MOTIVATION: Single-cell technologies enable high-resolution cellular studies but face challenges in identifying differential features due to data complexity. RESULTS: We present OTMODE, a non-parametric method using unbalanced Sinkhorn algorithm and Wald test to improve differential feature identification in single-cell multi-omics data. Under simulation, OTMODE achieved superior performance (average 90% F1 score; average 92% AUC score) with high efficiency (2.2 s for 5000 cells). In practice, it shows greater sensitivity than other state-of-the-art methods in detecting meaningful processes and can evaluate annotation accuracy by identifying potentially misannotated clusters from auto-annotation tools. Furthermore, OTMODE integrates seamlessly with Scanpy, offering a user-friendly solution for researchers. AVAILABILITY AND IMPLEMENTATION: OTMODE is freely available at https://github.com/Eggong/OTMODE and also available at https://pypi.org/project/OTMODE/.

Abstract Background Genome-wide association studies (GWAS) have revealed numerous loci for complex diseases, yet the cellular contexts through which these variants act remain poorly understood. Single-cell RNA sequencing (scRNA-seq) offers high-resolution profiling of cellular heterogeneity, but linking GWAS signals to disease-relevant cells remains a challenge. Methods We developed a statistical framework that integrates GWAS-prioritized genes with single-cell transcriptomes to detect disease-relevant cells. Each target cell is contrasted with pools of healthy reference cells, ensuring that differences reflect disease rather than technical variability. Two controls are implemented: reference cells matched by sequencing depth and expression-matched control genes without known disease associations. Iterative Monte Carlo sampling generates comparisons across random draws of reference cells and control genes, reducing stochastic noise. A difference-of-differences statistic captures disease-specific shifts, with Fisher’s method aggregating significance and enrichment tests identifying cell types overrepresented in patients. Results In simulations and real-world datasets, the framework showed strong performance. In large peripheral blood cohorts, it consistently identified trait-associated populations, with monocytes as a benchmark. Analyses of disease datasets revealed both established and novel pathogenic populations, including age-associated B cells and cytotoxic CD8+ T cells in systemic lupus erythematosus, antigen-presenting acinar cells in Sjögren’s syndrome, and dysregulated proximal tubules in chronic kidney disease. Conclusions These findings demonstrate the utility of integrating GWAS with single-cell transcriptomics to pinpoint disease-relevant cells in complex traits.