Hello everyone,
I need to make a design matrix, and I can use the approach outlined in
3.3.1 of the edgeR users guide, but I think that there may be a better way
to conduct the analysis.  I have 36 samples in a 3x2x2 incomplete factorial
design.  I have three genotypes (wild type, a single mutant, and a double
mutant), two tissues (roots and shoots), and two treatments (no trt, trt).
the mutants are m1 and m1/m2.  One approach would be to group the treatment
groups into a single variable Group, as is done in the edgeR guide, and
then follow the example, extracting interaction terms to find the
differences between each genotypes response.

There should be a different way to model this problem that is more / as
powerful as the previous method (right?).  Because in the double mutant the
gene expression is going to have a component that is dependent on each
mutation and then the interaction of each mutation.

So I am interested in trying to estimate the genes that are responsive to
treatment and dependent on the introduction of mut2 into the mut1
background (for tissues independently)

Does such a method exist, how can I do it / what should I read to
understand how to do it?

Thanks,

-- 
Sam McInturf

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