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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Affymetrix. (2001). Statistical algorithms reference guide. Santa Clara, CA: Author.
Affymetrix. (2002). GeneChip expression analysis: Data analysis fundamentals. Santa Clara, CA: Author.
Atkins, P. (1994). Physical chemistry (5th edition). Oxford: Oxford University Press.
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.
Gentleman, R., Carey, V., Huber, W., Irizarry, R., & Dudoit, S. (2005). Bioinformatics and computational biology solutions using R and bioconductor. New York: Springer.
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.
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.
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.
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.
Irizarry, R. A., Wu, Z., & Jaffee, H. A. (2006). Comparison of Affymetrix GeneChip expression measures. Bioinformatics, 22(7), 789–794.
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.
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.
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.
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.
Purutçuoğlu, V., & Wit, E. (2007). FGX: a frequentist gene expression index for Affymetrix arrays. Biostatistics, 8(2), 433–437.
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.
Wu, Z., & Irizarry, R. A.. Stochastic models inspired by hybridization theory for short oligonucleotide arrays. Journal of Computational Biology, 12, 882–893.
Acknowledgements
The authors want to thank the University of Palermo for supporting this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Augugliaro, L., Mineo, A.M. (2010). A Statistical Calibration Model for Affymetrix Probe Level Data. In: Palumbo, F., Lauro, C., Greenacre, M. (eds) Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03739-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-03739-9_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03738-2
Online ISBN: 978-3-642-03739-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)