[BioC] design a modelMatrix with no common references

[Giusy Della Gatta]

Thank you James,
You are right it is a lot of work and maybe I can find better solutions.
Given that they are two mose Knock outs and then two independent experiments, what if
I split them  in two independent ones and I  proceed
with their normalization and only at the end I will compare
the datasets of differentially expressed genes: Myc_CD3/Myc-PBS VERSUS Rag-CD3/Rag-PBS?

Thank you!
G


 
-----Original Message-----
From: James W. MacDonald [mailto:jmacdon at med.umich.edu] 
Sent: Friday, July 10, 2009 10:15 AM
To: Giusy Della Gatta
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] design a modelMatrix with no common references

Hi Guisy,

You really have two different experiments here, so I don't know if limma 
is going to want to do things automatically for you without warnings or 
incorrect model matrices. However, I think you want to use the 
parameters argument to modelMatrix() rather than the ref argument (since 
you have two different reference samples).

 > targets <- matrix(paste(rep(c("Myc","Rag"), each=4), 
rep(c("CD3","PBS"), each=2, times=3)[2:9], sep = "_"), byrow=T, ncol=2)
 > targets
      [,1]      [,2]
[1,] "Myc_CD3" "Myc_PBS"
[2,] "Myc_PBS" "Myc_CD3"
[3,] "Rag_CD3" "Rag_PBS"
[4,] "Rag_PBS" "Rag_CD3"
 > colnames(targets) <- c("Cy3","Cy5")
 > rownames(targets) <- paste("Array", 1:4)
 > targets
         Cy3       Cy5
Array 1 "Myc_CD3" "Myc_PBS"
Array 2 "Myc_PBS" "Myc_CD3"
Array 3 "Rag_CD3" "Rag_PBS"
Array 4 "Rag_PBS" "Rag_CD3"
 > parameters <- cbind(First=c(-1,1,0,0), Second=c(0,0,-1,1))
 > rownames(parameters) <- c("Myc_PBS","Myc_CD3","Rag_PBS","Rag_CD3")
 > parameters
         First Second
Myc_PBS    -1      0
Myc_CD3     1      0
Rag_PBS     0     -1
Rag_CD3     0      1
 > modelMatrix(targets, parameters)
Found unique target names:
  Myc_CD3 Myc_PBS Rag_CD3 Rag_PBS
         First Second
Array 1    -1      0
Array 2     1      0
Array 3     0     -1
Array 4     0      1
Warning message:
In modelMatrix(targets, parameters) :
   number of parameters should be one less than number of targets

But that seems like a lot of work, as the parameters matrix is exactly 
the model matrix you want.

Best

Giusy Della Gatta wrote:
> Hi everybody,
> 
> I have Agilent two colors expression arrays in which have been analyzed 
> two KO mice samples (myc-/- and Rag -/-) treated with CD3 and with PBS.
> I have a total of 4 arrays composed as follows:
>      Sample   Cy3         Cy5
> 1. Myc24CD3	Myc_CD3	Myc_PBS (Swap)	
> 2. Myc24PBS	Myc_PBS	Myc_CD3	
> 3. Rag24CD3	Rag_CD3	Rag_PBS (Swap)		
> 4. Rag24PBS	Rag_PBS	Rag_CD3	
> 
> After the normalization I don't know
> how to proceed for the  construction of the model matrix.
> 
> By using the suggestions of the  "Direct Two Color Designs" example (chapter 7.4 LIMMA guide)
> I did:
> 
> 
>> targets
>                  FileName     Cy3     Cy5 Collection_time
> 1 3_Myc24CD3gr_Myc24PBSre Myc_CD3 Myc_PBS             24h
> 2 9_Myc24PBSgr_Myc24CD3re Myc_PBS Myc_CD3             24h
> 3 5_Rag24CD3gr_Rag24PBSre Rag_CD3 Rag_PBS             24h
> 4 4_Rag24PBSgr_Rag24CD3re Rag_PBS Rag_CD3             24h
> 
>> designmyc= modelMatrix(targets, ref="Myc_PBS")
> Found unique target names:
>  Myc_CD3 Myc_PBS Rag_CD3 Rag_PBS
> 
>> designmyc
>      Myc_CD3 Rag_CD3 Rag_PBS
> [1,]      -1       0       0
> [2,]       1       0       0
> [3,]       0      -1       1
> [4,]       0       1      -1
> 
>> fit = lmFit(MA.Rq, designmyc)
> Coefficients not estimable: Rag_PBS 
> Warning message:
> Partial NA coefficients for 45018 probe(s)
> 
> 
> But at this point I calculated just the ratios of Myc_CD3/Myc_PBS
> and Rag_Myc/Myc_PBS (I am not really interested in this last one!).
> How can I specify in the model matrix design that I need two different references
> to calculate the following logratios:  Myc_CD3/Myc_PBS, Rag_Myc/Rag_PBS?
> 
> 
> Thank you in advance!
> Giusy
>  
> 
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-- 
James W. MacDonald, M.S.
Biostatistician
Douglas Lab
University of Michigan
Department of Human Genetics
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