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.
