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) |
|---|---|
| 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
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Cite this
Durbin, B., & Rocke, D. M. (2003). Estimation of transformation parameters for microarray data. Bioinformatics, 19(11), 1360-1367. https://doi.org/10.1093/bioinformatics/btg178