Sample-level differential expression for TCGA samples
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What do you acutally want to compare?
I have performed already a Tumor Vs. Normal DE with EdgeR, but I want to visualize the potential expression differences in sample level in a network I have built.
Edit: I am aware that cBioPortal does not have tumor vs. normal analysis when it comes to expression.
Still not clear what you want to do. When you say 'sample level', do you mean that you want your network vertices to be samples and not genes? In that case, just transpose your input data with
t()(in R).Sorry I should have made it more clear :) I don't want different expression between groups (tumor Vs normal) in general, but differential expression of genes in every patient (tumor Vs normal for patient A, patient B and so on). The reason is that after doing the differential expression analysis in patient groups from specific subtypes of TCGA-COAD, I found that a lot of critical genes, known to be differentially expressed in colon cancer, were having a logFC ~O.4 or so. So I wanted to check whether the results are "diluted" with those genes being highly differentially expressed in some of the samples while not in others.
Hope that I made it clear enough now.
You could do a paired analysis in order to adjust for the within-patient effects - in this way, you would obtain a p-value and fold-change for each gene. Otherwise, if you're just comparing 1 tumour versus 1 normal, the best that you can do is derive a ratio of expression for each T-N pair, and/or convert your entire dataset to Z-scores and compare these values in each T-N pair separately.
Helpful as always! Thanks for your suggestions and help, Kevin.