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Oxford University Press (OUP), Bioinformatics, 11(19), p. 1360-1367

DOI: 10.1093/bioinformatics/btg178

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Estimation of transformation parameters for microarray data

Journal article published in 2003 by Blythe Durbin, David M. Rocke ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.