Re: Any problem with the design matrix or the contrasts? Thanks

"Ryan C. Thompson" <[email protected]>
Newsgroups gmane.science.biology.informatics.conductor
Message-ID <[email protected]>
Hi,

You're going to need to address your two scientific questions using 
separate fits. This is because your first question (pos vs neg) is 
testing a main effect, and you are effectively fitting an interaction 
model (one coefficient for each combination of AR & gender). So your 
interaction model is correct for testing the second question, which is 
testing one factor within only a single level of the other. But for 
testing the main effect, you'll need to fit an additive model using the 
formula suggested by Federico. Furthermore, the test for a main effect 
may not be valid in the presence of an interaction effect, so you should 
test for that too using the interaction model, and then look closely at 
the data before trusting probe sets that are significant for both the 
main effect and interaction effect. The interaction contrast using your 
design would be ((F.pos-M.pos)-(F.neg-M.neg)).

-Ryan

On 08/01/2014 08:07 AM, Rao,Xiayu wrote:
> Hello,
>
> I learned from posts in the forum and analyzed my data using the suggested design and contrasts. It turns out there are no sig genes or few in the end. Could you please help check if the design does not fit the data or the contrasts made are incorrect??
>
> #the research questions are 1) to compare between pos and neg for AR,  and 2) Male.pos vs. Female.pos.
>
> Treat <- factor(paste(targets$gender,targets$AR,sep="."))
> chip <- factor(targets$chip)
> design <- model.matrix(~0+Treat+chip)
> colnames(design)[1:4] <- levels(Treat)
> fit <- lmFit(y,design)
>
> cm <- makeContrasts(posVSneg=(F.pos+M.pos-F.neg-M.neg)/2, MposVSFpos=M.pos-F.pos, levels=design)
> fit2 <- contrasts.fit(fit,cm)
> fit2 <- eBayes(fit2)
> topTable(fit2, coef="posVSneg", sort.by="p")           #0 sig genes
> topTable(fit2, n=20,coef="MposVSFpos", sort.by="p")             #4 sig genes
>
> targets.txt
> sample gender AR          chip
> s1            F              pos         1
> s2            F              neg        1
> s3            M            neg        2
> s4            M            pos         2
> s5            F              neg        2
> s6            M            pos         2
> s7            M            pos         2
> s8            M            pos         3
> s9            M            pos         3
> s10         M            pos         3
> s11         M            pos         3
> s12         F              pos         4
> s13         F              pos         4
> s14         M            pos         4
> s15         M            pos         4
> s16         F              pos         5
> s17         M            pos         5
> s18         M            pos         5
> s19         M            neg        6
> s20         M            neg        6
> s21         F              neg        6
> s22         F              pos         6
> s23         F              pos         6
> s24         F              neg        6
> s25         F              pos         6
> s26         F              pos         6
> s27         F              pos         6
> s28         M            neg        6
>
>
> Thanks,
> Xiayu
>
> 	[[alternative HTML version deleted]]
>
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