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

Jules Samaran, Gabriel Peyré and Laura Cantini ()
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Jules Samaran: Machine Learning for Integrative Genomics Group
Gabriel Peyré: Université PSL
Laura Cantini: Machine Learning for Integrative Genomics Group

Nature Communications, 2024, vol. 15, issue 1, 1-20

Abstract: 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 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.

Date: 2024
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DOI: 10.1038/s41467-024-51382-x

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