Bioinformatics Vol. 17 no. 6 2001
Pages 509-519
© 2001 Oxford University Press
A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes
1 Department of Information and Computer
Science
2 Department of Ecology and Evolutionary
Biology, Institute for Genomics and Bioinformatics, University of
California at Irvine, Irvine, CA 92697-3425, USA
Received on September 5, 2000
; revised on February 15, 2001
; accepted on February 15, 2001
Motivation: DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data.
Results: We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model log-expression values by independent normal distributions, parameterized by corresponding means and variances with hierarchical prior distributions. We derive point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. An additional hyperparameter, inversely related to the number of empirical observations, determines the strength of the background variance. Simulations show that these point estimates, combined with a t -test, provide a systematic inference approach that compares favorably with simple t -test or fold methods, and partly compensate for the lack of replication.
Availability: The approach is implemented in software called Cyber-T accessible through a Web interface at www.genomics.uci.edu/software.html. The code is available as Open Source and is written in the freely available statistical language R.
Contact: pfbaldi{at}ics.uci.edu; tdlong{at}uci.edu
* To whom correspondence should be addressed.
3 Also at Department of Biological Chemistry, College of Medicine, University of California, Irvine.
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Y. Xie, W. Pan, and A. B. Khodursky A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data Bioinformatics, December 1, 2005; 21(23): 4280 - 4288. [Abstract] [Full Text] [PDF]
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R. Jornsten, H.-Y. Wang, W. J. Welsh, and M. Ouyang DNA microarray data imputation and significance analysis of differential expression Bioinformatics, November 15, 2005; 21(22): 4155 - 4161. [Abstract] [Full Text] [PDF]
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I. K. Fodor, D. O. Nelson, M. Alegria-Hartman, K. Robbins, R. G. Langlois, K. W. Turteltaub, T. H. Corzett, and S. L. McCutchen-Maloney Statistical challenges in the analysis of two-dimensional difference gel electrophoresis experiments using DeCyderTM Bioinformatics, October 1, 2005; 21(19): 3733 - 3740. [Abstract] [Full Text] [PDF]
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H. Ji and W. H. Wong TileMap: create chromosomal map of tiling array hybridizations Bioinformatics, September 15, 2005; 21(18): 3629 - 3636. [Abstract] [Full Text] [PDF]
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R. G. Gardner, Z. W. Nelson, and D. E. Gottschling Ubp10/Dot4p Regulates the Persistence of Ubiquitinated Histone H2B: Distinct Roles in Telomeric Silencing and General Chromatin Mol. Cell. Biol., July 15, 2005; 25(14): 6123 - 6139. [Abstract] [Full Text] [PDF]
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Y. Lu, P.-Y. Liu, P. Xiao, and H.-W. Deng Hotelling's T2 multivariate profiling for detecting differential expression in microarrays Bioinformatics, July 15, 2005; 21(14): 3105 - 3113. [Abstract] [Full Text] [PDF]
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G. J. Weber, S. E. Choe, K. A. Dooley, N. N. Paffett-Lugassy, Y. Zhou, and L. I. Zon Mutant-specific gene programs in the zebrafish Blood, July 15, 2005; 106(2): 521 - 530. [Abstract] [Full Text] [PDF]
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A. Hsiao, T. Ideker, J. M. Olefsky, and S. Subramaniam VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data Nucleic Acids Res., July 1, 2005; 33(suppl_2): W627 - W632. [Abstract] [Full Text] [PDF]
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M. Psarros, S. Heber, M. Sick, G. Thoppae, K. Harshman, and B. Sick RACE: Remote Analysis Computation for gene Expression data Nucleic Acids Res., July 1, 2005; 33(suppl_2): W638 - W643. [Abstract] [Full Text] [PDF]
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Y. Liang, B. Tayo, X. Cai, and A. Kelemen Differential and trajectory methods for time course gene expression data Bioinformatics, July 1, 2005; 21(13): 3009 - 3016. [Abstract] [Full Text] [PDF]
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S. A. Gharib, D. L. Luchtel, D. K. Madtes, and R. W. Glenny Global gene annotation analysis and transcriptional profiling identify key biological modules in hypoxic pulmonary hypertension Physiol Genomics, June 16, 2005; 22(1): 14 - 23. [Abstract] [Full Text] [PDF]
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A. A. Sharov, D. B. Dudekula, and M. S. H. Ko A web-based tool for principal component and significance analysis of microarray data Bioinformatics, May 15, 2005; 21(10): 2548 - 2549. [Abstract] [Full Text] [PDF]
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T. Hulf, P. Bellosta, M. Furrer, D. Steiger, D. Svensson, A. Barbour, and P. Gallant Whole-Genome Analysis Reveals a Strong Positional Bias of Conserved dMyc-Dependent E-Boxes Mol. Cell. Biol., May 1, 2005; 25(9): 3401 - 3410. [Abstract] [Full Text] [PDF]
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G. K. Smyth, J. Michaud, and H. S. Scott Use of within-array replicate spots for assessing differential expression in microarray experiments Bioinformatics, May 1, 2005; 21(9): 2067 - 2075. [Abstract] [Full Text] [PDF]
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