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OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics

Huidong Su¹, Caicai Zhang¹, Frank Qingyun Wang¹

¹Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, 999077, China.

Bioinformatics (Oxford, England)

|December 3, 2025

Summary

OTMODE, a new method for single-cell multi-omics data, enhances differential feature identification. It offers superior performance and efficiency, aiding in the discovery of biological insights and improving cell annotation accuracy.

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Published on: October 25, 2018

Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Single-cell technologies offer high-resolution cellular insights but struggle with complex data for differential feature identification.
Identifying distinct cellular features is crucial for understanding biological processes and disease mechanisms.

Purpose of the Study:

To introduce OTMODE, a novel computational method for improved differential feature identification in single-cell multi-omics data.
To enhance the accuracy and efficiency of analyzing complex single-cell datasets.

Main Methods:

Developed OTMODE, a non-parametric method utilizing the unbalanced Sinkhorn algorithm and Wald test.
Implemented OTMODE with seamless integration into the Scanpy analysis platform.

Main Results:

OTMODE demonstrated superior performance in simulations, achieving high F1 (90%) and AUC (92%) scores with remarkable efficiency (2.2s for 5000 cells).
The method showed increased sensitivity in detecting biological processes compared to existing state-of-the-art approaches.
OTMODE effectively evaluates annotation accuracy and identifies potentially misannotated cell clusters.

Conclusions:

OTMODE provides a powerful and efficient tool for differential feature identification in single-cell multi-omics research.
The method aids in uncovering biological insights and refining cell type annotations, advancing single-cell data analysis.