[BioC] lmFit simple design or contrast

[Beatriz]

Hi, everybody

I've read "limma: Linear Models for Microarray Data User's Guide" 
several times and I can't understood when you should use a simple design 
or a contrast matrix.

I have done my own experiments with the explanation of page 31 ("Two 
Groups: Affymetrix") and I can do it and obtain results but I don't 
understand why.

My experiment is

FileName

	

Array

	

Target

File1

	

1

	

Mu

File2

	

1

	

WT

File3

	

2

	

Mu

File4

	

2

	

WT

File5

	

3

	

Mu

File6

	

4

	

WT


I have 2 questions:

1) When I design the design matrix with the instructions of this user's 
guide, I obtain
     WT MUvsWT
[1,]  1   1
[2,]  1   0
[3,]  1   1
[4,]  1   0
[5,]  1   1
[6,]  1   0 

but I don't understand why you write 1s and 0s (I know column WT is 
logIntensity and MUvsWT logRatio, but  I don't understand why you put 
this number)
do you consider that your WT intensity is always 10' (log10=1)?
and MUvsWT is 10 or 1 (log10=1, log1=0)?


2) When I use a simple design and plot the results with plotMA(fita), I 
can see a plot with M label in the Y-axe and A label in the X-axe but 
the graphical representation is very similar to Intensity vs Intensity 
plot (M are the log2ratio and A the average of control and experiment 
intensity values)

    --------------------------------------------------------------------------

>  design <- cbind(WT=1, MUvsWT=c(1,0,1,0,1,0))
>  design

             WT MUvsWT
        [1,]  1   1
        [2,]  1   0
        [3,]  1   1
        [4,]  1   0
        [5,]  1   1
        [6,]  1   0

>  fita <- lmFit(eSet,design)
>  fita <- eBayes(fita)
     
    --------------------------------------------------------------------------


When I use a contrast matrix and plot the results with plotMA(fit2), I 
can see a plot with M label in the Y-axe and A label in the X-axe and 
the graphical representation is a real MA plot

    --------------------------------------------------------------------------

>  design2 <- cbind(MU=c(1,0,1,0,1,0),WT=c(0,1,0,1,0,1))
>  design2

             MU WT
        [1,]  1  0
        [2,]  0  1
        [3,]  1  0
        [4,]  0  1
        [5,]  1  0
        [6,]  0  1

>  fit <- lmFit(eSet,design2)
>  contraste <- makeContrasts(MUvsWT=MU-WT, levels=design2)
>  contraste

           MUvsWT
        MU      1
        WT     -1

>  fit2 <- contrasts.fit(fit,contraste)
>  fit2 <- eBayes(fit2)
    --------------------------------------------------------------------------


why you obtain diferent plots? is because of the design matrix?

Thanks a lot for your help

Beatriz