An embedded and charset-unspecified text was scrubbed... Name: not available Url: https://stat.ethz.ch/pipermail/bioconductor/attachments/20051117/ 126fc437/attachment.pl

Hi, everybody I've read "limma: Linear Models for Microarray Data User's Guide" several times and I can't understood when you should use a simple design or a contrast matrix. I have done my own experiments with the explanation of page 31 ("Two Groups: Affymetrix") and I can do it and obtain results but I don't understand why. My experiment is FileName Array Target File1 1 Mu File2 1 WT File3 2 Mu File4 2 WT File5 3 Mu File6 4 WT I have 2 questions: 1) When I design the design matrix with the instructions of this user's guide, I obtain WT MUvsWT [1,] 1 1 [2,] 1 0 [3,] 1 1 [4,] 1 0 [5,] 1 1 [6,] 1 0 but I don't understand why you write 1s and 0s (I know column WT is logIntensity and MUvsWT logRatio, but I don't understand why you put this number) do you consider that your WT intensity is always 10' (log10=1)? and MUvsWT is 10 or 1 (log10=1, log1=0)? 2) When I use a simple design and plot the results with plotMA(fita), I can see a plot with M label in the Y-axe and A label in the X-axe but the graphical representation is very similar to Intensity vs Intensity plot (M are the log2ratio and A the average of control and experiment intensity values) ------------------------------------------------------------------ -------- > design <- cbind(WT=1, MUvsWT=c(1,0,1,0,1,0)) > design WT MUvsWT [1,] 1 1 [2,] 1 0 [3,] 1 1 [4,] 1 0 [5,] 1 1 [6,] 1 0 > fita <- lmFit(eSet,design) > fita <- eBayes(fita) ------------------------------------------------------------------ -------- When I use a contrast matrix and plot the results with plotMA(fit2), I can see a plot with M label in the Y-axe and A label in the X-axe and the graphical representation is a real MA plot ------------------------------------------------------------------ -------- > design2 <- cbind(MU=c(1,0,1,0,1,0),WT=c(0,1,0,1,0,1)) > design2 MU WT [1,] 1 0 [2,] 0 1 [3,] 1 0 [4,] 0 1 [5,] 1 0 [6,] 0 1 > fit <- lmFit(eSet,design2) > contraste <- makeContrasts(MUvsWT=MU-WT, levels=design2) > contraste MUvsWT MU 1 WT -1 > fit2 <- contrasts.fit(fit,contraste) > fit2 <- eBayes(fit2) ------------------------------------------------------------------ -------- why you obtain diferent plots? is because of the design matrix? Thanks a lot for your help Beatriz