HopRatio: Profiling single-sample transcriptomic dysregulation using stable gene-ordering relationships
Summary Single-sample transcriptomic analysis can provide gene-level views of how individual tumors deviate from matched reference states, complementing cohort-level differential expression. Here, we present HopRatio, a rank-based framework that quantifies, for each gene in each sample, the fraction of stable reference gene-ordering relationships inverted relative to a context-matched reference cohort. By relying on within-sample ranks rather than cross-sample expression magnitudes, HopRatio enables sample-specific scoring without cross-sample normalization for score calculation. Across 16 cancer types from The Cancer Genome Atlas with tissue-matched Genotype-Tissue Expression references, HopRatio generated individualized dysregulation profiles that supported tumor-normal discrimination using single genes and compact multi-gene panels. Recurrent high-performing features defined a 246-gene set enriched for developmental, membrane-associated, and ion-transport programs and associated with poor survival across cancers. Benchmarking in the Sequencing Quality Control dataset supported its robustness relative to commonly used differential expression methods, highlighting a scalable route for interpretable individualized transcriptomic analysis.