Dear all, i tried to extract genes that are linearly correlated to a dose response in a dietary experiment using the following script. dm <- model.matrix(~ DHA, data=targets) fit <- lmFit(MA.list,dm) fit2 <- eBayes(fit) table <- topTable(fit2,coef=2, adjust.method="none", number = 10000, p.value = 0.05) the first time i did it i had in the output two columns with the intercept and the slope but i did it again and now I do not get them anymore. I can't figure out why as i dont think i have changed anything, but obviously i must have. Just to confirm however, is the slope equivalent to the fit2$coefficients values? Thanks, Christian -- The University of Stirling has been ranked in the top 12 of UK universities for graduate employment*. 94% of our 2012 graduates were in work and/or further study within six months of graduation. *The Telegraph The University of Stirling is a charity registered in Scotland, number SC 011159.

Hi Christian, If you used a previous version of limma the first time, then this might be the case. Older version of limma: > library(limma) > dha <- rnorm(10) > dat <- matrix(rnorm(10000), ncol=10) > design <- model.matrix(~dha) > fit <- lmFit(dat, design) > > fit2 <- eBayes(fit) > topTable(fit2) X.Intercept. dha F P.Value adj.P.Val 399 -1.16703330 0.9726376 6.951548 0.0009571525 0.5130698 556 -1.24250007 0.2182220 6.881951 0.0010261396 0.5130698 305 0.02123802 -1.1632224 5.965466 0.0025658479 0.7115675 496 -0.97817815 -0.2335099 5.776557 0.0030993670 0.7115675 835 -1.00327277 0.9266638 5.530451 0.0039641990 0.7115675 Current version of limma: > library(limma) > dha <- rnorm(10) > dat <- matrix(rnorm(10000), ncol=10) > design <- model.matrix(~dha) > fit <- lmFit(dat, design) > fit2 <- eBayes(fit) > topTable(fit2) Removing intercept from test coefficients logFC AveExpr t P.Value adj.P.Val B 281 1.0398797 -0.49364355 3.724635 0.0001969434 0.1969434 -1.930158 78 0.9062016 -0.05566960 3.245828 0.0011759163 0.5879581 -2.637635 149 0.8217517 -0.06901555 2.943345 0.0032561888 0.9545421 -3.034482 52 0.7695025 0.21765738 2.756199 0.0058609793 0.9545421 -3.260587 394 0.7458264 -0.01242832 2.671397 0.0075689249 0.9545421 -3.358155 Since the intercept is (when analyzing microarrays) an uninteresting coefficient, it is now automatically removed, as the message above notes. And the F-statistic that used to be computed when you didn't specify a contrast (in the older versions of limma) is not testing something useful. Best, Jim On 7/10/2014 10:20 AM, Christian De Santis wrote: > Dear all, > > i tried to extract genes that are linearly correlated to a dose > response in a dietary experiment using the following script. > > dm <- model.matrix(~ DHA, data=targets) fit <- lmFit(MA.list,dm) fit2 > <- eBayes(fit) table <- topTable(fit2,coef=2, adjust.method="none", > number = 10000, p.value = 0.05) > > the first time i did it i had in the output two columns with the > intercept and the slope but i did it again and now I do not get them > anymore. I can't figure out why as i dont think i have changed > anything, but obviously i must have. Just to confirm however, is the > slope equivalent to the fit2$coefficients values? > > Thanks, Christian > -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099