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Visualizing population structure with variational autoencoders.

C J Battey¹, Gabrielle C Coffing¹, Andrew D Kern¹

¹Department of Biology, University of Oregon Institute of Ecology and Evolution, Eugene, Oregon, 97403.

G3 (Bethesda, Md.)

|February 9, 2021

Summary

This summary is machine-generated.

Variational autoencoders (VAEs) offer a novel approach for visualizing population genetic variation. This machine learning method better preserves global geometry compared to existing techniques, aiding in population structure analysis.

Keywords:

data visualization deep learning machine learning neural network pca population genetics population structure variational autoencoder

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Area of Science:

Population Genetics
Machine Learning
Bioinformatics

Background:

Dimensionality reduction is crucial for visualizing population structure from genetic data.
Existing methods like PCA, t-SNE, and UMAP have limitations in preserving global geometry or plotting dimensions.

Purpose of the Study:

To explore the utility of Variational Autoencoders (VAEs) for visualizing population genetic variation.
To develop and implement a VAE-based tool for enhanced genetic data visualization.

Main Methods:

Utilized VAEs, a type of generative machine learning model, for dimensionality reduction.
Implemented a VAE model named popvae as a command-line Python program.
Compared VAE performance against t-SNE and UMAP for preserving global geometry.

Main Results:

VAEs, implemented as popvae, better preserve global geometry than t-SNE and UMAP.
The VAE approach captures subtle population structure in human and mosquito genetic data.
Generated artificial genotypes characteristic of specific samples or populations.

Conclusions:

VAEs provide a powerful and effective tool for visualizing population genetic variation.
Popvae enhances the analysis of population structure by preserving global geometric relationships.
The VAE approach offers potential for both visualization and synthetic data generation in population genetics.