Review Article | Published:

Fundamentals of experimental design for cDNA microarrays

Nature Genetics volume 32, pages 490495 (2002) | Download Citation

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Abstract

Microarray technology is now widely available and is being applied to address increasingly complex scientific questions. Consequently, there is a greater demand for statistical assessment of the conclusions drawn from microarray experiments. This review discusses fundamental issues of how to design an experiment to ensure that the resulting data are amenable to statistical analysis. The discussion focuses on two-color spotted cDNA microarrays, but many of the same issues apply to single-color gene-expression assays as well.

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References

  1. 1.

    , & Variation in gene expression within and among natural populations. Nature Genet. 32, 261–266 2002

  2. 2.

    , , & Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc. Natl Acad. Sci. USA 97, 9834–9839 (2000).

  3. 3.

    Replicating effects and biases. Am. Stat. 55, 223–227 (2001).

  4. 4.

    The Design of Experiments (Wiley, NY, 1958).

  5. 5.

    & Biostatistics 2, 183–201 (2001).

  6. 6.

    & Nature Rev. Genet. 3, 579–588 (2002).

  7. 7.

    & Statistical design and the analysis of gene expression microarray data. Genet. Res. 77, 123–128 (2001).

  8. 8.

    , , & Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).

  9. 9.

    et al. The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nature Genet. 29, 389–395 (2001).

  10. 10.

    , & Design of studies using DNA microarrays. Genet. Epidemiol. 23, 21–36 (2002).

  11. 11.

    Microarray data normalization and transformation. Nature Genet. 32, 496–501 (2002).

  12. 12.

    et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30, e15 (2002).

  13. 13.

    Fallacy of per-weight and per-surface area standards, and their relation to spurious correlation. Appl. Physiol. 2, 1–15 (1949).

  14. 14.

    Spurious correlation between indices. Proc. R. Soc. Lond. 60, 489 (1897).

  15. 15.

    The Design of Experiments 6th edn (Oliver and Boyd, London, 1951).

  16. 16.

    From patterns to pathways: gene expression data analysis comes of age. Nature Genet. 32, 502–508 (2002).

  17. 17.

    , & Analysis of variance for gene expression microarray data. J. Comput. Biol. 7, 819–837 (2000).

  18. 18.

    et al. Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8, 625–637 (2001).

  19. 19.

    , , & Project normal: defining normal variance in mouse gene expression. Proc. Natl Acad. Sci. USA 98, 13266–13271 (2001).

  20. 20.

    et al. Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Genome Res. 10, 2022–2029 (2000).

  21. 21.

    et al. Statistical analysis of a gene expression microarray experiment with replication. Stat. Sinica 12, 203–218 (2002).

  22. 22.

    & Sex, flies and microarrays. Nature Genet. 29, 355–356 (2001).

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Acknowledgements

Support for this work was provided by the US National Institutes of Health. The analogy of measuring one man and one woman is attributed to Peter Petraitis.

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Affiliations

  1. The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA garyc@jax.org

    • Gary A. Churchill

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Competing interests

The author declares no competing financial interests.

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https://doi.org/10.1038/ng1031