funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types.
|Title||funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Klein 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 Published||2016 Feb 15|
|Keywords||Autoimmune 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 https://github.com/GreenwoodLab/funtooNorm, and has been submitted to Bioconductor at http://bioconductor.org.
|PubMed Central ID||PMC4743629|
|Grant List||MOP-300545 / / Canadian Institutes of Health Research / Canada|