Skip to main content
Springer Nature Link
Log in

On Efficiency of Experimental Designs for Single Factor cDNA Microarray Experiments

  • Conference paper
Data Mining and Knowledge Management (CASDMKM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3327))

  • 1098 Accesses

Abstract

Microarray experiments are used to perform gene expression profiling on a large scale. The focus of this study is on the efficiency of statistical design for single factor cDNA two-color microarray experiments. Relative efficiencies of proposed designs in Yang ([13]) are studied within the framework of incomplete block designs as well as row-column designs. Such efficiencies are investigated under fixed and mixed analysis of variance models. Furthermore, a real data analysis is conducted.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Black, M., Doerge, R.: Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray. Bioinformatics 18(12), 1609–1616 (2002)

    Article  Google Scholar 

  2. Cui, X., Kerr, M.K., Churchill, G.: Data transformations for cDNA microarray data (2002), http://www.jax.org/research/churchill/pubs/index.html

  3. Derisi, J.L., Iyer, V.R., Brown, P.O.: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997)

    Article  Google Scholar 

  4. Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97, 77–87 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  5. Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences 25, 14863–14868 (1998)

    Article  Google Scholar 

  6. John, J.A., Williams, E.R.: Cyclic and Computer Generated Designs. Chapman and Hall, London (1995)

    Book  MATH  Google Scholar 

  7. Kerr, M., Churchill, G.: Experimental design for gene expression microarrays. Biostatistics 2, 183–201 (2001)

    Article  MATH  Google Scholar 

  8. Kohane, I.S., Kho, A.T., Butte, A.J.: Microarrays for an Integrative Genomics. The MIT Press, Cambridge (2003)

    Google Scholar 

  9. Sebastiani, P., Gussoni, E., Kohane, I.S., Ramoni, M.F.: Statistical Challenges in Functional Genomics (with discussions). Statistical Science 18, 33–70 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Shah, K.R., Sinha, B.K.: Theory of Optimal Designs. Lecture notes in statistics. Springer, Heidelberg (1989)

    Book  MATH  Google Scholar 

  11. Smyth, G.K., Yang, Y.-H., Speed, T.P.: Statistical issues in microarray data analysis. In: Brownstein, M.J., Khodursky, A.B. (eds.) Functional Genomics: Methods and Protocols. Methods in Molecular Biology, pp. 111–136. Humana Press, Totowa (2002)

    Google Scholar 

  12. Wolfinger, R.D., Gibson, G., Wolfinger, E.D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C.A., Paules, R.: Assessing gene significance from cDNA Microarray expression data via mixed Models. Journal of Computational Biology 8(6), 625–637 (2001)

    Article  Google Scholar 

  13. Yang, X.: Optimal Design of Single Factor cDNA Microarray Experiments and Mixed Models for Gene Expression Data, Ph.D. dissertation, Department of Statistics, Virginia Tech (2003)

    Google Scholar 

  14. Yang, Y.H., Speed, T.P.: Design issues for cDNA microarray experiments. Nature Reviews 3, 579–588 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Monsanto Company, 800 N. Lindbergh Blvd., St. Louis, MO, 63167, USA

    Xiao Yang

  2. Department of Statistics, Virginia Tech, Blacksburg, VA, 24061-0439, USA

    Keying Ye

Authors
  1. Xiao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keying Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China; College of Information Science and Technology, University of Nebraska at Omaha, 68182, Omaha, NE, USA

    Yong Shi

  2. Institute of Policy & Management, Chinese Academy of Sciences, P.O. Box, 100080, Beijing, P.R. China

    Weixuan Xu

  3. College of Information Science and Technology, University of Nebraska at Omaha, 68182, NE, USA

    Zhengxin Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, X., Ye, K. (2004). On Efficiency of Experimental Designs for Single Factor cDNA Microarray Experiments. In: Shi, Y., Xu, W., Chen, Z. (eds) Data Mining and Knowledge Management. CASDMKM 2004. Lecture Notes in Computer Science(), vol 3327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30537-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30537-8_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23987-1

  • Online ISBN: 978-3-540-30537-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics