[BioC] limma modeling, paired samples and continuous variable

michela riba [guest] guest at bioconductor.org
Tue Apr 15 11:05:30 CEST 2014

I'm sorry for re-posting the message, but I cannot find it in the archive
Thanks a lot for attention

I would like to model and retrieve differential expression data
regarding an experimental design in which different patients (9) have different disease classes (3 disease classes) and a feature represented with a percentage (0, 0.50, 0.75,1).
    some conditions are replicated 2 or 3 times, regarding the disease condition
Till now I have done an analysis considering Genotype and Disease in the model (as a paired samples analysis)

design <- model.matrix(~Genotype+Disease)
design <- model.matrix(~0+Genotype+Disease)

now I would like to model also considering
a continuous variable , namely r

this way: design <- model.matrix(~Genotype+Disease+r)

to see if differential gene expression between two classes of disease are correlated with the r status

but till now it is not possible to gain results
Coefficients not estimable: r0,5 r0,75 r1 
Warning message:
Partial NA coefficients for 15246 probe(s) 

if I model
design <- model.matrix(~Disease+r)
it goes well, but  it would not consider the different genotypes

I thank you very much for attention

Thanks a lot 


 -- output of sessionInfo(): 

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

[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

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