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An 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. 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. Improved normalization of datasets containing multiple tissues can be expected to translate into increased power to detect associations of interest, due to the inferred reduction in residual error; funNorm and this extension funtooNorm are designed with this goal in mind.

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funtooNorm versioning

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funtooNorm classification

funtooNorm specifications

Software type:
Package
Restrictions to use:
None
Operating system:
Unix/Linux, Mac OS, Windows
Computer skills:
Advanced
Interface:
Command line interface
Biological technology:
Illumina
Programming languages:
R
Stability:
Stable
Issue URL:
https://github.com/GreenwoodLab/funtooNorm/issues

funtooNorm support

Maintainer

Credits

Publications

Institution(s)

Lady Davis Institute, Jewish General Hospital, Montreal, Canada; Ludmer Center for Neuroinformatics and Mental Health, McGill University Health Centre, McGill University, Montreal, Canada

Funding source(s)

This work was supported by the Ludmer Center for Neuroinformatics & Mental Health, and by the Canadian Institutes of Health Research operating grant MOP-300545.

Link to literature

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