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. 2005 Apr;6(3):123–131. doi: 10.1002/cfg.464

Reassessing Design and Analysis of two-Colour Microarray Experiments Using Mixed Effects Models

Guilherme J M Rosa 1,2,3,✉, Juan P Steibel 1, Robert J Tempelman 1
PMCID: PMC2447516  PMID: 18629220

Abstract

Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two-colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs.

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Selected References

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  1. Black Michael A., Doerge R. W. Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments. Bioinformatics. 2002 Dec;18(12):1609–1616. doi: 10.1093/bioinformatics/18.12.1609. [DOI] [PubMed] [Google Scholar]
  2. Churchill Gary A. Fundamentals of experimental design for cDNA microarrays. Nat Genet. 2002 Dec;32 (Suppl):490–495. doi: 10.1038/ng1031. [DOI] [PubMed] [Google Scholar]
  3. Dobbin K., Shih J. H., Simon R. Statistical design of reverse dye microarrays. Bioinformatics. 2003 May 1;19(7):803–810. doi: 10.1093/bioinformatics/btg076. [DOI] [PubMed] [Google Scholar]
  4. Dobbin K., Simon R. Comparison of microarray designs for class comparison and class discovery. Bioinformatics. 2002 Nov;18(11):1438–1445. doi: 10.1093/bioinformatics/18.11.1438. [DOI] [PubMed] [Google Scholar]
  5. Kerr M. K., Churchill G. A. Experimental design for gene expression microarrays. Biostatistics. 2001 Jun;2(2):183–201. doi: 10.1093/biostatistics/2.2.183. [DOI] [PubMed] [Google Scholar]
  6. Kerr M. K., Churchill G. A. Statistical design and the analysis of gene expression microarray data. Genet Res. 2001 Apr;77(2):123–128. doi: 10.1017/s0016672301005055. [DOI] [PubMed] [Google Scholar]
  7. Kerr M. K., Martin M., Churchill G. A. Analysis of variance for gene expression microarray data. J Comput Biol. 2000;7(6):819–837. doi: 10.1089/10665270050514954. [DOI] [PubMed] [Google Scholar]
  8. Kerr M. Kathleen. Design considerations for efficient and effective microarray studies. Biometrics. 2003 Dec;59(4):822–828. doi: 10.1111/j.0006-341x.2003.00096.x. [DOI] [PubMed] [Google Scholar]
  9. Lee Mei-Ling Ting, Whitmore G. A. Power and sample size for DNA microarray studies. Stat Med. 2002 Dec 15;21(23):3543–3570. doi: 10.1002/sim.1335. [DOI] [PubMed] [Google Scholar]
  10. Leil Tarek A., Ossadtchi Alex, Nichols Thomas E., Leahy Richard M., Smith Desmond J. Genes regulated by learning in the hippocampus. J Neurosci Res. 2003 Mar 15;71(6):763–768. doi: 10.1002/jnr.10541. [DOI] [PubMed] [Google Scholar]
  11. Pavlidis Paul, Li Qinghong, Noble William Stafford. The effect of replication on gene expression microarray experiments. Bioinformatics. 2003 Sep 1;19(13):1620–1627. doi: 10.1093/bioinformatics/btg227. [DOI] [PubMed] [Google Scholar]
  12. Wernisch Lorenz, Kendall Sharon L., Soneji Shamit, Wietzorrek Andreas, Parish Tanya, Hinds Jason, Butcher Philip D., Stoker Neil G. Analysis of whole-genome microarray replicates using mixed models. Bioinformatics. 2003 Jan;19(1):53–61. doi: 10.1093/bioinformatics/19.1.53. [DOI] [PubMed] [Google Scholar]
  13. Wolfinger R. D., Gibson G., Wolfinger E. D., Bennett L., Hamadeh H., Bushel P., Afshari C., Paules R. S. Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol. 2001;8(6):625–637. doi: 10.1089/106652701753307520. [DOI] [PubMed] [Google Scholar]
  14. Yang Yee Hwa, Speed Terry. Design issues for cDNA microarray experiments. Nat Rev Genet. 2002 Aug;3(8):579–588. doi: 10.1038/nrg863. [DOI] [PubMed] [Google Scholar]

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