Kan Yulong, Qi Yunjing, Zhang Zhongxiao, Liang Xikeng, Wang Weihao, Jin Shuilin
School of Mathematics/Harbin Institute of Technology, Harbin, China.
PLoS Comput Biol. 2025 Jan 16;21(1):e1012625. doi: 10.1371/journal.pcbi.1012625. eCollection 2025 Jan.
The rapid advance of large-scale atlas-level single cell RNA sequences and single-cell chromatin accessibility data provide extraordinary avenues to broad and deep insight into complex biological mechanism. Leveraging the datasets and transfering labels from scRNA-seq to scATAC-seq will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the lower capable of preserving fine-grained cell populations and intrinsic or extrinsic heterogeneity between datasets. Here, we present a robust deep transfer model based graph convolutional network, scTGCN, which achieves versatile performance in preserving biological variation, while achieving integration hundreds of thousands cells in minutes with low memory consumption. We show that scTGCN is powerful to the integration of mouse atlas data and multimodal data generated from APSA-seq and CITE-seq. Thus, scTGCN shows high label transfer accuracy and effectively knowledge transfer across different modalities.
大规模图谱级单细胞RNA序列和单细胞染色质可及性数据的迅速发展，为深入洞察复杂的生物学机制提供了非凡途径。利用这些数据集并将标签从scRNA-seq转移到scATAC-seq，将有助于探索单细胞组学数据。然而，当前的标签转移方法性能有限，这在很大程度上是因为在保留细粒度细胞群体以及数据集之间的内在或外在异质性方面能力较低。在此，我们提出了一种基于图卷积网络的强大深度转移模型scTGCN，它在保留生物学变异方面具有通用性能，同时能在数分钟内整合数十万个细胞且内存消耗低。我们表明，scTGCN对于整合小鼠图谱数据以及由APSA-seq和CITE-seq生成的多模态数据非常有效。因此，scTGCN显示出高标签转移准确性，并能在不同模态间有效地进行知识转移。
