Hi all, 

I just wanted to ask a general question about the best way to go about carrying out differential expression analysis between multiple groups. I have a microarray data set that has 10 different groups (i.e, 10 organs in one age group). Essentially what I want to do is find a common core set of differential expressed genes across every single group. I have created contrasts by subtracting each group away from each other etc, and then individually assessed DE genes from each group to then make venn diagrams and find common genes, however, this is super basic. I was wondering if there is a way of incorporating this in one function in r. Additionally, I want to do some machine learning in order for this core set of genes to be more rigorously assessed by the software, for example, 2 of the groups don't make cancer, the other 8 do and of the 8, they make cancer differently, some have high risk, some lower. I want to separate away core genes from both groups and then allow the machine to make decisions about the genes from the core set that might best fit, especially considering that solid cutoffs like log2fold change of 2 for example, may not be appropriate since organs that don't make as much cancer may have a significant change in a gene that is in the core set but isn't above, say, 2 fold and may be far less noticeable than if I make pre-determined cutoffs. 

Are there any good packages and tutorials for this kind of analysis as I cannot find anything with what I have searched so far! Any help would be much appreciated! Many thanks!