title: Use of pretransformation to cope with extreme values in important candidate features.
abstract: Extreme values in predictors often strongly affect the results of statistical analyses in high-dimensional settings. Although they frequently occur with most high-throughput techniques, the problem is often ignored in the literature. We suggest to use a very simple transformation, proposed before in a different context by Royston and Sauerbrei, as an intermediary step between array preprocessing and high-level statistical analysis. This straightforward univariate transformation identifies extreme values in continuous features and can thus be used as a diagnostic tool for outliers. The use of the transformation and its effects is demonstrated for diverse univariate and multivariate statistical analyses using nine publicly available microarray data sets.
authors.affiliation: Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, 81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de
authors.forename: Anne-Laure
authors.initials: AL
authors.lastname: Boulesteix
authors.forename: Vincent
authors.initials: V
authors.lastname: Guillemot
authors.forename: Willi
authors.initials: W
authors.lastname: Sauerbrei
mesh_terms: D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012044:Regression Analysis