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。





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


©2022年07月01日 22:05:25