Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France.
CNRS and DMA de l'Ecole Normale Supérieure, CNRS, Ecole Normale Supérieure, Université PSL, Paris, France.
Nat Commun. 2024 Sep 5;15(1):7762. doi: 10.1038/s41467-024-51382-x.
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.
未配对的多模态单细胞数据的大量出现，促使人们越来越多地研究开发对角整合方法。然而，由于特征转换，现有技术会丢失生物信息，并且难以处理特定模态的群体。为了克服这些关键限制，我们在这里引入了 scConfluence，这是一种单细胞对角整合的方法。scConfluence 结合了完整特征集上的非耦合自动编码器和弱连接特征上的正则化逆最优传输。我们在几个单细胞整合场景中广泛地对 scConfluence 进行基准测试，证明它优于现有技术。然后，我们在三个应用中展示了 scConfluence 的生物学相关性。我们预测了 Scgn、Synpr 和 Olah 在 scRNA-smFISH 整合中的空间模式。我们提高了高度异质的 scRNA-scATAC-CyTOF 整合中 B 细胞和单核细胞的分类。最后，我们基于形态学图像和 scRNA 揭示了 Intra Telencephalic 神经元中 Fezf2 和顶树突形态的联合贡献。
