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Estimation of transformation parameters for microarray data.

Blythe Durbin¹, David M Rocke

¹Department of Statistics, UC Davis, Davis, CA 95616, USA. bpdurbin@ucdavis.edu

Bioinformatics (Oxford, England)

|July 23, 2003

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A new method estimates transformation parameters for microarray data, stabilizing variance. This approach, based on generalized-log transformations, also optimizes for skewness and dependency, improving data analysis.

Area of Science:

Bioinformatics
Statistical Genetics

Background:

Microarray data analysis requires variance stabilization.
Existing generalized-log transformations offer first-order variance stabilization.

Purpose of the Study:

To introduce a method for estimating transformation parameters within the generalized-log family.
To integrate parameter estimation with linear models.
To explore criteria for optimal transformations beyond variance stabilization.

Main Methods:

Utilizing the Box-Cox procedure for parameter estimation.
Estimating transformation parameters in conjunction with linear models.
Investigating optimization criteria like minimum residual skewness and mean-variance dependency.

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Main Results:

A method for estimating transformation parameters in generalized-log transformations is presented.
The method allows for simultaneous estimation with linear models.
The study discusses finding transformations optimal for skewness and dependency.

Conclusions:

The proposed method enhances microarray data analysis by providing robust variance stabilization.
The approach offers flexibility in selecting transformations based on specific statistical criteria.
This work contributes to more reliable interpretation of gene expression data.