normPred: Predict for normalization

In crmn: CCMN and Other Normalization Methods for Metabolomics Data

Description

Predict the normalized data using a previously fitted normalization model.

Usage

normPred(normObj, newdata, factors = NULL, lg = TRUE, predfunc = predict, ...)

Arguments

normObj	the result from normFit
newdata	an ExpressionSet or a matrix (in which case the standards must be passed on via ...), possibly the same as used to fit the normalization model in order to get the fitted data.
factors	column names in the pheno data slot describing the biological factors. Or a design matrix.
lg	logical indicating that the data should be log transformed
predfunc	the function to use to get predicted values from the fitted object (only for crmn)
...	passed on to standardsPred, standardsFit, odestandards, analytes

Details

Apply fitted normalization parameters to new data to get normalized data. Current can not only handle matrices as input for methods 'RI' and 'one'.

Value

the normalized data

Author(s)

Henning Redestig

See Also

normFit

Examples

data(mix)
nfit <- normFit(mix, "crmn", factor="type", ncomp=3)
normedData <- normPred(nfit, mix, "type")
slplot(pca(t(log2(exprs(normedData)))), scol=as.integer(mix$type))
## same thing
Y <- exprs(mix)
G <- with(pData(mix), model.matrix(~-1+type))
isIS <- fData(mix)$tag == 'IS'
nfit <- normFit(Y, "crmn", factors=G, ncomp=3, standards=isIS)
normedData <- normPred(nfit, Y, G, standards=isIS)
slplot(pca(t(log2(normedData))), scol=as.integer(mix$type))

crmn documentation built on March 26, 2020, 8:35 p.m.