I've searched and searched but still not confident with my LIMMA analysis here. Below is an example target file table to demonstrate the sort of samples I have (my n number is thankfully bigger than the table suggests). In the example, I have 4 patients all 4 have a disease. After completion of the study, it is seen that two of the patients responded well to the treatment (Positive Response = 1) and two did not (Positive Response = 0). Their initial pre-treatment sample is where Intervention = 0, and conversely Intervention = 1 is a post-treatment sample. I've not shown it in the table but I also have a good number of Healthy volunteers which could be used for comparisons. However, I'm of the understanding that pairing the samples can possibly add more power to my analysis, but any ideas here would be welcome.<caption>Example of Targets file</caption>
I want to make the most of the data available here and that I haven't made any incorrect assumptions or misinterpreted things. I need some help designing the contrasts. I don't know if these questions need a paired design or if I just compare the appropriate subset of arrays.
I want to investigate the differences between responders and non-responders.
- I'd like to know if there is any difference in expression pre-treatment. i.e. could there be a way to molecularly stratify these patients before assigning treatment?
- What are the differences post-treatment. i.e. differences here could be used to understand the biology underpinning the treatment response/non-response?
Below is the basic LIMMA code for this, the
makeContrasts() is blank as that's the bit I'm not sure on.
design <- model.matrix(~ 0 + Intervention + Treatment.Response + Patient, data = targets)
fit <- lmFit(eset, design= design)
contrast.matrix <- makeContrasts("", levels = design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)