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

doi:10.1093/bioinformatics/btv615

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.

Authors

Kathleen Oros Klein¹, Stepan Grinek¹, Sasha Bernatsky², Luigi Bouchard³, Antonio Ciampi⁴, Ines Colmegna⁵, Jean-Philippe Fortin⁶, Long Gao⁴, Marie-France Hivert⁷, Marie Hudson⁸, Michael S Kobor⁹, Aurelie Labbe⁴, Julia L MacIsaac¹⁰, Michael J Meaney¹¹, Alexander M Morin¹⁰, Kieran J O'Donnell¹², Tomi Pastinen¹³, Marinus H Van Ijzendoorn¹⁴, Gregory Voisin¹, Celia M T Greenwood¹⁵

Affiliations

¹ Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Ludmer Center for Neuroinformatics and Mental Health.
² Divisions of Rheumatology and Clinical Epidemiology, McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada.
³ ECOGENE-21, Centre intégré universitaire de santé et de service sociaux du Saguenay-Lac-Saint-Jean, QC G8H 3P7, Canada, Department of Biochemistry, Université de Sherbrooke, QC J1K 2R1, Canada.
⁴ Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada.
⁵ Division of Experimental Medicine, McGill University Health Centre, McGill University, Montreal, QC H3A 1A3, Canada.
⁶ Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA.
⁷ Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA, Department of Medicine, Division of Endocrinology, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.
⁸ Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Department of Medicine, McGill University Health Center, Montreal, QC H4A 3J1, Canada.
⁹ Canadian Institute for Advanced Research, Child, and Brain Development Program, Toronto, ON M5G 1Z8, Canada, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Vancouver, BC V5Z 4H4, Canada, Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada.
¹⁰ Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Vancouver, BC V5Z 4H4, Canada, Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada.
¹¹ Ludmer Center for Neuroinformatics and Mental Health, Canadian Institute for Advanced Research, Child, and Brain Development Program, Toronto, ON M5G 1Z8, Canada, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Vancouver, BC V5Z 4H4, Canada, Douglas Mental Health University Institute, McGill University, Montreal, QC H4H 1R3, Canada, Departments of Psychiatry, McGill University, Montreal, QC, Canada H3A 1A1, Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada.
¹² Douglas Mental Health University Institute, McGill University, Montreal, QC H4H 1R3, Canada.
¹³ Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada and.
¹⁴ Centre for Child and Family Studies, Leiden University, Leiden 2300 RB, The Netherlands.
¹⁵ Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Department of Biochemistry, Université de Sherbrooke, QC J1K 2R1, Canada, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada, Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada and.

Abstract

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.

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*

Grants and funding

MOP-300545/Canadian Institutes of Health Research/Canada