Batch effect correction for genome-wide methylation data with Illumina Infinium platform
Open accessAbstract
BACKGROUND: Genome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data. METHODS: We evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average β value (QNβ); two step quantile normalization at probe signals implemented in "lumi" package of R (lumi); and quantile normalization of A and B signal separately (ABnorm). Subsequent Empirical Bayes (EB) batch adjustment was also evaluated. RESULTS: Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects (Dataset 1), normalization alone appeared adequate and "lumi" showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QNβ, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets. CONCLUSION: Genome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.
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Disappearance of batch effects is a useful but insufficient indicator of normalization performance because it does not show whether true differences can still be detected.
“the disappearance of batch effects is a useful indicator [12], but not sufficient as a performance indicator because it does not indicate whether true differences can”
From A data-driven approach to preprocessing Illumina 450K methylation array data (2013) · cites this paper as [12]
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- Authors
- Sun, Z., Chai, H. S., Wu, Y., White, W. M., Donkena, K. V., Klein, C. J., Garovic, V. D., Therneau, T. M., & Kocher, J.-P. A.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Kocher, Jean-Pierre A
- ORCID
- Venue
- BMC Medical Genomics
- Published
- 2011-12
- Volume
- 4
- Issue
- 1
- Pages
- 84-84
- ISSN
- 1755-8794 (ISSN-L)
- PMC
- PMC3265417
- OpenAlex
- W2108947463
- Cited by
- 108
- References
- 32
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 1 · 2022: 6 · 2021: 4 · 2020: 3 · 2019: 9 · 2018: 9 · 2017: 6 · 2016: 13 · 2015: 13 · 2014: 11 · 2013: 17 · 2012: 9
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Genomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Mayo Clinic
- Mayo Foundation for Medical Education and Research
- Center for Individualized Medicine, Mayo Clinic
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- batch effect correction, DNA methylation, genome-wide methylation profiling, quantile normalization, data normalization, CpG methylation
- MeSH terms
- Adult, Humans, Male, Reproducibility of Results, Genome, Human, CpG Islands, DNA Methylation, Oligonucleotide Array Sequence Analysis, Databases, Genetic
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