# [BioC] Limma lmFit simple design or contrast

Beatriz ramos.beatriz at gmail.com
Thu Nov 17 12:27:58 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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