Bioinformatics. 2025 Dec 3:btaf650. doi: 10.1093/bioinformatics/btaf650. Online ahead of print.
ABSTRACT
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 seconds 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: OTMODE is freely available at https://github.com/Eggong/OTMODE and also avaliable at https://pypi.org/project/OTMODE/.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
PMID:41335419 | DOI:10.1093/bioinformatics/btaf650
