Parameter estimation for the calibration and variance stabilization of microarray data
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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).
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Sampling Correction in Pedigree Analysis
- Parameter estimation for the calibration and variance stabilization of microarray data
- Transformations for cDNA Microarray Data
- Supervised Detection of Regulatory Motifs in DNA Sequences
- Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot
- On the Power of Profiles for Transcription Factor Binding Site Detection
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Articles in the same Issue
- Article
- Use of Mixture Models in a Microarray-Based Screening Procedure for Detecting Differentially Represented Yeast Mutants
- Sampling Correction in Pedigree Analysis
- Parameter estimation for the calibration and variance stabilization of microarray data
- Transformations for cDNA Microarray Data
- Supervised Detection of Regulatory Motifs in DNA Sequences
- Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot
- On the Power of Profiles for Transcription Factor Binding Site Detection
- An Empirical Bayesian Method for Differential Expression Studies Using One-Channel Microarray Data
