[BioC] limma

[Claire Vandiedonck]

Hi everyone,

I am not a statistician, so please forgive me if my question is trivial. 
I have some difficulties with the factorial design in limma with 2 
factors with 2 and 3 levels respectively.

Say the two factors are "Treatment" and "Source", with respectively two 
and three levels:
 >Treatment
 [1] No  No  No  Yes Yes Yes No  No  No  Yes Yes Yes No  No  No  Yes Yes Yes
Levels: No Yes
 > Source
 [1] RNA1 RNA1 RNA1 RNA1 RNA1 RNA1 RNA2 RNA2 RNA2 RNA2 RNA2 RNA2 RNA3 
RNA3 RNA3 RNA3 RNA3 RNA3
Levels: RNA1 RNA2 RNA3

I have three replicates of each condition per source, hence 18 arrays 
(which are Affymetrix).
My target is as follow:
   Source Treatment
1    RNA1        No
2    RNA1        No
3    RNA1        No
4    RNA1       Yes
5    RNA1       Yes
6    RNA1       Yes
7    RNA2        No
8    RNA2        No
9    RNA2        No
10   RNA2       Yes
11   RNA2       Yes
12   RNA2       Yes
13   RNA3        No
14   RNA3        No
15   RNA3        No
16   RNA3       Yes
17   RNA3       Yes
18   RNA3       Yes

I create the following design matrix:
design <- model.matrix(~0+Treatment*Source)

and apply the linear model:
fit <- lmFit(data, designl)

But then I fail in making the contrasts. I only manage to get pairwise 
effects, while I would like the following three effects: main effect of 
the treatment, main effect of the source, interaction between effect and 
source. Could any one help me please?

Alternatively, I used the group parametrization approach with one factor 
with 6 levels corresponding to my 6 combinations. This way, I managed to 
extract each of my contrasts of interest, but I would really like to 
understand how to use limma in a more classical statistical way, like in 
a two-way anova. Any help would be very much appreciated as my data are 
susceptible to get more complicated with additional treatments and even 
paired samples. Many thanks in advance.

BW

Claire


-- 
Claire Vandiedonck, PhD
Post-doctoral scientist
Wellcome Trust Centre for Human Genetics
Oxford University
Roosevelt Drive
Oxford, OX3 7BN
office: +44(0)1865 287 829
lab: +44(0)1865 287 531
mail: vandiedo at well.ox.ac.uk