[BioC] limma modeling, paired samples and continuous variable
michela riba [guest]
guest at bioconductor.org
Tue Apr 15 11:05:30 CEST 2014
Hi,
I'm sorry for re-posting the message, but I cannot find it in the archive
Thanks a lot for attention
Hi,
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)
or
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
Michela
-- output of 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
--
Sent via the guest posting facility at bioconductor.org.
More information about the Bioconductor
mailing list