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Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space

Lei Xiong, Kang Tian, Yuzhe Li, Qiangfeng Cliff Zhang
doi: https://doi.org/10.1101/2021.04.06.438536
Lei Xiong
1MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China 100084
2Tsinghua-Peking Center for Life Sciences, Beijing, China 100084
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Kang Tian
1MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China 100084
2Tsinghua-Peking Center for Life Sciences, Beijing, China 100084
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Yuzhe Li
1MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China 100084
3Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 100871
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Qiangfeng Cliff Zhang
1MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China 100084
2Tsinghua-Peking Center for Life Sciences, Beijing, China 100084
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  • For correspondence: qczhang@tsinghua.edu.cn
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ABSTRACT

Single-cell RNA-seq and ATAC-seq analyses have been widely applied to decipher cell-type and regulation complexities. However, experimental conditions often confound biological variations when comparing data from different samples. For integrative single-cell data analysis, we have developed SCALEX, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space. We demonstrate that SCALEX accurately and efficiently integrates heterogenous single-cell data using multiple benchmarks. It outperforms competing methods, especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We demonstrate the advantages of SCALEX by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19, which were assembled from multiple data sources and can keep growing through the inclusion of new incoming data. Analyses based on these atlases revealed the complex cellular landscapes of human and mouse tissues and identified multiple peripheral immune subtypes associated with COVID-19 disease severity.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵4 Co-first authorship

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 08, 2021.
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Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space
Lei Xiong, Kang Tian, Yuzhe Li, Qiangfeng Cliff Zhang
bioRxiv 2021.04.06.438536; doi: https://doi.org/10.1101/2021.04.06.438536
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Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space
Lei Xiong, Kang Tian, Yuzhe Li, Qiangfeng Cliff Zhang
bioRxiv 2021.04.06.438536; doi: https://doi.org/10.1101/2021.04.06.438536

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