funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types.

TitlefuntooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types.
Publication TypeJournal Article
Year of Publication2016
AuthorsKlein KOros, Grinek S, Bernatsky S, Bouchard L, Ciampi A, Colmegna I, Fortin J-P, Gao L, Hivert M-F, Hudson M, Kobor MS, Labbe A, MacIsaac JL, Meaney MJ, Morin AM, O'Donnell K, Pastinen T, Van Ijzendoorn MH, Voisin G, Greenwood CMT
Date Published2016 Feb 15
KeywordsAutoimmune Diseases, Cell Lineage, Diabetes, Gestational, DNA Methylation, Female, Humans, Oligonucleotide Array Sequence Analysis, Organ Specificity, Pregnancy, Software

MOTIVATION: DNA methylation patterns are well known to vary substantially across cell types or tissues. Hence, existing normalization methods may not be optimal if they do not take this into account. We therefore present a new R package for normalization of data from the Illumina Infinium Human Methylation450 BeadChip (Illumina 450 K) built on the concepts in the recently published funNorm method, and introducing cell-type or tissue-type flexibility.RESULTS: funtooNorm is relevant for data sets containing samples from two or more cell or tissue types. A visual display of cross-validated errors informs the choice of the optimal number of components in the normalization. Benefits of cell (tissue)-specific normalization are demonstrated in three data sets. Improvement can be substantial; it is strikingly better on chromosome X, where methylation patterns have unique inter-tissue variability.AVAILABILITY AND IMPLEMENTATION: An R package is available at, and has been submitted to Bioconductor at

Alternate JournalBioinformatics
PubMed ID26500152
PubMed Central IDPMC4743629
Grant ListMOP-300545 / / Canadian Institutes of Health Research / Canada