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Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets

A preprint version of the article is available at bioRxiv.

Abstract

The rapid emergence of large-scale atlas-level single-cell RNA-seq datasets presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integration approaches to be not only computationally scalable, but also capable of preserving a wide range of fine-grained cell populations. We have created Portal, a unified framework of adversarial domain translation to learn harmonized representations of datasets. When compared to other state-of-the-art methods, Portal achieves better performance for preserving biological variation during integration, while achieving the integration of millions of cells, in minutes, with low memory consumption. We show that Portal is widely applicable to integrating datasets across different samples, platforms and data types. We also apply Portal to the integration of cross-species datasets with limited shared information among them, elucidating biological insights into the similarities and divergences in the spermatogenesis process among mouse, macaque and human.

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Fig. 1: Overview of Portal.
Fig. 2: Benchmarking study.
Fig. 3: Preservation of fine-grained neuron subpopulations.
Fig. 4: Construction of the mouse cell atlas across the entire organism.
Fig. 5: Integration of scRNA-seq and scATAC-seq data.
Fig. 6: Integration of spermatogenesis datasets across species.

Data availability

All data used in this work are publicly available from online sources as follows: for mouse brain cells from ref. 8 (http://dropviz.org), ref. 9 (http://mousebrain.org/downloads.html) and ref. 34 (GSE110823), the mouse cell atlas from the Tabula Muris Consortium7 (https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733) and the mouse lemur cell atlas from the Tabula Microcebus Consortium31 (https://figshare.com/projects/Tabula_Microcebus/112227); for human PBMCs from ref. 39 (GSE156478), ref. 53 (GSE132044) and 10X Genomics35 (https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k); for mouse spermatogenesis cells from ref. 45 (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6946/); for human spermatogenesis cells from ref. 42 (GSE142585); for macaque spermatogenesis cells from ref. 42 (GSE142585); for hematopoietic stem cells from ref. 54 (GSE72857) and ref. 55 (GSE81682); for reprogramming of induced pluripotent stem cells from ref. 56 (GSE122662); for human brain cells from ref. 36 (GSE164485) and ref. 37 (https://github.com/LieberInstitute/10xPilot_snRNAseq-human#work-with-the-data). Source data are provided with this paper.

Code availability

Portal software is available at https://github.com/YangLabHKUST/Portal. The codes for reproducing the results are available at https://github.com/jiazhao97/Portal-reproducibility. All codes are deposited in Zenodo repositories57,58.

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Acknowledgements

We acknowledge grants as follows: Hong Kong Research Grant Council grants nos. 16307818, 16301419 and 16308120, Hong Kong University of Science and Technology’s startup grant no. R9405, Guangdong-Hong Kong-Macao Joint Laboratory grant no. 2020B1212030001 and the RGC Collaborative Research Fund grant no. C6021-19EF to C.Y.; Hong Kong Research Grant Council grant no. 16101118, Hong Kong University of Science and Technology’s startup grant no. R9364 and the Lo Ka Chung Foundation through the Hong Kong Epigenomics Project and the Chau Hoi Shuen Foundation to A.R.W.; the Hong Kong University of Science and Technology Big Data for Bio Intelligence Laboratory (BDBI), the Hong Kong University of Science and Technology Center for Aging Science Research Program to C.Y. and A.R.W.; Hong Kong Research Grant Council grants nos. 24301419 and 14301120, the Chinese University of Hong Kong’s startup grant no.4930181 to Z.L.; the Shanghai Sailing Program grant no. 21YF140600 to J.M.

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J.Z. and G.W. conceived and developed the method. A.R.W. and C.Y. supervised the project. J.Z., G.W., Z.L., A.R.W. and C.Y. designed the experiments, performed the analyses and wrote the manuscript. J.M., Y.W. and T.M.C. provided critical feedback during the study and helped revise the manuscript.

Corresponding authors

Correspondence to Angela Ruohao Wu or Can Yang.

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Nature Computational Science thanks Mengjie Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Zhao, J., Wang, G., Ming, J. et al. Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets. Nat Comput Sci 2, 317–330 (2022). https://doi.org/10.1038/s43588-022-00251-y

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