Hello, everyone:

I have a question related to conceptual understanding of lmFit.

I have the following experiment that I want to conduct, but I am not sure
which is the right way to use design matrix and contrasts. Here is the
experiment:

say I have 3 different strains that are genetically different, A, B and C
where A is the control. I also have two different treatments,
 T1 and T2. For each strain, I have 4 arrays for each treatment, so in
total, I have 24 arrays. What I want to find out is the significantly
differentially expressed genes for the following comparison:
1) for control strain A:  T1 vs T2
2) under T1, B vs. A (control)
3) under T1, C vs. A
4) for B, T1 vs T2
5) for C, T1 vs T2
6) interaction term of A and B , T1 and T2
7) interaction term of A and C, T1 and T2.

There are two ways I could use lmFit

One is:

for the design matrix, I will include all 3 strains and 2 conditions,
I use the following code:
            A_T1, A_T2, B_T1, B_T2, C_T1, C_T2
sample1:   1      ,0         ,0,        0,      0  ,         0
sample2 :

Then make a contrast matrix and follow the code below:

fitGene<-lmFit(gene,design=design,weights=arrayWt);
 fitGene2<-contrasts.fit(fitGene,cont.matrix)
fitGene2<-eBayes(fitGene2,proportion=p);


Two:
Instead of using all samples at one time to fit into a lmFit function, I use
two design matrix only involves A and B, T1 and T2,
and second design matrix that involves A and C, T1 and T2, and make contrast
matrix and fit separately. and later on I can compare these two
results if I want to.



The question I have is: which one is the right one? For the first method, I
will have large DOF , and much lower p-values, but it was testing the
same thing as the second one, so am I creating an artifact? Thanks for
your help!




Sabrina

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