Hi all,

I have used limma before for differential analysis but only for very simple
experiments comparing two conditions. I am helping a colleague make the
most out of an old dataset and wanted some advice. We have:

   - 8 affymetrix mouse4302 arrays
   - 2 conditions: wildtype and mutant
   - Samples were prepared at post-natal day 6 and were originally un-sexed

A principle components analysis showed samples weren't grouping by their
condition and but samples were separated along PC1 by their sex (obvious
because of Xist and a number of Y-linked genes). So:

1) Is it possible to incorporate the gender of the samples into the
analysis now that they are known?

The current design and contrast matrices I am using are as follows:

# Design matrix
condition <- factor(c("wt", "mu", "wt", "mu", "wt", "mu", "wt", "mu"))
condition <- relevel(condition, "wt")
design <- model.matrix(~0 + condition)
colnames(design) <- c("wt", "mu")
# Contrast matrix
cont.matrix <- makeContrasts(wtVmt = mu - wt, levels=design)

Using this simple design I get a very small number of significant results
(2). So if I wanted to incorporate gender I think I know about the design
matrix but am unsure about the contrast matrix.

# Design matrix
condition <- factor(c("wt", "mu", "wt", "mu", "wt", "mu", "wt", "mu"))
condition <- relevel(condition, "wt")
gender <- factor(c('male', 'male', 'female', 'male', 'female', 'female',
'male', 'female'))
design <- model.matrix(~0 + gender + condition)
colnames(design) <- c("female", "male", "mu")
# Contrast matrix
cont.matrix <- ???

2) What should the cont.matrix look like if I want to compare wildtype vs
mutant using the levels "female" "male" "mu"?

I have read through the examples in the limma user guide but the answer is
not obvious. Thanks for any help.

Ed

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