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bioRxiv · 10.1101/2024.10.30.621072

Graph-Regularized Optimal Transport for Single-Cell Data Integration

Wang, Z.·Zhan, Q.·Shen, L.
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Abstract

BackgroundRecent advancements in single-cell omics technologies have enabled detailed characterization of cellular processes. However, coassay sequencing technologies remain limited, resulting in un-paired single-cell omics datasets with differing feature dimensions; Findingwe present GROTIA (Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis), a computational method to align multi-omics datasets without requiring any prior correspondence information. GROTIA achieves global alignment through optimal transport while preserving local relationships via graph regularization. Additionally, our approach provides interpretability by deriving domain-specific feature importance from partial derivatives, highlighting key biological markers. Moreover, the transport plan between modalities can be leveraged for post-integration clustering, enabling a data-driven approach to discover novel cell subpopulations; ConclusionsWe demonstrate GROTIAs superior performance on four simulated and four real-world datasets, surpassing state-of-the-art unsupervised alignment methods and confirming the biological significance of the top features identified in each domain. The software is available at https://github.com/PennShenLab/GROTIA.

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Wang, Z., Zhan, Q., Shen, L.. 2024-11-02. Graph-Regularized Optimal Transport for Single-Cell Data Integration. https://doi.org/10.1101/2024.10.30.621072

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