A new variance stabilizing transformation for gene expression data analysis

Diana M. Kelmansky, Elena J. Martínez, Víctor Leiva

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


In this paper, we introduce a new family of power transformations, which has the generalized logarithm as one of its members, in the same manner as the usual logarithm belongs to the family of Box-Cox power transformations. Although the new family has been developed for analyzing gene expression data, it allows a wider scope of mean-variance related data to be reached. We study the analytical properties of the new family of transformations, as well as the mean-variance relationships that are stabilized by using its members. We propose a methodology based on this new family, which includes a simple strategy for selecting the family member adequate for a data set. We evaluate the finite sample behavior of different classical and robust estimators based on this strategy by Monte Carlo simulations. We analyze real genomic data by using the proposed transformation to empirically show how the new methodology allows the variance of these data to be stabilized.

Original languageEnglish
Pages (from-to)653-666
Number of pages14
JournalStatistical Applications in Genetics and Molecular Biology
Issue number6
StatePublished - Dec 2013
Externally publishedYes


  • Classical and robust estimators
  • Linear models
  • Microarrays
  • Monte Carlo method
  • Power transformations
  • R software
  • Regression methods


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