Batch effect removal methods for microarray gene expression data integration: a survey
Open accessAbstract
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.
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Batch correction lowered inter-study heterogeneity, and the pooled corrected metagenomes differed significantly between responders and nonresponders (P = 0.002).
“Inter-study heterogeneity was reduced by batch correction using an empirical Bayesian framework implemented in the ComBat R package36,37, and pooled batch-effect-corrected metagenomic data showed a significant difference between microbiota of Rs and NRs (Extended Data Fig. 7b,c; P = 0.002).”
From Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1 (2022) · cites this paper as [37]
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- Authors
- Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter, C., Weiss-Solis, D. Y., Duque, R., Bersini, H., & Nowe, A.
- Affiliations
- Vrije Universiteit Brussel (BE)
- Université Libre de Bruxelles (BE)
- Corresponding author
- Lazar, C.
- Meganck, S.
- Taminau, J.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-07-31
- Volume
- 14
- Issue
- 4
- Pages
- 469-490
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2147013674
- Cited by
- 333
- References
- 62
- Citations by year
- 2026: 19 · 2025: 20 · 2024: 14 · 2023: 29 · 2022: 37 · 2021: 29 · 2020: 43 · 2019: 35 · 2018: 26 · 2017: 17 · 2016: 21 · 2015: 20 · 2014: 15 · 2013: 7 · 2012: 1
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Briefings in Bioinformatics (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Funders
- Innoviris
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- batch effect correction, gene expression microarrays, genomic data integration, MAGE
- MeSH terms
- Computer Simulation, Genetic Variation, Gene Expression, Genome, Oligonucleotide Array Sequence Analysis, Genomics, Databases, Genetic, Transcriptome
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