Skip to main content
SpringerLink
Log in

Statistical Analysis of Time Course Microarray Data

  • Chapter
  • First Online:
Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

  • 4113 Accesses

Abstract

Time course gene expression experiments have proved valuable in a variety of studies. Their unique data structure and the diversity of tasks often associated with them present new challenges to statistical analysis. In this report, we give a brief review of several primary questions pertaining to such experiments and popular statistical tools to address them.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bar-Joseph, Z., Gerber, G., Gifford, D. K., Jaakkola, T. S., & Simon, I. (2003). Continous representations of time-series gene expression data. Journal of Computational Biology, 10, 341–356.

    Article  Google Scholar 

  2. Do, K.-A., Mueller, P., & Vanucci, M. (Eds.). (2006). Bayesian inference for gene expression and proteomics. Cambridge University Press.

    Google Scholar 

  3. Diggle, P. J., Heagerty, P., Liang, K.-Y., & Zeger, S. L. (2002). Analysis of longintudinal data (2nd ed.). New York: Oxford University Press.

    Google Scholar 

  4. Edwards, M. G., Sarkar, D., Klopp, R., Morrow, J. D., Weindruch, R., & Prolla, T. A. (2003). Age-related impairment of the transcriptional response to oxidative stress in the mouse heart. Physiological Genomics, 13, 119–127.

    Google Scholar 

  5. Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, 95, 14863–14868.

    Article  Google Scholar 

  6. Ernst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21, i159–i168.

    Article  Google Scholar 

  7. Hong, F., & Li, H. (2006). Functional hierarchical models for identifying genes with different time-course expression profiles. Biometrics, 62, 534–544.

    Article  MathSciNet  MATH  Google Scholar 

  8. Holter, N. S., Mitra, M., Maritan, A., Cieplak, M., Banavar, J. R., & Fedoroff, N. V. (2000). Fundamental patterns underlying gene expression profiles: Simplicity from complexity. Proc. Natl. Acad. Sci. USA, 97, 8409–8414.

    Article  Google Scholar 

  9. Ihaka, R., & Gentleman, R. (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5, 299–314.

    Google Scholar 

  10. Kaech, S.M., Hemby, S., Kersh, E., & Ahmed, R. (2002). Molecular and functional profiling of memory CD8 T cell differentiation. Cell, 111, 837–851.

    Article  Google Scholar 

  11. Luan, Y., & Li, H. (2003). Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics, 19(4), 474–482.

    Article  Google Scholar 

  12. Park, T., Yi, S. G., & Lee, S. (2003). Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics, 19, 694–703.

    Article  Google Scholar 

  13. Parmigiani, G., Garrett, E., Irizarry, R., & Zeger, S. (2003). The Analysis of gene expression data: Methods and software. New York: Springer Verlag.

    Book  MATH  Google Scholar 

  14. Poland, A., & Knutson, J. C. (1982). 2,3,7,8-tetrachlorodibenzo-p-dioxin and related halogenated aromatic hydrocarbons: Examination of the mechanism of toxicity. Annual Review of Pharmacology and Toxicology, 22, 517–554.

    Article  Google Scholar 

  15. Ramoni, M. F., Sebastiani, P., & Kohane, I. S. (2002). Cluster analysis of gene expression dynamics. Proceedings of the National Academy of Sciences, 99, 9121–9126.

    Article  MathSciNet  MATH  Google Scholar 

  16. Schliep, A., Steinhoff, C., & Schönhuth, A. (2003). Robust inference of groups in gene expression time-courses using mixtures of HMMs. Bioinformatics, 20, i283–i289.

    Article  Google Scholar 

  17. Spellman, P. T., Sherlock, G., Zhang, M., Iyer, V. R., Anders, K., Eisen, M. B., et al. (1998). Comprehensive identification of cell-cycle regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell, 9, 3273–3297.

    Google Scholar 

  18. Storey, J. D., Xuai, W., Leek, J. T., Tompkins, R. G., & Davis, R. W. (2005). Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences of the United States of America, 102, 12837–12842.

    Article  Google Scholar 

  19. Tai, Y. C., & Speed, T. P. (2006). A multivariate empirical Bayes statistic for replicated microarray time course data. Annals of Statistics, 34, 2387–2412.

    Article  MathSciNet  MATH  Google Scholar 

  20. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., et al. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences of the United States of America, 96, 2907–2912.

    Article  Google Scholar 

  21. Tavazoie, S., Hughes, J., Campbell, M., Cho, R., & Church, G. (1999). Systematic determination of genetic network architecture. Nature Genetics, 22, 281–285.

    Article  Google Scholar 

  22. Tu, B., Kudlicki, A., Rowicka, M., & McKnight, S. (2005). Logic of the yeast metabolic cycle: Temporal compartmentalization of cellular processes. Science, 310, 1152–1158.

    Article  Google Scholar 

  23. Wilson, C. L., & Safe, S. (1998). Mechanisms of ligand-induced aryl hydrocarbon receptor mediated biochemical and toxic responses. Toxicologic Pathology, 26, 657–671.

    Google Scholar 

  24. Wu, H., Yuan, M., Kaech, S., & Halloran, M. (2007). A statistical analysis of memory CD8 T cell differentiation: An application of a hierarchical state space model to a short time course microarray experiment. Annals of Applied Statistics, 1(2), 442–458.

    Article  MathSciNet  MATH  Google Scholar 

  25. Yuan, M. (2006). Flexible temporal expression profile modelling using the Gaussian process. Computational Statistics and Data Analysis, 51, 1754–1764.

    Article  MathSciNet  MATH  Google Scholar 

  26. Yuan, M., & Kendziorski, C. (2006). Hidden markov modles for microarray time course data in multiple biological conditions (with discussion). Journal of the American Statistical Association, 101, 1323–1340.

    Article  MathSciNet  MATH  Google Scholar 

  27. Yuan, M., Kendziorski, C., Park, F., Porter, J., Hayes, K., & Bradfield, C. (2003). HMM for microarray time course data in multiple biological conditions (Technical Report # 178). Department of Biostatistics, University of Wisconsin.

    Google Scholar 

Download references

Acknowledgements

This research was supported in part by NSF grants DMS-0706724 and DMS-0846234, NIH grant R01GM076274-01, and a grant from Georgia Cancer Coalition.

Author information

Authors and Affiliations

  1. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr NW, Atlanta, GA, 30332-0205, USA

    Lingyan Ruan & Ming Yuan

Authors
  1. Lingyan Ruan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Yuan .

Editor information

Editors and Affiliations

  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ruan, L., Yuan, M. (2011). Statistical Analysis of Time Course Microarray Data. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_14

Download citation

Publish with us

Policies and ethics

Search

Navigation