13 Sep 19:53 2013

## Re: Microarray analyses. How to know the direction of the expression?

Hi, On Thu, Sep 12, 2013 at 11:57 AM, James W. MacDonald <jmacdon@...> wrote: > Hi Mary, > > This is another reason to use a 'cell means' parameterization rather than a > 'factor effects' parameterization. This is just statistician blahblah for > computing a mean for each group rather than a baseline level and a > difference between the other sample and the baseline. > > In your situation, since the second coefficient has WT in the name (and you > have an intercept term), the intercept is computing the mean of a baseline > sample (in your case the KO sample), and the GroupWT coefficient is WT - KO. > > You could instead do > > design <- model.matrix(~0+Group) > contrast <- matrix(c(1,-1), ncol = 1) > colnames(contrast) <- "KO vs WT" Perhaps using a even a little less "statistician blahblah", the OP might find using the limma::makeContrasts function more intuitive, eg: R> contrast <- makeContrasts(KO-WT, levels=levels(Group)) And continue as Jim prescribed below: > fit <- lmFit(myEset, design) > fit2 <- contrasts.fit(fit, contrast) > fit2 <- eBayes(fit2) > topTable(fit2, 1) Sincerely, A non-statistician who deals with his fair share of statistical blah blah -- -- Steve Lianoglou Computational Biologist Bioinformatics and Computational Biology Genentech _______________________________________________ Bioconductor mailing list Bioconductor@... https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor