
Hi list,

I have a one-color setup with 238 samples and 6 conditions, for a geneset of just over 400 probes (targeted array). One of the conditions is control and the rest are experimental conditions. After normalization using vsn I would like to make a comparison first between the control and the rest and then between some of the conditions grouped. Being human samples I can expect great variability within conditions and thus not many genes coming as significant. But when I run a limma analysis, for some of the contrasts I get that all the genes come up significant with large B values and really small p-values. When doing boxplots of normalized expression values for some of these genes, the image doesn´t show they are that different, rather the opposite. Is my parametrization wrong? Are the results strangely significant because the reduced geneset to be analyzed? Being a targeted array probably many results should come up as significant, but I don't think all of them should show up as significant as they do for many of the contrasts.
here is my code:

> table(myeset$group)

 0  1  2  3  4  5 
68 60 18 53  6 33

>design <- model.matrix(~0+factor(myeset$group))
>colnames(design) <- c("G0","G1","G2","G3","G4","G5")

>fit <- lmFit(myeset,design)
>contrast.matrix <- makeContrasts((G1+G2+G3+G4+G5)-G0,(G1+G2+G3+G4)-G0, (G1+G2)-G0, G1-G2,(G1+G2+G3+G4)-G5, levels = design)
>fit2 <- contrasts.fit(fit, contrast.matrix)
>fit3 <- ebayes(fit2)
> topTable(fit3,adjust="BH",coef=1)
             ID    logFC  AveExpr        t       P.Value     adj.P.Val        B
411        VTI2 39.30167 13.08281 149.2822 5.690975e-249 2.356064e-246 501.3006
315        PI14 38.07841 12.54843 138.1938 1.399494e-240 2.896952e-238 489.1714
49        CHRM1 35.60063 11.93787 135.3310 2.623964e-238 3.621070e-236 485.7677
67        DDX32 34.95446 11.61260 132.9274 2.310411e-236 2.391276e-234 482.8165
144 GI_18044224 35.16818 11.83657 132.1551 9.904483e-236 8.200912e-234 481.8495
385        TDE1 34.80442 11.65140 131.8907 1.633346e-235 1.127009e-233 481.5163
136 GI_16878151 35.98769 12.01306 130.9807 9.205817e-235 5.444583e-233 480.3612
269        MGLL 37.82152 12.80129 129.2519 2.539646e-233 1.168237e-231 478.1303
120 GI_15559367 37.07777 12.19571 130.4492 1.285421e-233 6.652055e-232 478.0643
61       CSNK1E 35.45598 11.93302 128.1286 2.243467e-232 9.287953e-231 476.6548


Thanks for your help

D.

 		 	   		  
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