Hello,

	I have started using limma to analyze data obtained from experiments  
examining the effects of nicotine exposure on gene expression in  
defined regions of the central nervous system. I am using R v2.2.1  
and limma v2.4.13 running under linux. The data are obtained using 2- 
color microarrays using probes made from three different mice and  
duplicate arrays for each set of probes, giving 6 arrays (3  
biological and two replicates):

        Cy3     Cy5
File1    contr1 nic1
File2    contr1 nic1
File3    contr2 nic2
File4    contr2 nic2
File5   contr3 nic3
File6   contr3 nic3

I have tried several different ways of setting up the design and  
linear model to fit, including one similar to the one suggested by  
Gordon in his posting of 28 Sept 05:

design<-cbind(nic1vscontr1=c(1,1,0,0,0,0), nic2vscontr2=c 
(0,0,1,1,0,0), nic3vscontr3=c(0,0,0,0,1,1))
cont.matrix<- makeContrasts(nicvscontr= c(1,1,1)/3, levels=design)

this does return  results (of course, how meaningful they are  
requires more work...).

However, I also tried an alternate way of setting things up following  
an example in section 23.5 ("Technical replication") in Gordon's  
chapter in the Bioconductor book in which one of the controls is set  
as a reference and everything is done in relation to this. That  
particular example represented a more complex situation than the one  
here, but I wanted to see how this compared to the other method and  
assumed that it could be applied to my situation.:

design<- modelMatrix(targets, ref= "contr1")
Found unique target names:
  nic1  nic2  nic3  contr1 contr2 contr3
colnames(design)

[1] "nic1" "nic2" "nic3" "contr2" "contr3"



the design matrix is as follows

                         nic1   nic2   nic3    contr2      contr3
File1                  1       0           0         0                0
FIle2                  1       0           0          0               0
File3                 0        1           0         -1              0
File4                 0       1           0          -1              0
File5                 0         0         1           0              -1
File6                 0         0         1           0             -1

which is what I expected


however when I try to fit the data the following happens

fit<- lmFit(MA,design)
Coefficients not estimable: contr2 contr3

I am a neophyte with both microarrays and limma, and am still feeling  
my way around setting up design and contrast matrices. However, I  
can't understand why the second method fails. Any insights?

Thanks

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

phone: 215-762-2355
fax: 215-762-4850



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