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Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

Mohammad Lotfollahi, F. Alexander Wolf, Fabian J. Theis
doi: https://doi.org/10.1101/478503
Mohammad Lotfollahi
Helmholtz Zentrum Munchen
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F. Alexander Wolf
Helmholtz Zentrum Munchen
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Fabian J. Theis
Helmholtz Zentrum Munchen
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  • For correspondence: fabian.theis@helmholtz-muenchen.de
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Abstract

Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been proposed based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data i.e. out-of-sample have yet been demonstrated. Here, we present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell type and species specific response implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.

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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 November 29, 2018.
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Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species
Mohammad Lotfollahi, F. Alexander Wolf, Fabian J. Theis
bioRxiv 478503; doi: https://doi.org/10.1101/478503
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Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species
Mohammad Lotfollahi, F. Alexander Wolf, Fabian J. Theis
bioRxiv 478503; doi: https://doi.org/10.1101/478503

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