Classification of Inflammation of Unknown Origin patients based on RNA-seq and SomaScan data

A preliminary analysis was conducted on data acquired from RNA sequencing and SomaScan platforms, for the classification of patients with Inflammation of Unknown Origin. To this end, a multimodal data integration approach was designed, by combining the two platforms, in order to assess the potentiality of learning estimators, using the differentially expressed features from the independent profiling experiments of both platforms. The classification framing was the differentiation of Inflammation of Unknown Origin patients against a multitude of Systemic Autoinflammatory disease patients. Separate false discovery rate analyses were performed on each dataset to extract statistically significant features between the two designated sample groups. Genomic analysis managed higher overall classification metrics compared to proteomic analysis, averaging an ~19% increase overall metrics and classifiers, with a ~0.07% increase in standard error. The multimodal data integration approach achieved similar results to the individual platforms' analyses. More specifically, it managed the same classification accuracy, sensitivity, and specificity scores as the best individual analysis, with the simple Logistic Regression estimator.Clinical Relevance- This study highlights the advantage of exploiting RNA sequencing data to identify potential Inflammation of Unknown Origin disease specific biomarkers, even against other Systemic Autoinflammatory diseases. These findings are further emphasized given the non-apparent clinical discrepancy between Inflammation of Unknown Origin and other Systemic Autoinflammatory diseases.