Parameter estimation for the calibration and variance stabilization of microarray data

Huber Wolfgang, Anja von Heydebreck, Sueltmann Holger, Poustka Annemarie and Vingron Martin
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Huber Wolfgang: German Cancer Research Center, Heidelberg, Germany
Anja von Heydebreck: Max-Planck-Institute for Molecular Genetics, Berlin, Germany
Sueltmann Holger: German Cancer Research Center, Heidelberg, Germany
Poustka Annemarie: German Cancer Research Center, Heidelberg, Germany
Vingron Martin: Max-Planck-Institute for Molecular Genetics, Berlin, Germany

Statistical Applications in Genetics and Molecular Biology, 2003, vol. 2, issue 1, 24

Abstract: We derive and validate an estimator for the parameters of a transformation for the joint calibration (normalization) and variance stabilization of microarray intensity data. With this, the variances of the transformed intensities become approximately independent of their expected values. The transformation is similar to the logarithm in the high intensity range, but has a smaller slope for intensities close to zero. Applications have shown better sensitivity and specificity for the detection of differentially expressed genes. In this paper, we describe the theoretical aspects of the method. We incorporate calibration and variance-mean dependence into a statistical model and use a robust variant of the maximum-likelihood method to estimate the transformation parameters. Using simulations, we investigate the size of the estimation error and its dependence on sample size and the presence of outliers. We find that the error decreases with the square root of the number of probes per array and that the estimation is robust against the presence of differentially expressed genes. Software is publicly available as an R package through the Bioconductor project (http://www.bioconductor.org).

Keywords: microarrays; error model; variance stabilizing transformation; resistant regression; robust estimation; maximum likelihood; simulation (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (6)

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DOI: 10.2202/1544-6115.1008

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