Abstract Summary The rapid expansion of multi-omics data has enabled the development of molecular signatures–coordinated patterns of molecular features that serve as powerful biomarkers for diagnosis, prognosis, and therapeutic decision-making. Despite their potential, many published signatures suffer from limited reproducibility and narrow applicability, partly due to challenges in summarising complex, multi-feature profiles into a single, statistically sound and biologically meaningful score. Here, we introduce sigscores, an R package that streamlines the computation of summary scores for molecular signatures. Building on the quality control principles of our earlier tool, sigQC, sigscores supports an extensive array of scoring metrics–including measures of central tendency, dispersion, and aggregation. It incorporates a resampling framework to generate empirical null distributions for rigorous significance assessment and provides integrated visualisation tools for diagnostic evaluation. Optimised for parallel execution on multi-core systems, sigscores is well-suited for both exploratory research and high-throughput large-scale applications. Availability and Implementation Source code freely available for download on GitHub at https://github.com/alebarberis/sigscores, implemented in R and supported on MacOS and MS Windows. Supplementary Information Additional information available at https://alebarberis.github.io/sigscores/.
Barberis et al. (Wed,) studied this question.