Variance-stabilizing transformations for two-color microarrays

Blythe P. Durbin, David M Rocke

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


Motivation: Authors of several recent papers have independently introduced a family of transformations (the generalized-log family), which stabilizes the variance of microarray data up to the first order. However, for data from two-color arrays, tests for differential expression may require that the variance of the difference of transformed observations be constant, rather than that of the transformed observations themselves. Results: We introduce a transformation within the generalized-log family which stabilizes, to the first order, the variance of the difference of transformed observations. We also introduce transformations from the 'started-log' and log-linear-hybrid families which provide good approximate variance stabilization of differences. Examples using control-control data show that any of these transformations may provide sufficient variance stabilization for practical applications, and all perform well compared to log ratios.

Original languageEnglish (US)
Pages (from-to)660-667
Number of pages8
Issue number5
StatePublished - Mar 22 2004

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics


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