Estimation of transformation parameters for microarray data
Copyright policy )Estimation of transformation parameters for microarray data
Abstract Motivation and Results: Durbin et al. (2002), Huber et al. (2002) and Munson (2001) independently introduced a family of transformations (the generalized-log family) which stabilizes the variance of microarray data up to the first order. We introduce a method for estimating the transformation parameter in tandem with a linear model based on the procedure outlined in Box and Cox (1964). We also discuss means of finding transformations within the generalized-log family which are optimal under other criteria, such as minimum residual skewness and minimum mean-variance dependency. Availability: R and Matlab code and test data are available from the authors on request. Contact: bpdurbin@ucdavis.edu * To whom correspondence should be addressed.
- University of California, Davis United States
- University of California, San Francisco United States
Male, Likelihood Functions, Leukemia, Models, Statistical, Models, Genetic, Gene Expression Profiling, Reproducibility of Results, Sensitivity and Specificity, Rats, Linear Models, Animals, Computer Simulation, Algorithms, Oligonucleotide Array Sequence Analysis
Male, Likelihood Functions, Leukemia, Models, Statistical, Models, Genetic, Gene Expression Profiling, Reproducibility of Results, Sensitivity and Specificity, Rats, Linear Models, Animals, Computer Simulation, Algorithms, Oligonucleotide Array Sequence Analysis
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