Single-cell multi-omics data continues to grow at an unprecedented pace, and effectively integrating different modalities holds the promise for better characterization of cell identities. Although a number of methods have demonstrated promising results in integrating multiple modalities from the same tissue, the complexity and scale of data compositions typically present in cell atlases still pose a significant challenge for existing methods. Here we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semi-supervised framework and uses a neural network to simultaneously train labeled and unlabeled data, enabling label transfer and joint visualization in an integrative framework. Using multiple atlas data and a biologically varying multi-modal data, we demonstrate scJoint is computationally efficient and consistently achieves significantly higher cell type label accuracy than existing methods while providing meaningful joint visualizations. This suggests scJoint is effective in overcoming the heterogeneity in different modalities towards a more comprehensive understanding of cellular phenotypes.

中文翻译：

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scJoint：用于图集规模的单细胞RNA-seq和ATAC-seq数据集成的转移学习

单细胞多组学数据以前所未有的速度持续增长，有效整合不同的模式为更好地表征细胞身份提供了希望。尽管许多方法在整合来自同一组织的多种模态方面已显示出令人鼓舞的结果，但通常存在于细胞图谱中的数据组成的复杂性和规模仍然对现有方法提出了重大挑战。在这里，我们介绍scJoint，这是一种转移学习方法，用于整合图集规模的scRNA-seq和scATAC-seq数据的异构集合。scJoint在半监督的框架中利用来自带注释的scRNA-seq数据的信息，并使用神经网络同时训练标记和未标记的数据，从而在集成框架中实现标记转移和联合可视化。使用多个图集数据和生物学上变化的多模式数据，我们证明scJoint具有高效的计算能力，并且在提供有意义的联合可视化效果的同时，与现有方法相比，始终能够获得更高的细胞类型标签准确性。这表明scJoint可有效克服不同模式下的异质性，从而更全面地了解细胞表型。