ArticleBioinformatics (Oxford, England)2026
OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
motivationSingle-cell technologies enable high-resolution cellular studies but face challenges in identifying differential features due to data complexity.
resultsWe 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/.
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