Dear ALL,

based on an experimental design of testing 4 different substances("treatments") , dataset grows incrementally, as about on average a month period i get small batches of 5 CELs each(one control and 4 "treatments") which grow as beside the first batch, now i got the second batch which are "technical replicates" of the first batch. The platform is Affymetrix PrimeView(PM-only). So my main and very important question is:

as there is a "batch effect"(but on the same array batches) regarding the time of "creating" getting and each batch, should i preprossess each batch with the fRMA algorithm like this:

i.e. batch1 < ReadAffy(..)

norm1 <- frma(batch_1, background="rma", normalize="quantile", summarize="robust_weighted_average", target="probeset) ?

and then merge all the collected batches with some package such as inSilicoMerging which has a batch effect option ?

Or also there are other options in the frma() command that can also account for this ?

Please excuse me for my naive questions, but im a beginner in R, and i believe that this question is important, as in the end i want to unite these small batches i collect to have a final ExpressionSet

You can preprocess your data using fRMA, in the manner you suggest, on each batch of samples and then combine the data for analysis. This will allow you to avoid repeatedly preprocessing your data and still be able to make comparisons across batches. However, fRMA only addresses one specific type of batch effects (ones affecting individual probes), so you may need to perform additional batch effect correction / modeling. There are several ways to do this (searching the BioC help archives will turn up multiple options).