[BioC] limma: Help with time course experiment, common ref design

[n.taris at voila.fr]

Dear all,

I would like to analyze a time course experiment (5 time points, 2 technical replicates per time point) based on a common reference (=pool of all the conditions) design dataset with the limma package. By reading the contents of the limma user's guide, I found limma very interesting to first pre-process the data and then  fit linear models. However, since I am not familiar with R, I face some issues with the linear models.

Here is the script I'm have been using so far in order to normalize the data within and between arrays.


library(limma)
targets=readTargets("targets.txt")
RG=read.maimages(files=c("1 R1.gpr","1 R2.gpr","4 R1.gpr","4 R2.gpr","11 R1.gpr","11 R2.gpr","15 R1.gpr","15 R2.gpr","18 R1.gpr","18 R2.gpr"), source="genepix")
f=function(x) as.numeric(x$Flags > -50)
RG=read.maimages(files=c("1 R1.gpr","1 R2.gpr","4 R1.gpr","4 R2.gpr","11 R1.gpr","11 R2.gpr","15 R1.gpr","15 R2.gpr","18 R1.gpr","18 R2.gpr"), source="genepix",wt.fun=f)

MA=normalizeWithinArrays(RG, method="loess", bc.method="normexp", offset=50)
MA.q=normalizeBetweenArrays(MA, method="quantile")

Now with the linear models: 

I am a bit confused about the way to write the script. From the section 8.8 of the user's guide, I tried to see how to apply the example to mine but I can't get it anything worthy. 


targets<-readTargets("runxtargets.txt")
targets

   SlideNumber Cy3  Cy5
1          1R1 ref  1R1
2          1R2 ref  1R2
3          4R1 ref  4R1
4          4R2 ref  4R2
5         11R1 ref 11R1
6         11R2 ref 11R2
7         15R1 ref 15R1
8         15R2 ref 15R2
9         18R1 ref 18R1
10        18R2 ref 18R2


design <- modelMatrix(targets,ref="ref")

Found unique target names:
 11R1 11R2 15R1 15R2 18R1 18R2 1R1 1R2 4R1 4R2 ref 


>From there, I don't know where I should go. My interest is to test a replicate effect as well as the time effect. 
Any help or suggestion would be greatly appreciated. In advance, sorry if the question sounds simplistic.

Thanks.

Nicolas

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Nicolas Taris
Team 'Diversity and Connectivity in Coastal Marine Landscapes (DIV&CO)'
UMR CNRS 7144
Station Biologique de Roscoff
Place Georges Teissier
France 
n.taris at voila.fr
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