[BioC] Limma design question (model interactions)

[Naomi Altman]

You have a compound hypothesis, so you will need to do 2 contrasts:

i.e. you want B=Ref intersect A != Ref

Interaction means that the effect of B depends on 
the level of A observed.  Since you do not 
observe A and B together in the same samples, you cannot test interaction.

--Naomi


At 03:10 PM 6/4/2008, Wolfgang Raffelsberger wrote:
>Dear list,
>here a question about the appropriate design of 
>a specific test layout for use with limma. The 
>problem seems almost trivial, but among the 
>numerous postings I haven’t found something resolving my problem :
>
>I have 2 samples (A and B) that were hybridized 
>against a (common) reference (Ref). Now I would 
>like to find those genes that differ from A to 
>Ref but NOT in B to Ref (i.e. A and B would differ, but without those AvR~0 ).
>In my mind, differentially regulated genes in 
>both A and B could be described as an 
>interaction, but the code shown below for 
>integrating an interaction component won’t work to give the answer :
>
>The experiment layout :
>FileName Cy3 Cy5
>array1 A Ref
>array2 A Ref
>array3 B Ref
>array4 B Ref
>
> > library(limma)
> > dat1 <- 
> matrix(runif(200,-3,3),nc=4,dimnames=list(as.character(1:50),c("AvR","AvR","BvR","BvR"))) 
> # just an example ..
> > f.A <- c(1,1,0,0)
> > f.B <- c(0,0,1,1)
> > design2 <- model.matrix(~f.A + f.A:f.B )
> > fit2 <- lmFit(dat1, design2)
>Coefficients not estimable: f.A:f.B
> > fit2 <- eBayes(fit2)
>Warning message:
>In ebayes(fit = fit, proportion = proportion, 
>stdev.coef.lim = stdev.coef.lim) :
>Estimation of var.prior failed - set to default value
>
> > topTable(fit2, coef=1) # any A or B different 
> to Ref, but contains also those with AvR ~ BvR or AvR ~ 0
> > topTable(fit2, coef=2) # just for DE genes as 
> A vs B, but contains also those with AvR ~0
> > topTable(fit2, coef=3) # just returns just NAs
>
>I suppose a part of the problem is, that the 
>last column of design2 holds just 0s :
> > design2[,3] # column for interactions contains only 0s
>1 2 3 4
>0 0 0 0
>
>I also tried (following a posting from Gordon Smyth, 2006-06-01) :
> > design3 <- model.matrix(~ f.A * f.B )
> > design3 # the matrix has one more column, 
> again, the column for interactions contains only 0s
>And finally with lmFit() & eBayes() I got the 
>same results as from lmFit(dat1, design2).
>
>Of course there is the less perfect solution of 
>doing 2 comparisons (1: A v Ref, 2: A vs B; as 
>described in Limma use guide chapter 8.4) and 
>then seeking only genes at the intersection. But 
>I'm surprised I can't get this as a single model working !
>Do you have any suggestions how the design / 
>model-matrix should be set to test (from an 
>integrated model) for differences in A to Ref but NOT in B to Ref ?
>
>Thank’s very much,
>Wolfgang Raffelsberger
>
>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
>Wolfgang Raffelsberger, PhD
>Laboratoire de BioInformatique et Génomique Intégratives
>CNRS UMR7104, IGBMC 1 rue Laurent Fries,  67404 Illkirch  Strasbourg,  France
>Tel (+33) 388 65 3300         Fax (+33) 388 65 3276
>wolfgang.raffelsberger at igbmc.fr
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
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