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PubMed · 12874047

Estimation of transformation parameters for microarray data.

Blythe Durbin·David M Rocke
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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.

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AlgorithmsAnimalsComputer SimulationGene Expression ProfilingLeukemiaLikelihood FunctionsLinear ModelsMaleModels, GeneticModels, StatisticalOligonucleotide Array Sequence AnalysisRatsReproducibility of ResultsSensitivity and SpecificityComparative StudyEvaluation StudyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.Validation Study

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Blythe Durbin, David M Rocke. 2003-07-22. Estimation of transformation parameters for microarray data.. https://doi.org/10.1093/bioinformatics%2Fbtg178

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