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.
Original language | English (US) |
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Pages (from-to) | 1360-1367 |
Number of pages | 8 |
Journal | Bioinformatics |
Volume | 19 |
Issue number | 11 |
DOIs | |
State | Published - Jul 22 2003 |
ASJC Scopus subject areas
- Clinical Biochemistry
- Computer Science Applications
- Computational Theory and Mathematics