Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
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Most Downloaded Articles
- A General Framework for Weighted Gene Co-Expression Network Analysis by Zhang, Bin and Horvath, Steve
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments by Smyth, Gordon K
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates by Lund, Steven P./ Nettleton, Dan/ McCarthy, Davis J. and Smyth, Gordon K.
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
- Normalization, bias correction, and peak calling for ChIP-seq by Diaz, Aaron/ Park, Kiyoub/ Lim, Daniel A. and Song, Jun S.
Parameter Estimation for the Exponential-Normal Convolution Model for Background Correction of Affymetrix GeneChip Data
1Southern Methodist University
2University of Texas Southwestern Medical Center
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 5, Issue 1, Pages –, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1237, September 2006
- Published Online:
There are many methods of correcting microarray data for non-biological sources of error. Authors routinely supply software or code so that interested analysts can implement their methods. Even with a thorough reading of associated references, it is not always clear how requisite parts of the method are calculated in the software packages. However, it is important to have an understanding of such details, as this understanding is necessary for proper use of the output, or for implementing extensions to the model.In this paper, the calculation of parameter estimates used in Robust Multichip Average (RMA), a popular preprocessing algorithm for Affymetrix GeneChip brand microarrays, is elucidated. The background correction method for RMA assumes that the perfect match (PM) intensities observed result from a convolution of the true signal, assumed to be exponentially distributed, and a background noise component, assumed to have a normal distribution. A conditional expectation is calculated to estimate signal. Estimates of the mean and variance of the normal distribution and the rate parameter of the exponential distribution are needed to calculate this expectation. Simulation studies show that the current estimates are flawed; therefore, new ones are suggested. We examine the performance of preprocessing under the exponential-normal convolution model using several different methods to estimate the parameters.