scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning.
scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning.
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DOI：
10.1038/s41587-021-01161-6
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发表时间：
2022-05
影响因子：
46.9
通讯作者：
Wang, Y. X. Rachel
中科院分区：
文献类型：
--
作者：
Lin, Yingxin;Wu, Tung-Yu;Wan, Sheng;Yang, Jean Y. H.;Wong, Wing H.;Wang, Y. X. Rachel
单细胞多组学数据继续以前所未有的速度增长。虽然几种方法已经证明了整合来自同一组织的几种数据模式的有希望的结果，但细胞图谱中存在的数据组成的复杂性和规模仍然构成了挑战。在这里，我们提出了scJoint，这是一种迁移学习方法，可以整合scRNA-seq和scATAC-seq数据的atlas-scale，异构集合。scJoint在半监督框架中利用来自注释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，允许在集成框架中进行标记转移和联合可视化。使用atlas数据以及使用ASAP-seq和CITE-seq生成的多模态数据集，我们证明了scJoint在计算上是高效的，并且始终实现比现有方法更高的细胞类型标记准确性，同时提供有意义的联合可视化。因此，scJoint克服了不同数据模式的异质性，从而能够更全面地了解细胞表型。
Single-cell multiomics data continues to grow at an unprecedented pace. Although several methods have demonstrated promising results in integrating several data modalities from the same tissue, the complexity and scale of data compositions present in cell atlases still pose a challenge. 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 semisupervised framework and uses a neural network to simultaneously train labeled and unlabeled data, allowing label transfer and joint visualization in an integrative framework. Using atlas data as well as multimodal datasets generated with ASAP-seq and CITE-seq, we demonstrate that scJoint is computationally efficient and consistently achieves substantially higher cell-type label accuracy than existing methods while providing meaningful joint visualizations. Thus, scJoint overcomes the heterogeneity of different data modalities to enable a more comprehensive understanding of cellular phenotypes.
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影响因子：
48
作者：
Amodio, Matthew;van Dijk, David;Srinivasan, Krishnan;Chen, William S.;Mohsen, Hussein;Moon, Kevin R.;Campbell, Allison;Zhao, Yujiao;Wang, Xiaomei;Venkataswamy, Manjunatha;Desai, Anita;Ravi, V.;Kumar, Priti;Montgomery, Ruth;Wolf, Guy;Krishnaswamy, Smita
通讯作者：
Krishnaswamy, Smita
影响因子：
12.3
作者：
Abdelaal, Tamim;Michielsen, Lieke;Mahfouz, Ahmed
通讯作者：
Mahfouz, Ahmed
影响因子：
48
作者：
Barkas, Nikolas;Petukhov, Viktor;Kharchenko, Peter V.
通讯作者：
Kharchenko, Peter V.
DOI：
10.1126/science.aau0730
发表时间：
2018-09-28
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Cao J;Cusanovich DA;Ramani V;Aghamirzaie D;Pliner HA;Hill AJ;Daza RM;McFaline-Figueroa JL;Packer JS;Christiansen L;Steemers FJ;Adey AC;Trapnell C;Shendure J
通讯作者：
Shendure J
影响因子：
48
作者：
Korsunsky, Ilya;Millard, Nghia;Raychaudhuri, Soumya
通讯作者：
Raychaudhuri, Soumya