title: Improving cross-study prediction through addon batch effect adjustment or addon normalization.
abstract: To date most medical tests derived by applying classification methods to high-dimensional molecular data are hardly used in clinical practice. This is partly because the prediction error resulting when applying them to external data is usually much higher than internal error as evaluated through within-study validation procedures. We suggest the use of addon normalization and addon batch effect removal techniques in this context to reduce systematic differences between external data and the original dataset with the aim to improve prediction performance.
  
  
  
  We evaluate the impact of addon normalization and seven batch effect removal methods on cross-study prediction performance for several common classifiers using a large collection of microarray gene expression datasets, showing that some of these techniques reduce prediction error.
  
  
  
  All investigated addon methods are implemented in our R package bapred.
  
  
  
  hornung@ibe.med.uni-muenchen.de.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors: Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany.
authors: Roman
authors: R
authors: Hornung
authors: Applied Mathematics Department, Agrocampus Ouest, Rennes, France.
authors: David
authors: D
authors: Causeur
authors: Leibniz Supercomputing Center, Garching, Germany.
authors: Christoph
authors: C
authors: Bernau
authors: Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany.
authors: Anne-Laure
authors: AL
authors: Boulesteix
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011237:Predictive Value of Tests; D012107:Research Design; D017423:Sequence Analysis, RNA