Estimation of transformation parameters for microarray data

Bioinformatics. 2003 Jul 22;19(11):1360-7. doi: 10.1093/bioinformatics/btg178.


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.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Validation Study

MeSH terms

  • Algorithms*
  • Animals
  • Computer Simulation
  • Gene Expression Profiling / methods*
  • Leukemia / genetics
  • Likelihood Functions
  • Linear Models
  • Male
  • Models, Genetic*
  • Models, Statistical*
  • Oligonucleotide Array Sequence Analysis / methods*
  • Rats
  • Reproducibility of Results
  • Sensitivity and Specificity