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Benchmarking atlas-level data integration in single-cell genomics

View ORCID ProfileMD Luecken, View ORCID ProfileM Büttner, View ORCID ProfileK Chaichoompu, A Danese, View ORCID ProfileM Interlandi, View ORCID ProfileMF Mueller, View ORCID ProfileDC Strobl, View ORCID ProfileL Zappia, View ORCID ProfileM Dugas, View ORCID ProfileM Colomé-Tatché, View ORCID ProfileFJ Theis
doi: https://doi.org/10.1101/2020.05.22.111161
MD Luecken
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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M Büttner
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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K Chaichoompu
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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A Danese
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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M Interlandi
2Institute of Medical Informatics, University of Münster, Münster, Germany
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MF Mueller
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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DC Strobl
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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L Zappia
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
3Dep of Mathematics, Technische Universität München, Garching bei München, Germany
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M Dugas
2Institute of Medical Informatics, University of Münster, Münster, Germany
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M Colomé-Tatché
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
4European Research Institute for the Biology of Ageing, University of Groningen, University Medical centre Groningen, Groningen, The Netherlands
5TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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  • For correspondence: maria.colome@helmholtz-muenchen.de fabian.theis@helmholtz-muenchen.de
FJ Theis
1Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
3Dep of Mathematics, Technische Universität München, Garching bei München, Germany
5TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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  • For correspondence: maria.colome@helmholtz-muenchen.de fabian.theis@helmholtz-muenchen.de
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Abstract

Cell atlases often include samples that span locations, labs, and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration.

Choosing a data integration method is a challenge due to the difficulty of defining integration success. Here, we benchmark 38 method and preprocessing combinations on 77 batches of gene expression, chromatin accessibility, and simulation data from 23 publications, altogether representing >1.2 million cells distributed in nine atlas-level integration tasks. Our integration tasks span several common sources of variation such as individuals, species, and experimental labs. We evaluate methods according to scalability, usability, and their ability to remove batch effects while retaining biological variation.

Using 14 evaluation metrics, we find that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, BBKNN, Scanorama, and scVI perform well, particularly on complex integration tasks; Seurat v3 performs well on simpler tasks with distinct biological signals; and methods that prioritize batch removal perform best for ATAC-seq data integration. Our freely available reproducible python module can be used to identify optimal data integration methods for new data, benchmark new methods, and improve method development.

Competing Interest Statement

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

Footnotes

  • Added 1 citation and amended spelling of author names with accents.

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 4.0 International license.
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Posted May 27, 2020.
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Benchmarking atlas-level data integration in single-cell genomics
MD Luecken, M Büttner, K Chaichoompu, A Danese, M Interlandi, MF Mueller, DC Strobl, L Zappia, M Dugas, M Colomé-Tatché, FJ Theis
bioRxiv 2020.05.22.111161; doi: https://doi.org/10.1101/2020.05.22.111161
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Benchmarking atlas-level data integration in single-cell genomics
MD Luecken, M Büttner, K Chaichoompu, A Danese, M Interlandi, MF Mueller, DC Strobl, L Zappia, M Dugas, M Colomé-Tatché, FJ Theis
bioRxiv 2020.05.22.111161; doi: https://doi.org/10.1101/2020.05.22.111161

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