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© Jack Moreh_stockvault
Publication : Nature communications

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

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Nature communications - 05 Sep 2024

Samaran J, Peyré G, Cantini L

Link to Pubmed [PMID] – 39237488

Link to DOI – 10.1038/s41467-024-51382-x

Nat Commun 2024 Sep; 15(1): 7762

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 introduce scConfluence, a 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.