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

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0503-2) contains supplementary material, which is available to authorized users.

A Minimal DNA Methylation Signature in Oral Tongue Squamous Cell Carcinoma Links Altered Methylation with Tumor Attributes

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Inuteroarsenic exposure and epigenome-wide associations in placenta, umbilical artery, and human umbilical vein endothelial cells

“…Data were obtained and processed from raw methylation image files and normalized using internal control probes via the functional normalization method with 2 principal components to account for technical variation between samples using the minfi package of R. 54 DNA methylation was estimated at each CpG as the fraction of DNA molecules whose target CpG loci is methylated and referred to as b-values. Measurements at CpG loci on X and Y chromosomes were excluded from the analysis to avoid gender-specific methylation bias.…”

WALT: fast and accurate read mapping for bisulfite sequencing

“…All tests were run using identical Intel Xeon processors using a single core (details in Supplementary Material). We used three datasets: (1) SRR1532534, (2) SRR948855 (Fortin et al, 2014) and (3) SRR2296821 (Yong-Villalobos et al, 2015) for this comparison, evaluating both speed and accuracy. All of them are paired-end data by Illumina HiSeq 2000.…”

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