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Valavanis I, Sifakis EG, Georgiadis P, et al. A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip. IEEE J Biomed Health Inform. 2014;18(3):817-23doi: 10.1109/JBHI.2014.2298351.
Valavanis, I., Sifakis, E. G., Georgiadis, P., Kyrtopoulos, S., & Chatziioannou, A. A. (2014). A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip. IEEE journal of biomedical and health informatics, 18(3), 817-23. https://doi.org/10.1109/JBHI.2014.2298351
Valavanis, Ioannis, et al. "A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip." IEEE journal of biomedical and health informatics vol. 18,3 (2014): 817-23. doi: https://doi.org/10.1109/JBHI.2014.2298351
Valavanis I, Sifakis EG, Georgiadis P, Kyrtopoulos S, Chatziioannou AA. A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip. IEEE J Biomed Health Inform. 2014 May;18(3):817-23. doi: 10.1109/JBHI.2014.2298351. PMID: 24808224.
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IEEE J Biomed Health Inform. 2014 May;18(3):817-23. doi: 10.1109/JBHI.2014.2298351.

A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip.

IEEE journal of biomedical and health informatics

Ioannis Valavanis, Emmanouil G Sifakis, Panagiotis Georgiadis, Soterios Kyrtopoulos, Aristotelis A Chatziioannou

PMID: 24808224 DOI: 10.1109/JBHI.2014.2298351

Abstract

High-throughput DNA methylation profiling exploits microarray technologies thus providing a wealth of data, which however solicits rigorous, generic, and analytical pipelines for an efficient systems level analysis and interpretation. In this study, we utilize the Illumina's Infinium Human Methylation 450K BeadChip platform in an epidemiological cohort, targeting to associate interesting methylation patterns with breast cancer predisposition. The computational framework proposed here extends the--established in transcriptomic microarrays--logarithmic ratio of the methylated versus the unmethylated signal intensities, quoted as M-value. Moreover, intensity-based correction of the M-signal distribution is introduced in order to correct for batch effects and probe-specific errors in intensity measurements. This is accomplished through the estimation of intensity-related error measures from quality control samples included in each chip. Moreover, robust statistical measures exploiting the coefficient variation of DNA methylation measurements between control and case samples alleviate the impact of technical variation. The results presented here are juxtaposed to those derived by applying classical preprocessing and statistical selection methodologies. Overall, in comparison to traditional approaches, the superior performance of the proposed framework in terms of technical bias correction, along with its generic character, support its suitability for various microarray technologies.

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