Hi all,
I'm currently trying to use the package NanoStringDiff to analyse Nanostring nCounter gene expression data.
I have a dataset based on samples from 6 patients (C7, C8, C9, C10, C11, C12), each patient with a treated sample and with a control sample :
treated id
1 drug C7
2 CTRL C7
3 drug C8
4 CTRL C8
5 drug C9
6 CTRL C9
7 drug C10
8 CTRL C10
9 drug C11
10 CTRL C11
11 drug C12
12 CTRL C12
I would like to detect the effect of the treatment, while correcting for variations between patients.
Based on answers given on other GLM based packages, I've came up with the following design matrix and contrasts, but I'm questionning the validity of this approach.
design.full=model.matrix(~pheno$id + pheno$treated)
design.full
(Intercept) pheno$idC11 pheno$idC12 pheno$idC7 pheno$idC8
1 1 0 0 1 0
2 1 0 0 1 0
3 1 0 0 0 1
4 1 0 0 0 1
5 1 0 0 0 0
6 1 0 0 0 0
7 1 0 0 0 0
8 1 0 0 0 0
9 1 1 0 0 0
10 1 1 0 0 0
11 1 0 1 0 0
12 1 0 1 0 0
pheno$idC9 pheno$treatedCTRL
1 0 0
2 0 1
3 0 0
4 0 1
5 1 0
6 1 1
7 0 0
8 0 1
9 0 0
10 0 1
11 0 0
12 0 1
result=glm.LRT(NanoStringData,design.full,contrast=c(0,0,0,0,0,0,1))
This question have already been adressed here : https://support.bioconductor.org/p/98249/, but I think it stayed unanswered due to few people Using NanoStringDiff.
Thanks for any of your any input, even if you're not familiar with NanoStringDiff.
Due to my low background in statistics I was not really sure to do the right thing, and hence not really trusting my results. Thanks for your approval!