[BioC] limma fit interpretation doubt

[David Moriña Soler]

Hi Jim,

thank you very much for your help! If I'm right, the solution you propose

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

gives the difference in expression between times inside each treatment 
group, while I'm more interested in the 'conventional' treatment main 
effect,  as you suggest, a main effect for treatment, after controlling 
for time. Is there a way to achieve that using limma?

Thanks!

David


On 26/02/14 16:55, James W. MacDonald wrote:
> 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