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 |
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ASJC Scopus subject areas
- Clinical Biochemistry
- Computer Science Applications
- Computational Theory and Mathematics
Cite this
Estimation of transformation parameters for microarray data. / Durbin, Blythe; Rocke, David M.
In: Bioinformatics, Vol. 19, No. 11, 22.07.2003, p. 1360-1367.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Estimation of transformation parameters for microarray data
AU - Durbin, Blythe
AU - Rocke, David M
PY - 2003/7/22
Y1 - 2003/7/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0042594496&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0042594496&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btg178
DO - 10.1093/bioinformatics/btg178
M3 - Article
C2 - 12874047
AN - SCOPUS:0042594496
VL - 19
SP - 1360
EP - 1367
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 11
ER -