Hi,
I am just doing a straightforward analysis of mouse affy data using
affy,gcrma and limma
It is a simple 2 group design with not much complication to it. So this is a
minor problem that I have been scratching my head since the beggining of the
week, and cannot figure out the answer, so hopefully someone can help me.

After I read my data, normalize and analyze as follows:

library (affy)
library (limma)
library (gcrma)
Data <- ReadAffy()
eset <- gcrma(Data)
treatment<-c("SD","SD","NSD","NSD","NSD","NSD","NSD","NSD","NSD","NSD","NSD","SD","SD","SD","SD","SD","SD")
design <-model.matrix(~factor(treatment))
colnames(design) <- c("SD","NSD")
fit <- lmFit(eset, design)
fit <- eBayes(fit)

I just wanted to get all the normalized data for the GEO report, however I
get strange results regarding the scale of the expression values if I use
"exprs" from Biobase. Log Expression values range from ~1-4 if I do:

esetNL= exprs (eset)
esetlog=log(esetNL,2)
write.table(esetlog, file="Myfile_gcrma_matrix.txt", quote=F, sep="\t")

The dynamic range of log expression values I have usually observed ranges
from ~3-14, and it is what I get if I do:

>write.table(topTable(fit, coef=2, adjust="fdr", sort.by="A",number=50000),file="limma_SD.txt",
sep="\t")

although that only gives me the average expression values.
Is the data extracted by "exprs" already in log2 scale? can someone tell why
I am seeing such difference in scale/dynamic range? Am I not using "exprs"
correctly?

thanks very much in advance
Lucia

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