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Analysis of High-throughput DNA Methylation Bead Arrays Utilizing Bayesian Genotyping Algorithms

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12 Author(s)
Yuanyuan Xiao ; Dept. of Epidemiology & Biostat., Univ. of California, San Francisco, CA ; Segal, M.R. ; Houseman, E.A. ; Wiemels, J.
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We present a statistical framework, MAMS-M, for determining the methylation status of hundreds of cancer related CpG sites. MAMS-M extends and adapts our previous SNP genotyping algorithm, MAMS, to methylation bead array data, exploiting the similarities in data structure between the two platforms. MAMS-M employs a multi-site, multi-array model-based clustering approach to derive initial methylation calls, and then recalibrate these calls and associated confidence measures using site-specific adjustments. We demonstrate the performance of MAMS-M using a real-life data set with cancer applications.

Published in:

BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on  (Volume:1 )

Date of Conference:

27-30 May 2008

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