scJoint可将数据库级别的单细胞RNA-seq和ATAC-seq数据与迁移学习整合
2022-01-24   阅读:583   来源:自然

澳大利亚悉尼大学Y. X. Rachel Wang、美国斯坦福大学Wing H. Wong等研究人员合作表明,scJoint可将数据库级别的单细胞RNA-seq和ATAC-seq数据与迁移学习整合。这一研究成果与2022年1月20日在线发表在国际学术期刊《自然—生物技术》上。

研究人员提出了scJoint,一种转移学习方法,用于整合图谱规模的、异质的scRNA-seq和scATAC-seq数据集合。scJoint在一个半监督框架中利用注释的scRNA-seq数据信息,并使用一个神经网络同时训练标记的和未标记的数据,从而在一个整合的框架中进行标签转移和联合可视化。

利用图谱数据以及用ASAP-seq和CITE-seq生成的多模态数据集,研究人员证明scJoint的计算效率很高,并且始终比现有的方法获得更高的细胞类型标签准确性,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,使研究人员能够更全面地了解细胞表型。

据了解,单细胞多组学数据继续以前所未有的速度增长。尽管一些方法在整合来自同一组织的几种数据模式方面取得了可喜的成果,但细胞图谱中的数据组成的复杂性和规模仍然构成了挑战。

附:英文原文

Title: scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning

Author: Lin, Yingxin, Wu, Tung-Yu, Wan, Sheng, Yang, Jean Y. H., Wong, Wing H., Wang, Y. X. Rachel

Issue&Volume: 2022-01-20

Abstract: 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. Integration of data from single-cell RNA-seq and ATAC-seq is achieved with transfer learning.

DOI: 10.1038/s41587-021-01161-6

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