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Visualizing Population Structure with Variational Autoencoders

C. J. Battey, Gabrielle C. Coffing, View ORCID ProfileAndrew D. Kern
doi: https://doi.org/10.1101/2020.08.12.248278
C. J. Battey
1University of Oregon Institute of Ecology and Evolution
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  • For correspondence: cbattey2@uoregon.edu
Gabrielle C. Coffing
1University of Oregon Institute of Ecology and Evolution
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Andrew D. Kern
1University of Oregon Institute of Ecology and Evolution
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Abstract

Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here we explore the utility of variational autoencoders (VAEs) – generative machine learning models in which a pair of neural networks seek to first compress and then recreate the input data – for visualizing population genetic variation. VAEs incorporate non-linear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP. Our implementation, which we call popvae, is available as a command-line python program at github.com/kr-colab/popvae. The approach yields latent embeddings that capture subtle aspects of population structure in humans and Anopheles mosquitoes, and can generate artificial genotypes characteristic of a given sample or population.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • In this revision we replicated our tSNE and UMAP analyses with parameters intended to favor preservation of larger-scale structure, edited the text for clarity, and added more citations to prior work.

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 October 19, 2020.
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Visualizing Population Structure with Variational Autoencoders
C. J. Battey, Gabrielle C. Coffing, Andrew D. Kern
bioRxiv 2020.08.12.248278; doi: https://doi.org/10.1101/2020.08.12.248278
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Visualizing Population Structure with Variational Autoencoders
C. J. Battey, Gabrielle C. Coffing, Andrew D. Kern
bioRxiv 2020.08.12.248278; doi: https://doi.org/10.1101/2020.08.12.248278

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