RT Journal Article
SR Electronic
T1 Functional normalization of 450k methylation array data improves replication in large cancer studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 002956
DO 10.1101/002956
A1 Jean-Philippe Fortin
A1 Aurélie Labbe
A1 Mathieu Lemire
A1 Brent W. Zanke
A1 Thomas J. Hudson
A1 Elana J. Fertig
A1 Celia M.T. Greenwood
A1 Kasper D. Hansen
YR 2014
UL http://biorxiv.org/content/early/2014/02/23/002956.abstract
AB We propose an extension to quantile normalization which 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 datasets 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.