COMPASS: A Web-Based COMPosite Activity Scoring System to Navigate Health and Disease Through Deterministic Digital Biomarkers

Abstract Quantifying pathway activity in a reproducible and interpretable manner remains a central challenge in systems biology and precision medicine. Here, we introduce COMPASS (COMPosite Activity Scoring System), a deterministic, ontology-free, threshold-based framework that converts gene expression into per-sample pathway activity scores without reliance on permutation or reference cohorts. Implemented as an intuitive web application, COMPASS derives gene-specific activation thresholds directly from data, standardizes deviations from these boundaries, and integrates directionally opposing genes into a single composite score using closed-form logic. Implemented as an accessible web application, COMPASS enables users to upload expression matrices, define gene signatures, and perform activity scoring, statistical comparisons, and survival analyses without coding. Across diverse biological and clinical datasets, COMPASS generates stable and transferable digital biomarkers that quantify cellular states, benchmark ‘humanness’ and ‘relevance’ of model systems and enable outcome stratification. In head-to-head comparisons with widely used single-sample enrichment methods (GSVA and ssGSEA), COMPASS shows consistent performance across multi-cohort datasets, with improved discrimination when integrating bidirectional gene programs. Stratified bootstrap analyses further demonstrate reduced variability and increased robustness. By directly linking expression thresholds, deviation, and gene directionality, COMPASS provides a transparent and generalizable framework for ontology-free pathway activity quantification and outcome modeling. Impact statement COMPASS redefines pathway analysis by replacing permutation-based enrichment with a closed-form, threshold-driven framework that yields robust, interpretable, and clinically actionable activity scores, enabling reproducible, sample-level pathway scoring without coding and bridging gene expression to clinically meaningful outcomes. Competing Interest Statement The authors have declared no competing interest. Footnotes New results on bootstrapping and survival analysis