Hi James, thanks for your reply! My intention to use the above mentioned (although improper as you mentioned) method was to be able to form clear tests. I assumed that when I am testing for genes responding to the covariate e.g. significant "cov" I am testing against the Baseline in the reference group (the group that has been assigned as reference by limma or lm). But I want to test against the baseline in all groups. Basically I wanted to adapt the procedure that has been proposed for factoral designs in the limma userguide (Section 8.7). I now realize that testing for significant coefficients for cov and interaction of group only seems to be a good choice for me. However I was a little puzzled by the fact that the effect size for cov would be only the effect size / slope within GroupA, which I somehow confused with a signifcant nonzero slope of the covariate exclusively in GroupA. Best, Moritz 2013/8/21 James W. MacDonald <jmacdon@uw.edu> > Hi Moritz, > > > On 8/20/2013 2:30 PM, Moritz Hess wrote: > >> Hi, >> I am investigating the global gene expression response to a continuous >> environmental covariate in two groups of individuals using limma. >> I am interested in genes whose expression is: >> A) correlated with the covariate >> B) differentially correlated with the covariate in the two groups of >> individuals (Interaction of Group and Covariate) >> >> In order to be able to set up the proper contrasts I split the Covariate >> in >> two Vectors where one Vector contains only the samples with the lowest >> level of the covariate >> e.g. CovBase = c(0,0,0,3,0,3,0,0) >> and where the other vector contains all the samples with higher levels of >> the covariate >> e.g. Cov = c(34,2,5,0,5,0,2,34) >> and set up a design matrix without intercept: >> >> ~ Group + CovBase + Cov >> >> >> The contrasts I am testing are specified as follows: >> Effect of Covariate: Cov-CovBase >> Interaction of Group and Covariate: (GroupA-GroupB) - (Cov-CovBase) >> >> Does this procedure makes sense ? >> > > It doesn't make sense to me. In the first place the formula you use will > create an intercept. Secondly, if you want the covariate to be continuous, > then why are you splitting it like that? And why do you think values of 3 > are lower than values of 2? > > If I were trying to do what you say you are doing, then I would just do > > cov <- c(34,2,5,3,5,3,2,34) > group <- factor(whateveryourgroupsare) > > design <- model.matrix(~cov*group) > > Best, > > Jim > > > >> Thank you very much in advance >> >> Moritz >> >> >> >> ______________________________**_________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.e="" thz.ch="" mailman="" listinfo="" bioconductor=""> >> Search the archives: http://news.gmane.org/gmane.** >> science.biology.informatics.**conductor<http: news.gmane.org="" gmane="" .science.biology.informatics.conductor=""> >> > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > -- *Moritz Heß PhD Candidate * *Research associate Forest Research Institute of Baden Württemberg (FVA) Wonnhalde 4 79100 Freiburg (Germany) phone +49 761 4018 301* [[alternative HTML version deleted]]