Benchmarking single-sample gene set scoring methods for application in precision medicine

Gene set-based single-sample scoring methods are promising to elucidate patient level disease heterogeneity and enable functional interpretation of molecular data for precision medicine approaches. Despite the availability of numerous algorithms, their performance under different scenarios and for downstream applications for precision medicine approaches has not been systematically evaluated. In this study, we conducted a comprehensive survey of an exhaustive list of single-sample scoring methods to assess their stability and reproducibility performances under commo scenarios which include limitations of input data or data integration across studies. We also evaluated their performances for downstream patient stratification and clinical association analyses, as well as predictive modeling of disease states. The in-depth characterization of these scoring methods highlights the importance for a rational design of analysis strategies and provides fundamental insights into method selection under different scenarios or for different applications.