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pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules

View ORCID ProfileGilles Gut, View ORCID ProfileStefan G. Stark, View ORCID ProfileGunnar Rätsch, View ORCID ProfileNatalie R. Davidson
doi: https://doi.org/10.1101/2021.01.28.428664
Gilles Gut
1Department of Computer Science, ETH Zürich, Switzerland
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Stefan G. Stark
1Department of Computer Science, ETH Zürich, Switzerland
2Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Gunnar Rätsch
1Department of Computer Science, ETH Zürich, Switzerland
2Swiss Institute of Bioinformatics, Lausanne, Switzerland
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  • For correspondence: natalie.davidson@cuanschutz.edu gunnar.ratsch@ratschlab.org
Natalie R. Davidson
1Department of Computer Science, ETH Zürich, Switzerland
2Swiss Institute of Bioinformatics, Lausanne, Switzerland
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  • For correspondence: natalie.davidson@cuanschutz.edu gunnar.ratsch@ratschlab.org
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ABSTRACT

Motivation Deep learning techniques have yielded tremendous progress in the field of computational biology over the last decade, however many of these techniques are opaque to the user. To provide interpretable results, methods have incorporated biological priors directly into the learning task; one such biological prior is pathway structure. While pathways represent most biological processes in the cell, the high level of correlation and hierarchical structure make it complicated to determine an appropriate computational representation.

Results Here, we present pathway module Variational Autoencoder (pmVAE). Our method encodes pathway information by restricting the structure of our VAE to mirror gene-pathway memberships. Its architecture is composed of a set of subnetworks, which we refer to as pathway modules. The subnetworks learn interpretable latent representations by factorizing the latent space according to pathway gene sets. We directly address correlation between pathways by balancing a module-specific local loss and a global reconstruction loss. Furthermore, since many pathways are by nature hierarchical and therefore the product of multiple downstream signals, we model each pathway as a multidimensional vector. Due to their factorization over pathways, the representations allow for easy and interpretable analysis of multiple downstream effects, such as cell type and biological stimulus, within the contexts of each pathway. We compare pmVAE against two other state-of-the-art methods on two single-cell RNA-seq case-control data sets, demonstrating that our pathway representations are both more discriminative and consistent in detecting pathways targeted by a perturbation.

Availability and implementation https://github.com/ratschlab/pmvae

Competing Interest Statement

The authors have declared no competing interest.

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 January 30, 2021.
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pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules
Gilles Gut, Stefan G. Stark, Gunnar Rätsch, Natalie R. Davidson
bioRxiv 2021.01.28.428664; doi: https://doi.org/10.1101/2021.01.28.428664
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pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules
Gilles Gut, Stefan G. Stark, Gunnar Rätsch, Natalie R. Davidson
bioRxiv 2021.01.28.428664; doi: https://doi.org/10.1101/2021.01.28.428664

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