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Optimal Designs for Microarray Experiments with Biological and Technical Replicates

  • Rashi Gupta
  • Panu Somervuo
  • Sangita Kulathinal
  • Petri Auvinen

Microarrays are powerful tools for global monitoring of gene expressions in many areas of biomedical research (Brown and Botstein (1999)). Since the first publication on the statistical analysis of data from microarray experiments (Chen et al. (1997)), considerable amount of research has been carried out regarding such analysis. However, little work has been done on designing microarray experiments despite the fact that designing is the key for optimization of resources and efficient estimation of the parameters of interest.

Microarray experiments consist of large number of steps, as a result various sources of errors and variability crop-in during the experiment which then affect the final outcome. However, the sources of variation in the microarray experiment are yet to be completely understood. The extent to which these sources of variations are known should be considered while designing the experiment so as to obtain quality data and precise results.

The main purpose of this article is to describe approaches for designing microarray experiments considering both technical and biological replicates. Our approach is similar to the ones taken by Churchill (2002); Wit and McClure (2004). The method for searching optimal designs has been implemented in Matlab. In Section 2, we describe the various sources of variations in the microarray experiment. Section 3 describes the model, optimality criteria, and the implementation. In Section 4, we illustrate our approach with examples. The paper concludes with a discussion section.

Keywords

Optimal Design Optimality Criterion Microarray Experiment Parameter Estimator Gene Expression Microarrays 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Brown P, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nature Genetics (Suppl.) 21:33-37CrossRefGoogle Scholar
  2. Chen Y, Dougherty E, Bittner M (1977) Ratio-based decisions and the quantitative analysis of cDNA micro-array images. Journal of Biomedical Optics 2:364-374CrossRefGoogle Scholar
  3. Churchill GA (2002) Fundamentals of experimental design for cDNA microarrays. Nature Genetics 32:490-495CrossRefGoogle Scholar
  4. Kerr MK, Churchill GA (2001) Experimental design for gene expression microarrays. Biostatistics 2:183-201MATHCrossRefGoogle Scholar
  5. Kerr MK (2003) Design considerations for efficient and effective microarray studies. Biometrics 59:822-828MATHCrossRefMathSciNetGoogle Scholar
  6. Searle S (1971) Linear Models. New York: WileyMATHGoogle Scholar
  7. Vinciotti V, Khanin R, D’Alimonte D, Liu X, Cattini N, Hotchkiss G, Bucca G, de Jesus O, Rasaiyaah J, Smith CP, Kellam P, Wit E (2005) An experimental evaluation of a loop versus a reference design for two-channel microarrays. Bioinformatics 21(4):492-501CrossRefGoogle Scholar
  8. Wit EC, McClure JD (2004) Statistics for Microarrays: Design and Analysis and Inference. John Wiley & SonsGoogle Scholar
  9. Woo Y, Krueger W, Kaur A, Churchill G (2005) Experimental design for three-color and four-color gene expression microarrays. Bioinformatics 21(Suppl 1):i459-i467Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • Rashi Gupta
    • 1
    • 2
  • Panu Somervuo
    • 2
  • Sangita Kulathinal
    • 1
  • Petri Auvinen
    • 2
  1. 1.Department of Mathematics and StatisticsUniversity of HelsinkiHelsinkiFinland
  2. 2.Institute of BiotechnologyUniversity of HelsinkiHelsinkiFinland

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