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

• Published in: Nature biotechnology Vol. 40; no. 5; pp. 703 - 710
• Main Authors: Lin, Yingxin, Wu, Tung-Yu, Wan, Sheng, Yang, Jean Y. H., Wong, Wing H., Wang, Y. X. Rachel
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-05-2022; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/2401; 631/114/2415; Accuracy; Agriculture; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Datasets; Gene expression; Heterogeneity; Learning; Life Sciences; Neural networks; Phenotypes; Sparsity; Transfer learning
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/s41587-021-01161-6

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.