[BioC] Limma final gene expression report

[Ankit Pal]

Dear All,
While looking at the Limma user guide, I came across
the following example

> targets <- readTargets("SwirlSample.txt")
> RG <- read.maimages(targets$FileName, source="spot")

> RG$genes <- readGAL()                     
> RG$printer <- getLayout(RG$genes)         
> MA <- normalizeWithinArrays(RG)           
> MA <- normalizeBetweenArrays(MA)          
> fit <- lmFit(MA, design=c(-1,1,-1,1))     
> fit <- eBayes(fit)                        
> options(digits=3)
> topTable(fit, n=30, adjust="fdr")         
ID        Name       M    A     t  P.Value    B
control   BMP2      -2.21 12.1 -21.1 0.000357 7.96
control   BMP2      -2.30 13.1 -20.3 0.000357 7.78
control   Dlx3      -2.18 13.3 -20.0 0.000357 7.71
control   Dlx3      -2.18 13.5 -19.6 0.000357 7.62
fb94h06 20-L12       1.27 12.0  14.1 0.002067 5.78
fb40h07  7-D14       1.35 13.8  13.5 0.002067 5.54

I have omitted a few rows and columns.
Here we see that after all the data transformations,
we get an output where the ranking for the probes in
an array is  done on the basis of the B value.
Notice that there are reapeating names for genes,
therefore for a set of replicates, within and across
arrays, each spot is reported separately as an
individual entity.
In the case of BMP2 from the above example, which
result do I consider?
Is there a way in which I can get a single result for
a set of replicates.
I am new to this package, so please do let me know if
there is a problem in my understanding the concept.
Thank you,
-Ankit