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Wolfgang Raffelsberger
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160
@wolfgang-raffelsberger-1805
Last seen 9.6 years ago
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
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.
Wolfgang Raffelsberger, PhD
Laboratoire de BioInformatique et G?nomique Int?gratives
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
