[BioC] lmFit simple design or contrast
Beatriz
ramos.beatriz at gmail.com
Thu Nov 17 11:56:51 CET 2005
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
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