I have genomic data that I want to play with. I have 3 CSV files, each with ~300 samples and ~19000 genes, which contain information on expression levels, relative expression levels (tumor / normal), and Boolean values indication mutations. I also have an additional CSV file that has ground truth information (when did the patient's cancer progress, how long were they observed).
It makes sense for me to use one of the data CSV files along with the ground truth file to create a supervised learning algorithm. But how would I incorporate the other CSV files? To me, I know that the data for gene A in patient B in datafile C is different than gene A in patient B in datafile D, but they're both gene A, and I am not sure how to distinguish the use of gene A in different contexts in the code I write.
My initial thought is to append something like _1 or _2 to the genes in the other files, combine all the features to have a 300x57000 matrix, and do stuff on that, but that seems like the wrong way to go about it. Is there a name for what I am asking for? Is there a better way to do this? I am having trouble wrapping my head around how this would work
Edit: Another idea I just had was to find a way to combine the data from each file so that each feature has a tuple of values associated with it, and maybe I can throw that into my random forest?
Edit: tl;dr: Feature X has n values associated with it, which may or may not be related. How does this factor into how I use them in my learning algorithm?