Advertisement

Microarrays pp 97-117 | Cite as

Intensity Concentration Relationships for Electrochemical Detection

Latin Square and Mixture Study Analysis
  • Mervyn Thomas
Part of the Integrated Analytical Systems book series (ANASYS)

5.1 Introduction

The currently accepted technology for polynucleotide quantitation on gene chips involves labelling the assayed polynucleotides with a fluorescent marker, hybridis ing the sample to the gene chip, and then imaging the gene chip under fluorescence. The background-corrected intensity of fluorescence is assumed to be proportional to the concentration of the target polynucleotide.

CombiMatrix have developed a novel technology for quantification of binding to their custom gene chip platform. The sensor is based on the horseradish peroxidase coupled electrochemical properties of each gene chip cell. The technology is inher ently cheaper, more reliable, more sensitive, and more robust than fluorescence based optical sensors of gene expression binding.

CombiMatrix have noted that the response between polynucleotide concentration and reading intensity is nonlinear. That is, there is no longer a simple proportional ity between background-corrected fluorescence and polynucleotide...

Keywords

Background Correction Median Normalisation Gene Chip Principal Component Score Deconvolution Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    B.M. Bolstad, R.A. Irizarry, M. Astrand, and T.P. Speed. A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics, 19(2):185–193, 2003.CrossRefGoogle Scholar
  2. 2.
    W.S. Cleveland. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368):829–836, 1979.CrossRefGoogle Scholar
  3. 3.
    W.G. Cochran and G.M. Cox. Experimental Designs. John Wiley & Sons, New York, second edition, 1957.Google Scholar
  4. 4.
    A. Cross, J. Settle, N. Drake, and R. Paivinen. Subpixel measurement of tropical forest cover using avhrr data. International Journal of Remote Sensing, 12:1119–1129, 1991.CrossRefGoogle Scholar
  5. 5.
    T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York, 1st edition, 2001.Google Scholar
  6. 6.
    I. Helland. PLS regression and statistical models. Scandinavian Journal of Statistics, 17:97–114, 1990.Google Scholar
  7. 7.
    P. Huber. Robust Statistics. John Wiley and Sons, New York, 1981.CrossRefGoogle Scholar
  8. 8.
    I. Jolliffe. Principal Components Analysis. Springer Verlag, New York, 1986.Google Scholar
  9. 9.
    M. Stone. Crossvalidatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B (Methodological), 36:111–147, 1974.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Mervyn Thomas
    • 1
  1. 1.Emphron Informatics Inc.QueenslandAustralia

Personalised recommendations