Rank-based transcriptional signatures

We have developed a method for the definition and the analysis of gene expression signatures for diagnostic purposes. Our approach relies on construction of a reference map of transcriptional signatures, from both healthy controls and affected patients, using the respective mRNA or miRNA profiles. Subsequently, disease diagnosis can be performed by determining the relative map position of an individual’s transcriptional signature. Our approach addresses simultaneously the scarce repeatability issue and the high sensitivity of expression profiling methods to protocol variations, thereby providing a novel approach to RNA signature definition and analysis. Specifically, our method requires only that the relative position of RNA species be accurate in a ranking by value, not their absolute values. Furthermore, our method makes no assumptions on which RNA species must be included in the signature and, by considering a large subset (or even the whole set) of known RNAs, our approach can tolerate a moderate number of erroneous inversions in the ranking. The diagnostic power of our method has been convincingly demonstrated in an open scientific competition (sbv IMPROVER Diagnostic Signature Challenge), scoring second place overall, and first place in one sub-challenge. In addition, we report the application of our method to published miRNA expression profile data sets, quantifying its performance in terms of predictive capability and robustness to batch effects, compared with current state-of-the-art methods.