[BioC] Any problem with the design matrix or the contrasts? Thanks

[Rao,Xiayu]

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 at gmail.com] 
Sent: Friday, August 08, 2014 3:22 PM
To: Rao,Xiayu
Cc: bioconductor at 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 at 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
>
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