funtooNorm
Computational Tool
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General Information
DescriptionAn R package for normalization of DNA methylation data when there are multiple cell or tissue types
Homepagehttps://github.com/GreenwoodLab/funtooNorm
Publications
funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types
Kathleen Oros Klein; Stepan Grinek; Sasha Bernatsky; Luigi Bouchard; Antonio Ciampi; Ines Colmegna; Jean-Philippe Fortin; Long Gao; Marie-France Hivert; Marie Hudson; Michael S. Kobor; Aurelie Labbe; Julia L. MacIsaac; Michael J. Meaney; Alexander M. Morin; Kieran J. O’Donnell; Tomi Pastinen; Marinus H. Van Ijzendoorn; Gregory Voisin; Celia M.T. Greenwood
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.
Contact: celia.greenwood@mcgill.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
DOI: https://doi.org/10.1093/bioinformatics/btv615
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