A variance-stabilizing transformation for gene-expression microarray data

B. P. Durbin, J. S. Hardin, D. M. Hawkins, David M Rocke

Research output: Contribution to journalArticlepeer-review

284 Scopus citations

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.

Original languageEnglish (US)
JournalBioinformatics
Volume18
Issue numberSUPPL. 1
StatePublished - 2002

Keywords

  • cDNA array
  • Microarray
  • Normalization
  • Statistical analysis
  • Transformation

ASJC Scopus subject areas

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

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