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

Hi Riba,

The patient and disease don't disappear; they are absorbed into the intercept term. The model you are fitting is called a 'factor effects' model, and all the coefficients are interpreted as differences between a given sample type and the 'baseline', which in this case is the Stage A disease for Genotype 1.

In other words:

> colnames(design)
[1] "(Intercept)"   "DiseasestageB" "DiseasestageC" "Genotypept02" "Genotypept03" "Genotypept04"  "Genotypept06"
[8] "Genotypept09"  "Genotypept10"  "Genotypept13"  "Genotypept14"

DiseasestageB can be interpreted as Stage B - Stage A after controlling for the paired nature of your data. The DiseasestageC coefficient is interpreted analogously.

This is a basic concept of linear modeling, and if you are getting tripped up on the basics then I would highly recommend finding a local statistician who can help you.

Best,
Jim

--

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