I am working my way through the Limma User's Guide and had a question  
about the design matrices for the example in section 8.4 (2 groups,  
same reference).
I understand the difference between the two design matrices in terms  
of what you can extract directly from the linear model and what has  
to be obtained by contrasts and how you directly construct the  
matrices using cbind as in the manual. I have two questions, one of  
which may trivial (i.e., stupid), and the other not. I will preface  
this by admitting that my knowledge of statistics beyond the very  
basics is relatively weak.

The non-trivial question:

I realize that more than one design matrix can be set up to analyze  
the same set of data (as in the example), and that similar results  
should be obtainable with each design. If you are eventually  
obtaining the same information from each design (i.e., identifying  
differentially expressed genes) what is the benefit of one design  
over the other- could one design produce a different level of  
statistical confidence that a given set of genes is differentially  
regulated? Is there any rule of thumb for choosing one design matrix  
over another?

The trivial (?) question

I set up the two types of design matrices using the factor Group and  
the model.matrix function as in the manual:

 > Group-> factor(c("WT","WT","MU","MU","MU"),levels=c("WT","MU"))
 > Group
[1] WT WT MU MU MU
Levels: WT MU
 > design-> model.matrix(~Group)
 > design
   (Intercept) GroupMU
1           1       0
2           1       0
3           1       1
4           1       1
5           1       1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$Group
[1] "contr.treatment"

 > design2-> model.matrix(~0+Group)
 > design2
   GroupWT GroupMU
1       1       0
2       1       0
3       0       1
4       0       1
5       0       1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$Group
[1] "contr.treatment"


I have not been able to find a clear explanation of what the tilde  
(~)  does in model.matrix to produce the design matrix, especially in  
the context of "~0+Group." Any idea as to where  I can get an  
explanation of how this works? (The 2445-page R manual wasn't any  
help!).

Thanks for you help!

Mike White



Michael M. White, Ph.D.
Department of Pharmacology & Physiology
MS #488
Drexel University College of Medicine
245 N. 15th Street
Philadelphia, PA 19102-1192





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