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Compositional perturbation autoencoder for single-cell response modeling

View ORCID ProfileMohammad Lotfollahi, View ORCID ProfileAnna Klimovskaia Susmelj, Carlo De Donno, Yuge Ji, Ignacio L. Ibarra, F. Alexander Wolf, Nafissa Yakubova, Fabian J. Theis, David Lopez-Paz
doi: https://doi.org/10.1101/2021.04.14.439903
Mohammad Lotfollahi
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
3School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
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  • ORCID record for Mohammad Lotfollahi
Anna Klimovskaia Susmelj
2Facebook AI, 6 Rue Ménars, Paris, 75002, France
5Swiss Data Science Center, Zurich, Switzerland
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  • ORCID record for Anna Klimovskaia Susmelj
Carlo De Donno
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
7Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Bavaria, Germany
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Yuge Ji
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
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Ignacio L. Ibarra
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
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F. Alexander Wolf
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
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Nafissa Yakubova
2Facebook AI, 6 Rue Ménars, Paris, 75002, France
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Fabian J. Theis
1Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany
3School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
4Department of Mathematics, Technical University of Munich, Munich, Munich, Germany
6Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
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  • For correspondence: fabian.theis@helmholtz-muenchen.de
David Lopez-Paz
2Facebook AI, 6 Rue Ménars, Paris, 75002, France
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Abstract

Recent advances in multiplexing single-cell transcriptomics across experiments are enabling the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret and prioritize perturbations. Here, we present the Compositional Perturbation Autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug response across different cell types, doses, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, allowing drug similarity analysis and predictions for unseen dosages and drug combinations. We show CPA accurately models single-cell perturbations across compounds, dosages, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying it captures features that distinguish different interaction programs. Finally, we demonstrate CPA allows in-silico generation of 5,329 missing combinations (97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design by enabling in-silico response prediction at the single-cell level.

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. F.A.W. is a full-time employee of Cellarity Inc., and has ownership interest in Cellarity, Inc.

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 April 15, 2021.
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Compositional perturbation autoencoder for single-cell response modeling
Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Yuge Ji, Ignacio L. Ibarra, F. Alexander Wolf, Nafissa Yakubova, Fabian J. Theis, David Lopez-Paz
bioRxiv 2021.04.14.439903; doi: https://doi.org/10.1101/2021.04.14.439903
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Compositional perturbation autoencoder for single-cell response modeling
Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Yuge Ji, Ignacio L. Ibarra, F. Alexander Wolf, Nafissa Yakubova, Fabian J. Theis, David Lopez-Paz
bioRxiv 2021.04.14.439903; doi: https://doi.org/10.1101/2021.04.14.439903

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