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

Bioinformatics. 2016 Feb 15;32(4):593-5. doi: 10.1093/bioinformatics/btv615. Epub 2015 Oct 24.


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

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Autoimmune Diseases / genetics*
  • Cell Lineage / genetics*
  • DNA Methylation*
  • Diabetes, Gestational / genetics*
  • Female
  • Humans
  • Oligonucleotide Array Sequence Analysis
  • Organ Specificity*
  • Pregnancy
  • Software*