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Query to reference single-cell integration with transfer learning

View ORCID ProfileMohammad Lotfollahi, Mohsen Naghipourfar, View ORCID ProfileMalte D. Luecken, Matin Khajavi, Maren Büttner, Ziga Avsec, View ORCID ProfileAlexander V. Misharin, View ORCID ProfileFabian J. Theis
doi: https://doi.org/10.1101/2020.07.16.205997
Mohammad Lotfollahi
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
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  • For correspondence: mohammad.lotfollahi@helmholtz-muenchen.de
Mohsen Naghipourfar
2Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
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Malte D. Luecken
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
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Matin Khajavi
2Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
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Maren Büttner
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
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Ziga Avsec
3Department of Computer Science, Technical University of Munich, Munich, Germany
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Alexander V. Misharin
4Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University, Chicago, USA
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Fabian J. Theis
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
5Department of Mathematics, Technical University of Munich, Munich, Munich, Germany
6School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
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Abstract

Large single-cell atlases are now routinely generated with the aim of serving as reference to analyse future smaller-scale studies. Yet, learning from reference data is complicated by batch effects between datasets, limited availability of computational resources, and sharing restrictions on raw data. Leveraging advances in machine learning, we propose a deep learning strategy to map query datasets on top of a reference called single-cell architectural surgery (scArches, https://github.com/theislab/scarches). It uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building, and the contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, and whole organism atlases, we showcase that scArches preserves nuanced biological state information while removing batch effects in the data, despite using four orders of magnitude fewer parameters compared to de novo integration. To demonstrate mapping disease variation, we show that scArches preserves detailed COVID-19 disease variation upon reference mapping, enabling discovery of new cell identities that are unseen during training. We envision our method to facilitate collaborative projects by enabling the iterative construction, updating, sharing, and efficient use of reference atlases.

Competing Interest Statement

F.J.T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc.

Footnotes

  • ↵‡ fabian.theis{at}helmholtz-muenchen.de

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 16, 2020.
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Query to reference single-cell integration with transfer learning
Mohammad Lotfollahi, Mohsen Naghipourfar, Malte D. Luecken, Matin Khajavi, Maren Büttner, Ziga Avsec, Alexander V. Misharin, Fabian J. Theis
bioRxiv 2020.07.16.205997; doi: https://doi.org/10.1101/2020.07.16.205997
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Query to reference single-cell integration with transfer learning
Mohammad Lotfollahi, Mohsen Naghipourfar, Malte D. Luecken, Matin Khajavi, Maren Büttner, Ziga Avsec, Alexander V. Misharin, Fabian J. Theis
bioRxiv 2020.07.16.205997; doi: https://doi.org/10.1101/2020.07.16.205997

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