Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.248278v1?rss=1

Authors: Kern, A. D., Coffing, G. C., Battey, C. J.

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.

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