The difference is that chip is treated as a numeric variable in one case
and as a factor variable in the other. I believe the second case is more
aproppiate.
On Aug 8, 2014 4:03 PM, "Rao,Xiayu" <XRao@mdanderson.org> wrote:

> Thank you for your kind suggestion, Federico!
>
> I tried your suggested design, and found the result is different. There
> are 10 sig DEGs in the end.
> > design <- model.matrix(~0+AR+gender+chip,data=targets)
> > design
>    ARneg ARpos genderM chip
> 1      0     1       0    1
> 2      1     0       0    1
> 3      1     0       1    2
> 4      0     1       1    2
> 5      1     0       0    2
> 6      0     1       1    2
> 7      0     1       1    2
> 8      0     1       1    3
> 9      0     1       1    3
> 10     0     1       1    3
> 11     0     1       1    3
> 12     0     1       0    4
> 13     0     1       0    4
> 14     0     1       1    4
> 15     0     1       1    4
> 16     0     1       0    5
> 17     0     1       1    5
> 18     0     1       1    5
>
> I also tried this design, which I originally thought would be the same as
> yours, but is different. There are 3 sig DEGs in the end. Could you please
> explain the cause of the difference ??
> > gender <- factor(targets$gender)
> > AR <- factor(targets$AR)
> > chip <- factor(targets$chip)
> > design <- model.matrix(~0+AR+gender+chip)
> > design
>    ARneg ARpos genderM chip2 chip3 chip4 chip5 chip6
> 1      0     1       0     0     0     0     0     0
> 2      1     0       0     0     0     0     0     0
> 3      1     0       1     1     0     0     0     0
> 4      0     1       1     1     0     0     0     0
> 5      1     0       0     1     0     0     0     0
> 6      0     1       1     1     0     0     0     0
> 7      0     1       1     1     0     0     0     0
> 8      0     1       1     0     1     0     0     0
> 9      0     1       1     0     1     0     0     0
> 10     0     1       1     0     1     0     0     0
> 11     0     1       1     0     1     0     0     0
> 12     0     1       0     0     0     1     0     0
> 13     0     1       0     0     0     1     0     0
> 14     0     1       1     0     0     1     0     0
> 15     0     1       1     0     0     1     0     0
> 16     0     1       0     0     0     0     1     0
> 17     0     1       1     0     0     0     1     0
> 18     0     1       1     0     0     0     1     0
> 19     1     0       1     0     0     0     0     1
> 20     1     0       1     0     0     0     0     1
>
>  Actually, what I did after my post was to treat the chip as a random
> effect instead of a fixed batch effect (although I though the code in my
> last post was correct), because I thought the within batch data is not well
> balanced and applying a mixed model may be safer. There are 11 sig DEGs
> generated from the mixed model, including the above 10 and 3 sig genes.
> Please add any thoughts you have so that I can learn more. Thank you very
> much !
>
> Thanks,
> Xiayu
>
>
>
>
> -----Original Message-----
> From: Federico Lasa [mailto:felasa@gmail.com]
> Sent: Friday, August 08, 2014 3:22 PM
> To: Rao,Xiayu
> Cc: bioconductor@stat.math.ethz.ch
> Subject: Re: [BioC] Any problem with the design matrix or the contrasts?
> Thanks
>
> Both a question and a quick suggestion..
>
> For the first case, should/does
>
> design <- model.matrix(~0+AR +gender +chip, data=targets)
> cm<-makeContrasts(ARpos-ARneg, levels=design)
>
> produce the same results you have?
>
> On Fri, Aug 1, 2014 at 10:07 AM, Rao,Xiayu <XRao@mdanderson.org> 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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