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

By Jean-Philippe Fortin, A. Labbe, M. Lemire, B. Zanke, Thomas J. Hudson, Elana J. Fertig, Celia M. T. Greenwood, Kasper D. Hansen. Genome Biology, 2014

762 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/16cf6f149a90cc14a2559b74673b5bade3f7460c

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Citations in the corpus

Epigenetic Signatures of Cigarette Smoking (2016, background): “Of these, 424 CpGs associated with the expression of 285 genes were replicated at FDR 0.0001 in 1264 CD14+ samples from the MESA.35 These genes are associated with pathways similar to those described earlier (Table XIII in the Data Supplement).”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “The processing step included an unsupervised functional normalization step that has been previously implemented for Illumina 450K methylation arrays (Fortin et al., 2014).”
New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs (2016, result): “For comparison, the dataset was normalized using the functional normalization method (Fortin et al., 2014), which yielded similar results.”
Harmonization of multi-site diffusion tensor imaging data (2017, methodology): “2.3 Harmonization methods We propose to use and adapt five statistical harmonization techniques for DTI data: global scaling, functional normalization [Fortin et al, 2014], RAVEL [Fortin et al, 2016a], Surrogate Variable Analysis (SVA)[Leek and Storey, 2007, 2008] and ComBat [Johnson et al, 2007].”
Harmonization of cortical thickness measurements across scanners and sites (2017, methodology): “It has also been proposed to use functional normalization, originally developed in (Fortin et al., 2014), for harmonizing DTI scalar maps.”
Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi (2016, methodology): “…(Maksimovic, Gordon, and Oshlack, 2012), stratiﬁed quantile normalization as discussed in Touleimat and Tost (2012) and Aryee et al. (2014), single-sample noob background correction followed by dye bias correction (Triche et al., 2013), and functional normalization (Fortin, Labbe, et al., 2014).”
SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions (2018, methodology): “Indeed, in a recent comparison for EPIC normalization methods, the minfi authors showed that preprocessing with noob (single-sample implementation, dubbed ssNoob) and dye-bias correction outperformed explicit normalization methods exemplified by funnorm (63).”
Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes (2016, background): “Funnorm (29) uses embedded control probes for HM450 data normalization but we show that the internal bisulfite conversion probes actually are influenced by real biology as they likely contain CpA probes, and can be problematic when samples with high CpH levels are analyzed.”
Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution (2016, methodology): “For the analysis of DNA methylation data, the functional normalized beta-values [45] were transformed to M values, and again lmFit was used to obtain test statistics for the covariate of interest.”