[BioC] Fwd: limma modeling, paired samples

[Riba Michela]

Hi,
I'm writing again dealing with a paired sample design:
the experimental setting involves 9 patients, 3 disease stages and microarray expression data
according to the included target file



target<- readTargets("targetPT.txt")
head(target)


Genotype <- factor(target$Genotype)
Disease<- factor(target$Disease, levels=c("stageA", "stageB", "stageC"))

 I have performed a paired samples analysis using
design <- model.matrix(~Genotype+Disease)

in order to sort out genes differentially expressed between stages A and B for example
but I noticed that the first patient and the first disease stage (in alphabetical order) disappears in the fit
using 
colnames (fit)

I tried to use
design <- model.matrix(~0+Genotype+Disease)
to explicit the coefficient in intercept
and the first Disease type disappears

I tried again
design <- model.matrix(~0+Disease+Genotype)
and again the first patient in alphabetical order disappears

I do not have sufficient mathematical  education to understand exactly what shoud fit the needs
I would prefer this last model formula to extract using a contrast matrix the differentially expressed genes between stages considering the variability due to different patients
because it explicits all the disease stages, 
anyhow I would ask what could be the best way to address this problem
and what could be the mistakes behind (i.e. I do not have all disease conditions for all the 9 patients,.. )

I thank you very much for attention,



Michela


> sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] it_IT.UTF-8/it_IT.UTF-8/it_IT.UTF-8/C/it_IT.UTF-8/it_IT.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] limma_3.18.13

loaded via a namespace (and not attached):
[1] tools_3.0.2