Abstract
Expression profiling of time-series experiments is widely used to study biological systems. However, determining the quality of the resulting profiles remains a fundamental problem. Because of inadequate sampling rates, the effect of arrest-and-release methods and loss of synchronization, the measurements obtained from a series of time points may not accurately represent the underlying expression profiles. To solve this, we propose an approach that combines time-series and static (average) expression data analysis—for each gene, we determine whether its temporal expression profile can be reconciled with its static expression levels. We show that by combining synchronized and unsynchronized human cell cycle data, we can identify many cycling genes that are missed when using only time-series data. The algorithm also correctly distinguishes cycling genes from genes that specifically react to an environmental stimulus even if they share similar temporal expression profiles. Experimental validation of these results shows the utility of this analytical approach for determining the accuracy of gene expression patterns.
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References
Gasch, A.P. et al. Genomic expression programs in the response of yeast cells to environmental change. Mol. Biol. Cell 11, 4241–4257 (2000).
Nau, G.J. et al. Human macrophage activation programs induced by bacterial pathogens. Proc. Natl. Acad. Sci. USA 99, 1503–1508 (2002).
Arbeitman, M.N. et al. Gene expression during the life cycle of Drosophila melanogaster. Science 298, 2270–2275 (2002).
Bar-Joseph, Z., Gerber, G., Jaakkola, T.S., Gifford, D.K. & Simon, I. Comparing the continuous representation of time series expression profiles to identify differentially expressed genes. Proc. Natl. Acad. Sci. USA 100, 10146–10151 (2003).
Storch, K.F. et al. Extensive and divergent circadian gene expression in liver and heart. Nature 418, 78–83 (2002).
Causton, H.C. et al. Remodeling of yeast genome expression in response to environmental changes. Mol. Biol. Cell 12, 323–337 (2001).
Pramilla, T., Miles, S., GuhaThakurta, D., Jemiolo, D. & Breeden, L.L. Conserved homeodomain proteins interact with mads box protein mcm1 to restrict ecb-dependent transcription to the m/g1 phase of the cell cycle. Genes Dev. 16, 3034–3045 (2002).
Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998).
Ishida, S. et al. Role for e2f in control of both DNA replication and mitotic functions as revealed from DNA microarray analysis. Mol. Cell Biol. 21, 4684–4699 (2001).
Iyer, V.R. et al. The transcriptional program in the response of human fibroblasts to serum. Science 283, 83–87 (1999).
Whitfield, M.L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977–2000 (2002).
Shedden, K. & Cooper, S. Analysis of cell-cycle gene expression in Saccharomyces cerevisiae using microarrays and multiple synchronization methods. Nucleic Acids Res, 30, 2920–2929 (2002).
Wichert, S., Fokianos, K. & Strimmer, K. Identifying periodically expressed transcripts in microarray time series data. Bioinformatics 20, 5–20 (2004).
Bar-Joseph, Z. et al. Computational discovery of gene modules and regulatory networks. Nat. Biotech. 21, 1337–1342 (2003).
Lee, T.I. et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 798, 799–804 (2002).
Bar-Joseph, Z., Gerber, G., Jaakkola, T.S., Gifford, D.K. & Simon, I. Continuous representations of time series gene expression data. J. Comput. Biol. 10, 341–356 (2003).
Chang, H.Y. et. al. Gene expression signature of serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2, E7 (2004).
Morris, G.F. & Mathews, M.B. Regulation of proliferating cell nuclear antigen during the cell cycle. J. Biol. Chem. 264, 13856–13864 (1989).
Goswami, P.C., Roti, J.L.R. & Hunt, C.R. The cell cycle-coupled expression of topoisomerase II alpha during S phase is regulated by mRNA stability and is disrupted by heat shock or ionizing radiation. Mol. Cell Biol. 16, 1500–1508 (1996).
Tobey, R.A., Valdez, J.G. & Crissman, H.A. Synchronization of human diploid fibroblasts at multiple stages of the cell cycle. Exp. Cell Res. 179, 400–416 (1988).
Qian, J., Dolled-Filhart, M., Lin, J., Yu, H. & Gerstein, M. Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions. J. Mol. Biol. 314, 1053–1066 (2001).
Ernst, J., Nau, G.J. & Bar-Joseph Z. Clustering short time series gene expression data. Bioinformatics 21, I159–I168 (2005).
Bar-Joseph, Z. Analyzing time series gene expression data. Bioinformatics 20, 2493–2503 (2004).
Zhao, L.P., Prentice, R. & Breeden, L. Statistical modeling of large microarray data sets to identify stimulus-response profiles. Proc. Natl. Acad. Sci. USA 98, 5631–5636 (2001).
Aach, J. & Church, G.M. Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495–508 (2001).
Bar-Joseph, Z., Farkash, S., Gifford, D.K., Simon, I. & Rosenfeld, R. Deconvolving cell cycle expression data with complementary information. Bioinformatics 20, I23–I30 (2004).
Lu, X., Zhang, W., Qin, Z.S., Kwast, K.E. & Liu, J.S. Statistical resynchronization and Bayesian detection of periodically expressed genes. Nucl. Acids. Res. 32, 447–455 (2004).
Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998).
Ramoni, M.F., Sebastiani, P. & Kohane, I.S. Cluster analysis of gene expression dynamics. Proc. Natl. Acad. Sci. USA 99, 9121–9126 (2002).
Schliep, A., Schonhuth, A. & Steinhoff, C. Using hidden markov models to analyze gene expression time course data. Bioinformatics 19, I264–I272 (2003).
Kim, S.V., Imoto, S. & Miyano, S. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief. Bioinform. 4, 228–235 (2003).
Acknowledgements
We thank Tommi Jaakkola, Larry Wasserman, Naftali Kaminski and the anonymous reviewers for comments on earlier versions. Z.B.J. is partially funded by National Science Fund Foundation CAREER award 0448453 and by the Pennsylvania Department of Health.
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Supplementary Table 1
S1 genes. (TXT 65 kb)
Supplementary Table 2
S2 genes. (TXT 9 kb)
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Simon, I., Siegfried, Z., Ernst, J. et al. Combined static and dynamic analysis for determining the quality of time-series expression profiles. Nat Biotechnol 23, 1503–1508 (2005). https://doi.org/10.1038/nbt1164
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DOI: https://doi.org/10.1038/nbt1164
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