normFit: Fit a normalization model
In crmn: CCMN and Other Normalization Methods for Metabolomics Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Fit the parameters for normalization of a metabolomics data set.

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

`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`.
`formula`	if fitfunc has formula interface or not
`...`	passed on to `standardsFit`, `standards`, analytes

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.

t1: divide by row-means of the L_2 scaled internal standards
one: divide by value of a single, user defined, internal standard
totL2: divide by the square of sums of the full dataset
nomis: See Sysi-Aho et al.
crmn: See Redestig et al.

a normalization model

Henning Redestig

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

normPred, standards, model.matrix

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))