crmn (version 0.0.8)

normFit: Fit a normalization model...

Description

Fit a normalization model

Usage

normFit(object, method, one="Succinate_d4", factors, lg=TRUE,
    fitfunc=lm, ...)

Arguments

object

an ExpressionSet or a matrix (with samples as columns) in which case the standards must be passed on via ...

method

chosen normalization method

one

single internal standard to use for normalization

factors

column names in the pheno data slot describing the biological factors. Or a design matrix directly.

lg

logical indicating that the data should be log transformed

fitfunc

the function that creates the model fit for normalization, must use the same interfaces as lm.

...

passed on to standardsFit, standards, analytes

Value

• a normalization model

Details

Fit the parameters for normalization of a metabolomics data set.

Normalization is first done by fitting a model and then applying that model either to new data or the same data using normPred. Five different methods are implemented. [object Object],[object Object],[object Object],[object Object],[object Object]

References

Sysi-Aho, M.; Katajamaa, M.; Yetukuri, L. & Oresic, M. Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics, 2007, 8, 93

Redestig, H.; Fukushima, A.; Stenlund, H.; Moritz, T.; Arita, M.; Saito, K. & Kusano, M. Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data Anal Chem, 2009, 81, 7974-7980

See Also

normPred, standards, model.matrix

Examples

data(mix)
nfit <- normFit(mix, "crmn", factors="type", ncomp=3)
slplot(sFit(nfit)$fit$pc, scol=as.integer(mix$runorder))
## same thing
Y <- exprs(mix)
G <- model.matrix(~-1+mix$type)
isIS <- fData(mix)$tag == 'IS'
nfit <- normFit(Y, "crmn", factors=G, ncomp=3, standards=isIS)
slplot(sFit(nfit)$fit$pc, scol=as.integer(mix$runorder))