ArticleBriefings in bioinformatics2025
Benchmarking single-sample gene set scoring methods for application in precision medicine.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- PathMED: an R toolkit for single-sample molecular scoring and machine learning with omics data.Bioinformatics (Oxford, England) · 2026Article
- SOPA and SIMPA: normalized single-sample integrated multiomics pathway analysis of tumor heterogeneity in solid cancers.Briefings in bioinformatics · 2026Article
- Multi-task spatial distillation reveals cell-type-resolved programmed cell death landscapes in the human kidney.Briefings in bioinformatics · 2026Article
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Authors and funding
8 authors.
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Abstract
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.
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