Improving cross-study prediction through addon batch effect adjustment and addon normalization

Hornung, Roman; Causeur, David; Bernau, Christoph und Boulesteix, Anne-Laure (8. Juni 2016): Improving cross-study prediction through addon batch effect adjustment and addon normalization. Department of Statistics: Technical Reports, Nr. 194

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".

• Dokumententyp: Paper
• Keywords: Classification, Machine learning, Prediction, Data analysis, Microarray data analysis
• Fakultät: Mathematik, Informatik und Statistik > Statistik > Technische Reports
• Themengebiete: 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
• Sprache: Englisch

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