A Variance-Stabilizing Transformation for Gene-Expression Microarray Data

Authors

B.P. Durbin, University of California - Davis
Johanna S. Hardin, Pomona CollegeFollow
D.M. Hawkins, University of Minnesota
David M. Rocke, University of California - Davis

Document Type

Article

Department

Mathematics (Pomona)

Publication Date

2002

Keywords

cDNA array, microarray, statistical analysis, transformation, normalization

Abstract

Motivation: Standard statistical techniques often assume that data are normally distributed, with constant variance not depending on the mean of the data. Data that violate these assumptions can often be brought in line with the assumptions by application of a transformation. Gene-expression microarray data have a complicated error structure, with a variance that changes with the mean in a non-linear fashion. Log transformations, which are often applied to microarray data, can inflate the variance of observations near background.

Results: We introduce a transformation that stabilizes the variance of microarray data across the full range of expression. Simulation studies also suggest that this transformation approximately symmetrizes microarray data.

Rights Information

© 2002 Oxford University Press

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Terms of Use for work posted in Scholarship@Claremont.

DOI

10.1093/bioinformatics/18.suppl_1.S

Recommended Citation

Durbin, B., Hardin, J., Hawkins, D., Rocke, D.; A Variance-Stabilizing Transformation for Gene-Expression Microarray Data; Bioinformatics, 18: S105-S110; 2002. doi: 10.1093/bioinformatics/18.suppl_1.S