[BioC] limma fit interpretation doubt

[James W. MacDonald]

Hi David,

On Wednesday, February 26, 2014 8:09:21 AM, David Moriña Soler wrote:
> Dear Bioconductor users,
>
> I just began to use limma package for the analysis of microarray data.
> I want to include in the analysis two factors (treatment and time) and
> the interaction between them. My design matrix looks like
> > head(design)
> Treat1.0h Treat1.6h Treat2.0h Treat2.6h
> 1           0           0          1          0
> 2           0           0          0          1
> 3           1           0          0          0
> 4           0           1          0          0
> 5           0           0          1          0
> 6           0           0          0          1
>
> Then, if I run
> > fit <-
> lmFit(data.norm,design,block=pData(data)$Subject,correlation=corfit$consensus),
>
>
> I have this output for the coefficients:
> > head(fit$coefficients)
>                        Treat1.0h      Treat1.6h Treat2.0h  Treat2.6h
> KCNE4              5.020670    4.981786   5.038805   4.924326
> IRG1               6.119265    6.015868   6.095171   6.027943
> SNAR-G2            8.242385    8.186429   8.144942   8.230391
> MBNL3             10.438644   10.417312  10.417042  10.358303
> HOXC4              8.985834    8.854698   8.969801   8.939682
> ENST00000319813    3.913602    4.102653   4.000681   3.960431
>
> How can I obtain for example the effect of the treatment, using
> contrasts and eBayes?

It depends on what you mean by 'the effect of the treatment'. Are you 
looking for a main effect for treatment, after controlling for time 
(e.g., a conventional main effect)? Or are you looking for the 
difference in treatment at each time separately?

The easiest way to create a contrasts matrix is to use makeContrasts(), 
which is as simple as deciding what comparisons you want to make. So 
for instance, let's say you want to know the difference in expression 
between the treatments at each time:

contrast <- makeContrasts(Treat1diff = Treat1.6h-Treat1.0h, Treat2diff 
= Treat2.6h-Treat2.0h, levels = design)

If you want the interaction too, it's easy to do that as well

contrast <-  makeContrasts(Treat1diff = Treat1.6h-Treat1.0h, Treat2diff 
= Treat2.6h-Treat2.0h, Interaction = 
Treat1.6h-Treat1.0h-Treat2.6h+Treat2.0h, levels = design)

Best,

Jim


>
> Thank you very much,
>
> David
>
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--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099