title: Comparing transformation methods for DNA microarray data.
abstract: BACKGROUND
  When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer.
  
  
  RESULTS
  We used the ratio between biological variance and measurement variance (which is an F-like statistic) as a quality measure for transformation methods, and we demonstrate a method for maximizing that variance ratio on real data. We explore a number of transformations issues, including Box-Cox transformation, baseline shift, partial subtraction of the log-reference signal and smoothing. It appears that the optimal choice of parameters for the transformation methods depends on the data. Further, the behavior of the variance ratio, under the null hypothesis of zero biological variance, appears to depend on the choice of parameters.
  
  
  CONCLUSIONS
  The use of replicates in microarray experiments is important. Adjustment for the null-hypothesis behavior of the variance ratio is critical to the selection of transformation method.
authors.affiliation: Clinical Epidemiology and Biostatistics, Academisch Medisch Centrum, University of Amsterdam, 1100 DD, Netherlands. h.h.thygesen@amc.uva.nl
authors.forename: Helene H
authors.initials: HH
authors.lastname: Thygesen
authors.forename: Aeilko H
authors.initials: AH
authors.lastname: Zwinderman
mesh_terms: D000818:Animals; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D007668:Kidney / Q000737:chemistry / Q000378:metabolism; D008099:Liver / Q000737:chemistry / Q000378:metabolism; D008297:Male; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data*; D009928:Organ Specificity / Q000235:genetics; D012016:Reference Values; D015203:Reproducibility of Results; D013737:Testis / Q000737:chemistry / Q000378:metabolism