standardsFit: Standards model

Description

Fit a model which describes the variation of the labeled internal standards from the biological factors.

Usage

standardsFit(object, factors, ncomp = NULL, lg = TRUE, fitfunc = lm, ...)

Arguments

object

an ExpressionSet or a matrix. Note that if you pass amatrix have to specify the identity of the standards by passing the appropriate argument to standards.

factors

the biological factors described in the pheno data slot if object is an ExpressionSet or a design matrix if object is a matrix.

ncomp

number of PCA components to use. Determined by cross-validation if left NULL

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 Q2, pca (if pcaMethods > 1.26.0), standards and analytes

Value

a list containing the PCA/MLR model, the recommended number of components for that model, the standard deviations and mean values and Q2/R2 for the fit.

Details

There is often unwanted variation in among the labeled internal standards which is related to the experimental factors due to overlapping peaks etc. This function fits a model that describes that overlapping variation using a scaled and centered PCA / multiple linear regression model. Scaling is done outside the PCA model.

Examples

Run this code

# NOT RUN {
data(mix)
sfit <- standardsFit(mix, "type", ncomp=3)
slplot(sfit$fit$pc)
## same thing
Y <- exprs(mix)
G <- model.matrix(~-1+mix$type)
isIS <- fData(mix)$tag == 'IS'
sfit <- standardsFit(Y, G, standards=isIS, ncomp=3)
# }