance, Yttrium, Histograms, Bioinformatics, Computational biology, Gene expression,variance moderation., ANOVA, microarray data analysis, permutation, time-course data
CITATION
O. ElBakry, M. O. Ahmad, M. N. S. Swamy, "Identification of Differentially Expressed Genes for Time-Course Microarray Data Based on Modified RM ANOVA", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 2, pp. 451-466, March/April 2012, doi:10.1109/TCBB.2011.65
REFERENCES
[1] V.G. Tusher, R. Tibshirani, and G. Chu, “Significance Analysis of Microarrays Applied to the Ionizing Radiation Response,” Proc. Nat'l Academy of Sciences USA, vol. 98, pp. 5116-5121, Apr. 2001.
[2] T. Park, S. Yi, S. Lee, S. Lee, D. Yoo, J. Ahn, and Y. Lee, “Statistical Tests for Identifying Differentially Expressed Genes in Time-Course Microarray Experiments.” Bioinformatics, vol. 19, pp. 694-703, Apr. 2003.
[3] S.D. Peddada, E.K. Lobenhofer, L. Li, C.A. Afshari, C.R. Weinberg, and D.M. Umbach, “Gene Selection and Clustering for Time-Course and Dose-Response Microarray Experiments Using Order-Restricted Inference,” Bioinformatics, vol. 19, pp. 834-841, May 2003.
[4] J.D. Storey, W. Xiao, J.T. Leek, R.G. Tompkins, and R.W. Davis, “Significance Analysis of Time Course Microarray Experiments,” Proc. Nat'l Academy of Sciences USA, vol. 102, pp. 12837-12842, Sept. 2005.
[5] Y. Tai and T. Speed, “A Multivariate Empirical Bayes Statistic for Replicated Microarray Time Course Data,” Annals of Statistics, vol. 34, pp. 2387-2412, 2006.
[6] C. Angelini, D. De Canditiis, M. Mutarelli, and M. Pensky, “A Bayesian Approach to Estimation and Testing in Time-Course Microarray Experiments,” Statistical Applications in Genetics and Molecular Biology, vol. 6, no. 1,article 24, 2007.
[7] C. Angelini, L. Cutillo, D. De Canditiis, M. Mutarelli, and M. Pensky, “BATS: A Bayesian User-Friendly Software for Analyzing Time Series Microarray Experiments,” BMC Bioinformatics, vol. 9, p. 415, Oct. 2008.
[8] P. Baldi and A.D. Long, “A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-test and Statistical Inferences of Gene Changes,” Bioinformatics, vol. 17, pp. 509-519, June 2001.
[9] B. Efron, R. Tibshirani, J. Storey, and V. Tusher, “Empirical Bayes Analysis of a Microarray Experiment,” J. Am. Statistical Assoc., vol. 96, pp. 1151-1160, 2001.
[10] P. Broberg, “Statistical Methods for Ranking Differentially Expressed Genes,” Genome Biology, vol. 4, R41, 2003.
[11] G. Smyth, “Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments,” Statistical Applications in Genetics and Molecular Biology, vol. 3, no. 1,article 3, 2004.
[12] X. Cui, G. Hwang, J. Qiu, N. Blades, and G. Churchill, “Improved Statistical Tests for Differential Gene Expression by Shrinking Variance Components Estimates,” Biostatistics, vol. 6, pp. 59-75, Jan. 2005.
[13] G. Wright and R. Simon, “A Random Variance Model for Detection of Differential Gene Expression in Small Microarray Experiments,” Bioinformatics, vol. 19, pp. 2448-2455, Dec. 2003.
[14] J.D. Storey and R. Tibshirani, “Statistical Significance for Genomewide Studies,” Proc. Nat'l Academy of Sciences USA, vol. 100, pp. 9440-9445, Aug. 2003.
[15] S. Dudoit, Y. Yang, T. Speed, and M. Callow, “Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments,” Statistica Sinica, vol. 12, pp. 111-140, 2002.
[16] E.S. Edgington, Randomization Tests. CRC Press, 1995.
