The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data
Open accessAbstract
Motivation: The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs. Results: Here we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform. Availability: BMIQ is freely available from http://code.google.com/p/bmiq/. Contact: a.teschendorff@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online
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Cites
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IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype
Turcan, S., Rohle, D., Goenka, A., Walsh, L. A., Fang, F., Yilmaz, E., Campos, C., Fabius, A. W. M., Lu, C., Ward, P. S., et al. (2012) · Nature · doi:10.1038/nature10866 · Cited by 1,974
High density DNA methylation array with single CpG site resolution
Bibikova, M., Barnes, B., Tsan, C., Ho, V., Klotzle, B., Le, J. M., Delano, D., Zhang, L., Schroth, G. P., Gunderson, K. L., et al. (2011) · Genomics · doi:10.1016/j.ygeno.2011.07.007 · Cited by 1,727 · cited 3×
“A key statistical issue with the Illumina 450k beadchip is that probes come in two different designs, which causes the methylation values derived from these two designs to exhibit widely different distributions (Dedeurwaerder et al., 2011).”
BackgroundMethodValidation of a DNA methylation microarray for 450,000 CpG sites in the human genome
Sandoval, J., Heyn, H., Moran, S., Serra-Musach, J., Pujana, M. A., Bibikova, M., & Esteller, M. (2011) · Epigenetics · doi:10.4161/epi.6.6.16196 · Cited by 990 · cited 3×
“A key statistical issue with the Illumina 450k beadchip is that probes come in two different designs, which causes the methylation values derived from these two designs to exhibit widely different distributions (Dedeurwaerder et al., 2011).”
BackgroundMethod
Cited by
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075
Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., Van Djik, S., Muhlhausler, B., Stirzaker, C., & Clark, S. J. (2016) · Genome biology · doi:10.1186/s13059-016-1066-1 · Cited by 1,480
A data-driven approach to preprocessing Illumina 450K methylation array data
Pidsley, R., Y Wong, C. C., Volta, M., Lunnon, K., Mill, J., & Schalkwyk, L. C. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-293 · Cited by 1,143
ChAMP: updated methylation analysis pipeline for Illumina BeadChips
Tian, Y., Morris, T. J., Webster, A. P., Yang, Z., Beck, S., Feber, A., & Teschendorff, A. E. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx513 · Cited by 1,091
Self-citationDNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis
Joubert, B. R., Felix, J. F., Yousefi, P., Bakulski, K. M., Just, A. C., Breton, C., Reese, S. E., Markunas, C. A., Richmond, R. C., Xu, C.-J., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.02.019 · Cited by 886
Metadata
- Authors
- Teschendorff, A. E., Marabita, F., Lechner, M., Bartlett, T., Tegner, J., Gomez-Cabrero, D., & Beck, S.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-11-21
- Volume
- 29
- Issue
- 2
- Pages
- 189-196
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2127431419
- Cited by
- 1,683
- References
- 5
- FWCI
- 17.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Teschendorff, A. E., Marabita, F., Lechner, M., Bartlett, T., Tegner, J., Gomez-Cabrero, D., & Beck, S. (2013). A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. https://doi.org/10.1093/bioinformatics/bts680
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