I am doing a quality control for an Affymetrix Mouse Gene ST 2.0 microarray using RMA method. As far as I understand, this method performs a background correction, log2 transformation and then a quantile normalization. This ensures that the distribution of intensities between different microarrays is the same.
However, I have read that a step of summarisation follows the previous ones, where a linear model is fit to each probe set accounting for the probe affinity effect, the log scale expression level for the array and an error term. Also, as far as I understand, this is done with the fitProbeLevelModel() function of the oligo package.
When I read the documentation of the function, it states the following:
Fits robust Probe Level linear Models to all the (meta)probesets in an FeatureSet. This is carried out on a (meta)probeset by (meta)probeset basis.
fitProbeLevelModel(object, background=TRUE, normalize=TRUE, target="core", method="plm", verbose=TRUE, S4=TRUE, ...)
My questions are the following:
(1) What is the aim of this linear model for quality control?
(2) What is the probe affinity effect?
(3) The function says that it summarises (meta)probesets, what is the difference between probesets?
(4) The function by default normalises and background corrects. This step was not supposed to be done before? If I have already normalized using RMA, should I set the parameter normalize=TRUE?