Improving cross-study prediction through addon batch effect adjustment or addon normalization - Institut Agro Rennes-Angers Accéder directement au contenu
Article Dans Une Revue Bioinformatics Année : 2017

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

Résumé

Motivation: 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 normaliza-tion 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. Results: 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. Availability and Implementation: All investigated addon methods are implemented in our R package bapred.

Dates et versions

hal-01518454 , version 1 (24-04-2019)

Identifiants

Citer

Roman Hornung, David Causeur, Christoph Bernau, Anne-Laure Boulesteix. Improving cross-study prediction through addon batch effect adjustment or addon normalization. Bioinformatics, 2017, 33 (3), pp.397-404. ⟨10.1093/bioinformatics/btw650⟩. ⟨hal-01518454⟩
273 Consultations
6 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More