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Cited by 166Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksGenomics and Chromatin Dynamics

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

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Lin, Yingxin, Wu, Tung-Yu, Wan, Sheng, Yang, Jean Y. H., Wong, Wing H., Wang, Y. X. Rachel·2022·Nature Biotechnology
DOI 10.1038/s41587-021-01161-6PMC PMC9186323

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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.

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  • Lin et al. proposed the scJoint framework, which applied semi-supervised learning of scRNA-seq datasets with cell-type information, and further transferred cellular annotation to scATAC-seq datasets and multimodal data integration.

    “In single-cell analysis, Lin et al. proposed the scJoint framework [181], which applied semi-supervised learning of scRNA-seq datasets with cell-type information, and further transferred cellular annotation to scATAC-seq datasets and multimodal data integration.”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [181]

  • scJoint was developed to transfer labels from scRNA-seq to scATAC-seq data, which may enable improved cell GRN inference.

    “Recently, scJoint38 was developed to transfer labels from scRNA-seq to scATAC-seq data, which may enable improved cell GRN inference.”

    From Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data (2025) · cites this paper as [38]

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Research works printed but not linked: 35

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Cited by

Sorted by citations

Authors
Lin, Y., Wu, T.-Y., Wan, S., Yang, J. Y. H., Wong, W. H., & Wang, Y. X. R.
Affiliations
  • The University of Sydney (AU)
  • Stanford University (US)
  • National Yang Ming Chiao Tung University (TW)
Corresponding author
  • Wong, Wing H.
  • Wang, Y. X. Rachel
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2022-01-20
Volume
40
Issue
5
Pages
703-710
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/s41587-021-01161-6
PMC
PMC9186323
OpenAlex
W4205514149
Cited by
166
References
8
Citations by year
2026: 35 · 2025: 42 · 2024: 36 · 2023: 39 · 2022: 13 · 2020: 1
FWCI
10.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Department of Education and Training | Australian Research Council (DP170100654, DE180101252)
  • U.S. Department of Health & Human Services | National Institutes of Health (R01 HG010359, P50 HG007735)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
single-cell RNA sequencing, single-cell ATAC-seq, transfer learning, cell type annotation, cell atlases, semi-supervised learning, CITE-seq
MeSH terms
Machine Learning, Exome Sequencing, Chromatin Immunoprecipitation Sequencing, RNA-Seq, Sequence Analysis, RNA, Single-Cell Analysis

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