Generalized Beta Convolution Model of the True Intensity for the Illumina Bead Arrays

Authors

  • Rohmatul Fajriyah Institute of Statistics, TU Graz; Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia

Keywords:

background correction, additive error, generalized beta distribution family, IlluminaBeadArrays and convolution model

Abstract

Microarray data, which come from many steps of production, have been known to contain noise. The pre-processing is implemented to reduce the noise, where the background is corrected. Prior to further analysis, many IlluminaBeadArrays users had applied the convolution model, a model which had been adapted from when it was first developed on the Affymetrix platform, to adjust the intensity value:  corrected background intensity value.

Several models based on the different underlying distributions and or the parameters estimation methods have been proposed and applied. For instance: the exponential-gamma, the normal-gamma, and the exponential-normal convolutions with maximum likelihood estimation, non-parametric, Bayesian and moment methods of the parameters estimation, including two recent exponential-lognormal and gamma-lognormal convolutions.

In this paper, we propose models and derive the corrected background intensity based on the generalized betas and the generalized beta-normal convolutions as a generalization of the existing models.

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Published

2015-07-30

How to Cite

Fajriyah, R. (2015). Generalized Beta Convolution Model of the True Intensity for the Illumina Bead Arrays. Thailand Statistician, 13(2), 145–167. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/37769

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Section

Articles