[BioC] LIMMA:design and contrast matrices for biological and technical replicates

[Adaikalavan Ramasamy]

Your "nic" variable is confounded with "contr" variable, therefore not
estimable.

Can you clarify the following please :
 1. Do you expect File1 and File2 to be biologically identical ? 
 2. Do you expect contr1, contr2, contr3 to be identical (i.e. your used
a universal RNA pool for all six arrays) ?

If the answer is yes to both of the above, then you might need something
along the lines of

samples <- as.factor( c("nic1", "nic1", "nic2", "nic2", "nic3", "nic3"))
design  <- model.matrix( ~ -1 + samples )

  samplesnic1 samplesnic2 samplesnic3
1           1           0           0
2           1           0           0
3           0           1           0
4           0           1           0
5           0           0           1
6           0           0           1

Regards, Adai


On Wed, 2006-04-12 at 12:12 -0400, Mike White wrote:
> 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
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
>