lcUMAPtSNE: use of non-linear dimensionality reduction techniques with genotype likelihoods

BACKGROUND: Understanding population structure is essential for conservation genetics, as it provides insights into population connectivity and supports the development of targeted strategies to preserve genetic diversity and adaptability. T-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) have proven effective for revealing population genetic structures in human and model organisms using hard-called genotypes, but their application in wild species using genotype likelihoods from low coverage sequencing (as a cost-saving measure) remains underexplored. RESULTS: Here, we present a Jupyter Notebook-based workflow that facilitates the use of UMAP and t-SNE on genotype likelihood-derived principal components. This workflow is demonstrated using medium to low-coverage whole-genome sequencing data from scimitar-horned oryx, which has been reintroduced into the wild and faces multiple conservation challenges. CONCLUSION: Detailed guidance on hyperparameter tuning and practical implementation is also provided, enhancing the application of these methods in wildlife genetics to potentially support biodiversity conservation. lcUMAPtSNE is designed as a complementary and exploratory dimensionality reduction tool, rather than a replacement or benchmarking framework, and its strengths are demonstrated here using empirical data.