Differential Rank Conservation (DIRAC) is a novel approach for studying gene expression within pathways; this method belongs to a larger family of algorithms designed to identify relative expression (i.e., the ordering among expression values) signatures for diagnosis and prognosis4. Specifically, DIRAC provides quantitative measures of how network ordering differs within and between phenotypes.
In the pilot study, we examined disease phenotypes including cancer subtypes and neurological disorders, and we identified networks that are tightly regulated, as defined by high conservation of transcript ordering. Interestingly, we observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. In addition to the phenotype-level analyses, we performed sample-to-sample analyses and found that, at this level, DIRAC can detect a change in ranking (i.e., shuffling) between phenotypes for any selected network. Using the same data as above, we identified variably expressed networks that represent statistically robust differences between disease states, and which serve as signatures for accurate molecular classification.