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

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 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-frie

SUBMITTER: Su H 

PROVIDER: S-EPMC12766913 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature