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Gene Set Analysis as a Means of Facilitating the Interpretation of Microarray Results

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Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R

Part of the book series: Use R! ((USE R))

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Abstract

Interpretation of results from the voluminous data generated by microarray experiments can be facilitated by incorporating information about the underlying biological mechanisms into the statistical analysis of the experimental data. In this chapter we discuss a powerful approach for fusing gene expression and gene annotation data to yield scientifically more interpretable results.

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References

  • Amaratunga, D., & Cabrera, J. (2004). Exploration and analysis of DNA microarray and protein array data. Hoboken: Wiley.

    Google Scholar 

  • Gene Ontology Consortium. (2000). Gene ontology: Tool for the unification of biology. Nature Genetics, 25, 25–29.

    Google Scholar 

  • Fisher, R. A. (1934) Statistical Methods for Research Workers. Oxford University Press.

    Google Scholar 

  • Mootha, V. K. Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., et al. (2003). PGC-1-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics, 34(3), 267–273.

    Google Scholar 

  • Pavlidis, P. Qin, J., Arango, V., Mann, J. J., & Sibille, E. (2004). Using the gene ontology for microarray data mining: A comparison of methods and application to age effects in human prefrontal cortex. Nuerochemical Research 29, 1213–1222.

    Google Scholar 

  • Raghavan, N., Amaratunga, D., Cabrera, J., Nie, A., Qin, J., & McMillian, M, (2006). On methods for gene function scoring as a means of facilitating the interpretation of microarray results. Journal of Computational Biology, 13(3), 798–809.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Janssen Pharmaceutical Companies of Johnson & Johnson, Raritan, NJ, USA

    Nandini Raghavan & Dhammika Amaratunga

  2. Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium

    An De Bondt

  3. OpenAnalytics BVBA, Heist-op-den-Berg, Belgium

    Tobias Verbeke

Authors
  1. Nandini Raghavan
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  2. An De Bondt
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  3. Tobias Verbeke
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  4. Dhammika Amaratunga
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Corresponding author

Correspondence to Nandini Raghavan .

Editor information

Editors and Affiliations

  1. , VMRD, Pfizer Animal Health, Hoge Wei 1, Zaventem, 1930, Belgium

    Dan Lin

  2. Center for Statistics, Hasselt University, Agoralaan 1, Diepenbeek, 3590, Belgium

    Ziv Shkedy

  3. , Department of Statistics and, School of Mathematical Sciences, Ramat Aviv, Tel Aviv, 69978, Israel

    Daniel Yekutieli

  4. Johnson & Johnson, Route 202 1000, Raritan, 08869, New Jersey, USA

    Dhammika Amaratunga

  5. , Biostatistics and Programming Center of, Janssen Pharmaceutica, Turnhoutseweg 30, Beerse, 2340, Belgium

    Luc Bijnens

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© 2012 Springer-Verlag Berlin Heidelberg

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Raghavan, N., De Bondt, A., Verbeke, T., Amaratunga, D. (2012). Gene Set Analysis as a Means of Facilitating the Interpretation of Microarray Results. In: Lin, D., Shkedy, Z., Yekutieli, D., Amaratunga, D., Bijnens, L. (eds) Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R. Use R!. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24007-2_12

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