Hello all, I've read Limma user's guide completely but I'm still unsure about the way I am making my design matrix. I have two groups (pre and post) for each subject. Two treatments (treatment and placebo) and sex. I want to find out if there are any differentially expressed genes between post-treatment and pre-treatment. The targets looks like this:

Sample_Name Sample_Group Sample_Source Treatment Sex
        003         post           A01   Placebo   M
       21v7          pre           B01 Rapamycin   M
       42v1          pre           C01 Rapamycin   M
      030v7          pre           F01 Rapamycin   F
       46v1          pre           G01 Rapamycin   M
       14v7          pre           H01   Placebo   M
        003          pre           A01   Placebo   M
       42v4         post           C01 Rapamycin   M
      030v9         post           F01 Rapamycin   F
       46v4         post           G01 Rapamycin   M
       14v9         post           H01   Placebo   M

I know that Sample_Group is my within-individual factor and Treatment is between-individual factor, but still do not get answers. Here is my code. I would really appreciate if anyone can help me. I am so confused.

groups <- factor($targets$Sample_Group) ## pre & post
treatment  <- factor(targets$Treatment) ## Rapamycin & placebo
individual <- factor(targets$Sample_Source)
gender <- factor(targets$Sex)
design1 <- model.matrix(~0+groups*treatment, data=targets)
dupfit <- duplicateCorrelation(eset, design1,
                               block=targets$Sample_Source)
fit <- lmFit(eset, design1,correlation=dupfit$consensus, block=individual)
fit2 <- eBayes(fit)

Thanks a lot.

Shiva.