[HTML][HTML] Functional normalization of 450k methylation array data improves replication in large cancer studies

JP Fortin, A Labbe, M Lemire, BW Zanke, TJ Hudson… - Genome biology, 2014 - Springer
JP Fortin, A Labbe, M Lemire, BW Zanke, TJ Hudson, EJ Fertig, CMT Greenwood
Genome biology, 2014Springer
We propose an extension to quantile normalization that removes unwanted technical
variation using control probes. We adapt our algorithm, functional normalization, to the
Illumina 450k methylation array and address the open problem of normalizing methylation
data with global epigenetic changes, such as human cancers. Using data sets from The
Cancer Genome Atlas and a large case–control study, we show that our algorithm
outperforms all existing normalization methods with respect to replication of results between …
Abstract
We propose an extension to quantile normalization that removes unwanted technical variation using control probes. We adapt our algorithm, functional normalization, to the Illumina 450k methylation array and address the open problem of normalizing methylation data with global epigenetic changes, such as human cancers. Using data sets from The Cancer Genome Atlas and a large case–control study, we show that our algorithm outperforms all existing normalization methods with respect to replication of results between experiments, and yields robust results even in the presence of batch effects. Functional normalization can be applied to any microarray platform, provided suitable control probes are available.
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