Microarray Experiments on Risk Analysis Using R

  • Teresa A. OliveiraEmail author
  • Amílcar Oliveira
  • Andreia A. Monteiro
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 136)


The microarray technique is a powerful biotechnological tool, expanding in a interesting way the vision with which issues in medicine are studied. Microarray technology, allows simultaneous evaluation of the expression of thousands of genes in different tissues of a given organism, and in different stages of development or environmental conditions. However, experiments with microarrays are still substantially costly and laborious, and as a consequence, they are usually conducted with relatively small sample sizes, thereby requiring a careful experimental design and statistical analysis. This paper adopts some applications of microarrays in risk analysis using R statistical software.


ANOVA Bioinformatics Design of experiments DNA Microarrays technology Software R 



The authors gratefully acknowledge the references for their constructive advices to improve this paper. This research was partially sponsored by national founds through the Fundação Nacional para a Ciência e Tecnologia, Portugal - FCT under the projects (PEst-OE/MAT/UI0006/2011 and PEst-OE/MAT/UI0006/2014).


  1. 1.
    Beisser, D., Klau, G., Dandekar, T., Müller, T., Dittrich, T.: BioNet: an R-package for the functional analysis of biological networks. Bioinformatics 26(8), 1129–1130 (2010)CrossRefGoogle Scholar
  2. 2.
    Coffey, C.S., Cofield, S.S.: Parametric linear models. In: Allison, D.B. et al.: DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments. Chap. 12, pp. 223–243, Chapman & Hall/CRC, Boca Raton (2006)Google Scholar
  3. 3.
    Draghici, S.: Data analysis tools for DNA microarrayus. Chapmann and Hall/CRC Press,Boca Raton (2003)Google Scholar
  4. 4.
    Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A.J., Sawitzki, C., Smith, C., Smyth, G., Tierney, L., Yang, J.Y.H., Zhang, J.: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)Google Scholar
  5. 5.
    George Casella: Statistical Design. Springer eBooks (2008)Google Scholar
  6. 6.
    Jaluria, P., Konstantopoulos, K., Betenbaugh, M.: BioNet: a perspective on microarrays: current applications, pitfalls, and potential uses. Microb. Cell Factories 13(8), Article 4 (2007)Google Scholar
  7. 7.
    Kerr, M.K., Churchill, G.A.: Experimental design for gene expression microarrays. Biostatistics 2, 183–201 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kerr, M.K., Afshari, C.A., Bennett, L., Bushell, P., Martinez, J., Walker, N.J., Churchill, G.A.: A statistical analysis of a gene expression microarray experiment with replication. Statistica Sínica, Taipei 12(2), 203–217 (2002)zbMATHGoogle Scholar
  9. 9.
    Kitsos, C.P.: Optimal Experimental Design for Non-Linear Models. Springer, Berlin (2013)CrossRefzbMATHGoogle Scholar
  10. 10.
    Mora, A., Michalickova, K., Donaldson, I.M.: A survey of protein interaction data and multigenic inherited disorders. BMC Bioinform., 14–47 (2013)Google Scholar
  11. 11.
    Pramana, S., Lin, D., Haldermans, P., Shkedy, Z., Verbeke, T., Göhlmann, H., Bondt, A., Talloen, W., Bijnens, L.: IsoGene: an R package for analyzing dose-response studies in microarray experiments. R J. 2/1 (2010)Google Scholar
  12. 12.
    Rosa, G.J.M., Steibel, J.P., Tempelman, R.J.: Reassessing design and analysis of two-colour microarray experiments using mixed effects models. Comp. Funct. Genomics 6(3), 123–131 (2005)CrossRefGoogle Scholar
  13. 13.
    Rosa, G.J.M., Rocha, L.B., Furlan, L.R.: Microarray gene expression studies: experimental design, statistical data analysis, and applications in livestock research. Revista Brasileira de Zootecnia 36, (Special Supplement), 185–209 (2007)Google Scholar
  14. 14.
    Sacan, A., Ferhatosmanoglu, N., Ferhatosmanoglu, H.: Microarray designer: an online search tool and repository for near-optimal microarray experimental designs. BMC Bioinform. 10, 304–310 (2009)CrossRefGoogle Scholar
  15. 15.
    Sivey, S.D.: Optimal Design. Chapman and Hall, London (1980)Google Scholar
  16. 16.
    Steibel, J.P., Rosa, G.J.M.: On reference designs for microarray experiments. Stat. Appl. Genet. Mol. Biol. 4(1), Article 36 (2005)Google Scholar
  17. 17.
    Tempelman, R.J.: Assessing statistical precision, power, and robustness of alternative experimental designs for two color microarray platforms based on mixed effects models. Vet. Immunol. Immunopathol. 105, 175–186 (2005)CrossRefGoogle Scholar
  18. 18.
    Wit, E., Nobile, A., Khanin, R.: Near-optimal designs for dual-channel microarrays studies. Appl. Stat. 54(5), 817–830 (2005)zbMATHMathSciNetGoogle Scholar
  19. 19.
    Wolfinger, R.D., Gibson, G., Wolfinger, E.D., Bennet, L., Hamadeh, H., Bushel, P., Afshari, C., Paules, R.S.: Assessing gene significance from cDNA midroarray expression data via mixed models. J. Comput. Biol. 8(6), 625–637 (2009)CrossRefGoogle Scholar
  20. 20.
    Yang, Y.H., Speed, T.: Design issues for cDNA microarray experiments. Nat. Rev. Genet. 3, 579–588 (2002)Google Scholar
  21. 21.
    Yasrebi, H.: SurvJamda: an R package to predict patients’ survival and risk assessment using joint analysis of microarray gene expression data. Bioinformatics 27(8), 1168–1169 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Teresa A. Oliveira
    • 1
    Email author
  • Amílcar Oliveira
    • 1
  • Andreia A. Monteiro
    • 2
  1. 1.Universidade Aberta and CEAULLisbonPortugal
  2. 2.MEMeC - Universidade AbertaLisbonPortugal

Personalised recommendations