Metabolomics data are inevitably subject to a component of unwanted variation, due to factors such as batch effects, matrix effects, and confounding biological variation. This package is a collection of functions designed to implement, assess, and choose a suitable normalization method for a given metabolomics study (De Livera et al (2015) <doi:10.1021/ac502439y>).
| Version: | 0.22 |
| Depends: | R (≥ 3.4.0) |
| Imports: | RGtk2, impute, crmn, gplots, limma, plotly, statTarget, Biobase, DiffCorr, e1071, AUC, htmlwidgets, metabolomics, MetNorm, ggplot2, GGally, grid |
| Suggests: | knitr, rmarkdown |
| Published: | 2017-10-22 |
| Author: | Alysha M De Livera, Gavriel Olshansky |
| Maintainer: | Alysha M De Livera <alyshad at unimelb.edu.au> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | NormalizeMets results |
| Reference manual: | NormalizeMets.pdf |
| Vignettes: |
User Manual |
| Package source: | NormalizeMets_0.22.tar.gz |
| Windows binaries: | r-devel: NormalizeMets_0.22.zip, r-release: NormalizeMets_0.22.zip, r-oldrel: not available |
| OS X El Capitan binaries: | r-release: not available |
| OS X Mavericks binaries: | r-oldrel: not available |
| Old sources: | NormalizeMets archive |
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