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A Statistical Calibration Model for Affymetrix Probe Level Data

  • Luigi Augugliaro
  • Angelo M. MineoEmail author
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Gene expression microarrays allow a researcher to measure the simultaneous response of thousands of genes to external conditions. Affymetrix GeneChip{ $Ⓡ$} expression array technology has become a standard tool in medical research. Anyway, a preprocessing step is usually necessary in order to obtain a gene expression measure. Aim of this paper is to propose a calibration method to estimate the nominal concentration based on a nonlinear mixed model. This method is an enhancement of a method proposed in Mineo et al. (2006). The relationship between raw intensities and concentration is obtained by using the Langmuir isotherm theory.

Notes

Acknowledgements

The authors want to thank the University of Palermo for supporting this research.

References

  1. Affymetrix. (2001). Statistical algorithms reference guide. Santa Clara, CA: Author.Google Scholar
  2. Affymetrix. (2002). GeneChip expression analysis: Data analysis fundamentals. Santa Clara, CA: Author.Google Scholar
  3. Atkins, P. (1994). Physical chemistry (5th edition). Oxford: Oxford University Press.Google Scholar
  4. Efron, B., Tibshirani, R., Storey, J., & Tusher, V. (2001). Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association, 96(456), 1151–1160.zbMATHCrossRefMathSciNetGoogle Scholar
  5. Gentleman, R., Carey, V., Huber, W., Irizarry, R., & Dudoit, S. (2005). Bioinformatics and computational biology solutions using R and bioconductor. New York: Springer.zbMATHCrossRefGoogle Scholar
  6. Hein, A. M., Richardson, S., Causton, H. C., Ambler, G. K., & Green, P. J. (2005). BGX: A fully Bayesian gene expression index for Affymetrix GeneChip data. Biostatistics, 6(3), 349–373.zbMATHCrossRefGoogle Scholar
  7. Hekstra, D., Taussig, A. R., Magnasco, M., & Naef, F. (2003). Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide arrays. Nucleic Acids Research, 31(7), 1962–1968.CrossRefGoogle Scholar
  8. Hill, A. A., Brown, E. L., Whitley, M. Z., Kellogg, G. T., Hunter, C. P., & Slonim, D. K. (2001). Evaluation of normalization procedures for oligonucleotide array data based on spike cRNA controls. Genome Biology, 2(12), 1–13.CrossRefGoogle Scholar
  9. Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K., Scherf, U., et al. (2003). Exploration, normalization and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2), 249–264.zbMATHCrossRefGoogle Scholar
  10. Irizarry, R. A., Wu, Z., & Jaffee, H. A. (2006). Comparison of Affymetrix GeneChip expression measures. Bioinformatics, 22(7), 789–794.CrossRefGoogle Scholar
  11. Li, C., & Wong, W. (2001). Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. In Proceedings of the National Academy of Science USA, 98, 31–36.Google Scholar
  12. Liu, X., Milo, M., Lawrence, N. D., & Rattray, M. (2005). A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips. Bioinformatics, 21(18), 3637–3644.CrossRefGoogle Scholar
  13. Mineo, A. M., Fede, C., Augugliaro, L., & Ruggieri, M. (2006). Modelling the background correction in microarray data analysis. In Proceedings in computational statistics, 17th COMPSTAT Symposium of the IASC (pp. 1593–1600). Heidelberg: Physica.Google Scholar
  14. Naef, F., & Magnasco, M. O. (2003). Solving the riddle of the bright mismatches: Labeling and effective binding in oligonucleotide arrays. Phyical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 68(1 Pt 1), 011906.Google Scholar
  15. Purutçuoğlu, V., & Wit, E. (2007). FGX: a frequentist gene expression index for Affymetrix arrays. Biostatistics, 8(2), 433–437.zbMATHCrossRefGoogle Scholar
  16. Tusher, V. G., Tibshirani, R., & Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences USA, 98(9), 5116–5121.zbMATHCrossRefGoogle Scholar
  17. Wu, Z., & Irizarry, R. A.. Stochastic models inspired by hybridization theory for short oligonucleotide arrays. Journal of Computational Biology, 12, 882–893.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Dipartimento di Scienze Statistiche e MatematicheUniversità di PalermoPalermoItaly

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