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bioRxiv · 10.1101/2024.02.26.582051

scConfluence : single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features

Samaran, J.·Peyre, G.·Cantini, L.
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Abstract

The abundance of unpaired multimodal single-cell data has motivated a growing body of research into the development of diagonal integration methods. However, the state-of-the-art suffers from the loss of biological information due to feature conversion and struggles with modality-specific populations. To overcome these crucial limitations, we here introduced scConfluence, a novel method for single-cell diagonal integration. scConfluence combines uncoupled autoencoders on the complete set of features with regularized Inverse Optimal Transport on weakly connected features. We extensively benchmark scConfluence in several single-cell integration scenarios proving that it outperforms the state-of-the-art. We then demonstrate the biological relevance of scConfluence in three applications. We predict spatial patterns for Scgn, Synpr and Olah in scRNA-smFISH integration. We improve the classification of B cells and Monocytes in highly heterogeneous scRNA-scATAC-CyTOF integration. Finally, we reveal the joint contribution of Fezf2 and apical dendrite morphology in Intra Telencephalic neurons, based on morphological images and scRNA.

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Samaran, J., Peyre, G., Cantini, L.. 2024-02-29. scConfluence : single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features. https://doi.org/10.1101/2024.02.26.582051

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