Batch effect removal methods for microarray gene expression data integration: a survey

By Cosmin Lazar, Stijn Meganck, Jonatan Taminau, David Steenhoff, Alain Coletta, Colin Molter, D. W. Solís, Robin Duque, Hugues Bersini, Ann Nowé. Briefings Bioinform., 2013

326 citations recorded by the source corpus.

Genomic data integration is a key goal to be achieved towards large-scale genomic data analysis. This process is very challenging due to the diverse sources of information resulting from genomics experiments. In this work, we review methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments. It has been acknowledged that the main source of variation between different MAGE datasets is due to the so-called 'batch effects'. The methods reviewed here perform data integration by removing (or more precisely attempting to remove) the unwanted variation associated with batch effects. They are presented in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process. We provide a systematic description of the MAGE data integration methodology together with some basic recommendation to help the users in choosing the appropriate tools to integrate MAGE data for large-scale analysis; and also how to evaluate them from different perspectives in order to quantify their efficiency. All genomic data used in this study for illustration purposes were retrieved from InSilicoDB http://insilico.ulb.ac.be.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1e54adc32b396ec4d5744605b96f846b37b274b8

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Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation (2014, background): “Such systematic errors, related to technical aspects, are often referred to as batch effects (see [6] for a comprehensive discussion).”