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Updated: Mar 31, 2026
Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
Published on: May 6, 2022
funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types
Kathleen Oros Klein1, Stepan Grinek1, Sasha Bernatsky2
1Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Ludmer Center for Neuroinformatics and Mental Health.
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.
