Currently watching http://videolectures.net/cancerbioinformatics2010_baggerly_irrh/. Some of the extraordinary quotes (approximative though):
If, after a computational analysis, you give a biologist a single gene, unrelated to cancer until now, that correlates the increase of risk of cancer, it is most likely that you would hear something like “No, you’ve got stroma contamination over here: I’ve been studying this gene for years now and I perfectly know that it is completely uncorrelated with cancer”
If, after a computational analysis, you give a biologist a list of hundreds of genes, and you say: here is the genetic signature of cancer, it is most likely that he will just agree with you, because “yeah, this one seems to correlate with that one, so yeah, that makes sense”.
=> This is precisely why I am developping the information flow framework for drug discovery and clinical biology; to make biological sense from the lists of hundreds of perturbed genes.
Forensic Bioinformatics: Here is the raw data, here is the final results. Let’s try to figure out how we get from the raw data to the results, disregarding what they said they did in supdata.
=> Idea: use the chemotherapeutic drug against 60 cell lines pannel to determine specificity and see if it correlates with the biological knowledge we have about those cell lines
Let’s use metagenes!!! As matematicians, we know them as PCA, but well, let’s call them metagenes.
Their list and ours: you might see the pattern. Yes, the genes are IDs are off-set by 1.
So, we had a look at the software they were using and it’s documentation. if you want to read the docs, go to my website, because it was me who wrote it, since there were none!
Most of review commitees in biological journals are biologists, they will skip all the part related to the microarray analysis, jump to the results and see if the computational biology results are in agreement with wet lab results.