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

Nature Biotechnology volume 40, pages 703–710 (2022)Cite this article

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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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Fig. 1: Overview of scJoint.
Fig. 2: Analysis of mouse cell atlas subset data containing 19 overlapping cell types from RNA and ATAC.
Fig. 3: Analysis of mouse cell atlas full data.
Fig. 4: Integration of multimodal PBMC data across biological conditions: with (stimulation) or without (control) T cell activation.
Fig. 5: Analysis of paired gene expression and chromatin accessibility data from SNARE-seq.

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Data availability

All single-cell datasets used in this paper are publicly available. • Mouse atlas data. The scRNA-seq dataset was downloaded from Tabula Muris5 (https://tabula-muris.ds.czbiohub.org/). The sci-ATAC-seq dataset of Cusanovich et al.26 was downloaded from https://atlas.gs.washington.edu/mouse-atac/. • Human fetal atlas data. The scRNA-seq dataset from Cao et al.27 was downloaded from GSE156793. The scATAC-seq dataset from Domcke et al.28 was downloaded from GSE149683. • SNARE-seq data. The SNARE-seq dataset of adult mouse cerebral cortex14 was downloaded from GSE126074. • Multimodal PBMC data. The ASAP-seq and CITE-seq datasets from Mimitou et al.34 were obtained from GSE156478. • Human hematopoiesis data. The scRNA-seq and scATAC-seq datasets from Granja et al.40 were downloaded from https://github.com/GreenleafLab/MPAL-Single-Cell-2019.

Code availability

scJoint was implemented using PyTorch (v.1.0.0) with code available at https://github.com/SydneyBioX/scJoint.

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Acknowledgements

We gratefully acknowledge the following funding sources: Research Training Program Tuition Fee Offset and Stipend Scholarship and Chen Family Research Scholarship to Y.L.; Australian Research Council Discovery Project grant (DP170100654) and AIR@innoHK program of the Innovation and Technology Commission of Hong Kong to J.Y.H.Y.; Australian Research Council DECRA Fellowship (DE180101252) to Y.X.R.W; NIH grants R01 HG010359 and P50 HG007735 to W.H.W.

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Author notes

  1. These authors contributed equally: Yingxin Lin and Tung-Yu Wu.

Authors and Affiliations

  1. School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia

    Yingxin Lin, Jean Y. H. Yang & Y. X. Rachel Wang

  2. Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia

    Yingxin Lin & Jean Y. H. Yang

  3. Department of Statistics, Stanford University, Stanford, CA, USA

    Tung-Yu Wu & Wing H. Wong

  4. Institute of Electronics, National Chiao Tung University, Hsinchu, Taiwan

    Sheng Wan

  5. Laboratory of Data Discovery for Health Limited, Science Park, Hong Kong SAR, China

    Jean Y. H. Yang

  6. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    Wing H. Wong

  7. Bio-X Program, Stanford University, Stanford, CA, USA

    Wing H. Wong

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Contributions

T.-Y.W., W.H.W. and Y.X.R.W. conceived and designed this project; Y.L., T.-Y.W. and S.W. performed data preprocessing, model development and evaluation of results; J.Y.H.Y., W.H.W. and Y.X.R.W. supervised the execution; Y.L., J.Y.H.Y., W.H.W. and Y.X.R.W. wrote the manuscript. All authors read and approved the manuscript.

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Correspondence to Wing H. Wong or Y. X. Rachel Wang.

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Nature Biotechnology thanks Jingshu Wang, Nancy Zhang and Qing Nie for their contribution to the peer review of this work.

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Lin, Y., Wu, TY., Wan, S. et al. scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning. Nat Biotechnol 40, 703–710 (2022). https://doi.org/10.1038/s41587-021-01161-6

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