[17] E.R. Girden, ANOVA: Repeated Measures. SAGE, 1992.
[18] S. Geisser and S.W. Greenhouse, “An Extension of Box's Results on the Use of the F Distribution in Multivariate Analysis,” Annals of Math. Statistics, vol. 29, pp. 885-891, 1958.
[19] B. Efron and R. Tibshirani, An Introduction to the Bootstrap. Chapman & Hall/CRC, 1993.
[20] F.J. Anscombe, “Sequential Estimation,” J. Royal Statistical Soc., vol. 15, pp. 1-29, 1953.
[21] N. Mukhopadhyay, S. Datta, and S. Chattopadhyay, Applied Sequential Methodologies: Real-World Examples with Data Analysis. CRC Press, 2004.
[22] E.K. Lobenhofer, L. Bennett, P.L. Cable, L. Li, P.R. Bushel, and C.A. Afshari, “Regulation of DNA Replication Fork Genes by 17{Beta}-Estradiol,” Molecular Endocrinology, vol. 16, pp. 1215-1229, June 2002.
[23] M.T. Lee, F.C. Kuo, G.A. Whitmore, and J. Sklar, “Importance of Replication in Microarray Gene Expression Studies: Statistical Methods and Evidence from Repetitive cDNA Hybridizations,” Proc. Nat'l Academy of Sciences USA, vol. 97, pp. 9834-9839, Aug. 2000.
[24] J. Adamski, Z. Ma, S. Nozell, and E.N. Benveniste, “17{Beta}-Estradiol Inhibits Class II Major Histocompatibility Complex (MHC) Expression: Influence on Histone Modifications and CBP Recruitment to the Class II MHC Promoter,” Molecular Endocrinology, vol. 18, pp. 1963-1974, Aug. 2004.
[25] M.T. Stang, M.J. Armstrong, G.A. Watson, K.Y. Sung, Y. Liu, B. Ren, and J.H. Yim, “Interferon Regulatory Factor-1-Induced Apoptosis Mediated by a Ligand-independent Fas-Associated Death Domain Pathway in Breast Cancer Cells,” Oncogene, vol. 26, pp. 6420-6430, Apr. 2007.
[26] J. Dejmek, A. Safholm, C. Kamp Nielsen, T. Andersson, and K. Leandersson, “Wnt-5a/Ca2+-Induced NFAT Activity Is Counteracted by Wnt-5a/Yes-Cdc42-Casein Kinase 1{Alpha} Signaling in Human Mammary Epithelial Cells,” Molecular Cellular Biology, vol. 26, pp. 6024-6036, Aug. 2006.
[27] T. Dubois, S. Howell, E. Zemlickova, and A. Aitken, “Identification of Casein Kinase Ialpha Interacting Protein Partners,” FEBS Letters, vol. 517, pp. 167-171, Apr. 2002.
[28] E. Woo, D.G. Jeong, M. Lim, S. Jun Kim, K. Kim, S. Yoon, B. Park, and S. Eon Ryu, “Structural Insight into the Constitutive Repression Function of the Nuclear Receptor Rev-erb&beta,” J. Molecular Biology, vol. 373, pp. 735-744, 2007.
[29] W. Tang, M. Norlin, and K. Wikvall, “Regulation of Human CYP27A1 by Estrogens and Androgens in HepG2 and Prostate Cells,” Archives of Biochemistry and Biophysics, vol. 462, pp. 13-20, June 2007.
[30] F. Martínez-Arribas, D. Agudo, M. Pollán, F. Gómez-Esquer, G. Díaz-Gil, R. Lucas, and J. Schneider, “Positive Correlation between the Expression of X-Chromosome RBM Genes (RBMX, RBM3, RBM10) and the Proapoptotic Bax Gene in Human Breast Cancer,” J. Cellular Biochemistry, vol. 97, pp. 1275-1282, 2006.
[31] P. Rousseeuw, “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis,” J. Computational and Applied Math., vol. 20, pp. 53-65, 1987.
36 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool