Pandora: a tool to estimate dimensionality reduction stability of genotype data

Genotype datasets typically contain a large number of single-nucleotide polymorphisms for a comparatively small number of individuals. To identify similarities between individuals and to infer an individual's origin or membership to a population, dimensionality reduction techniques are routinely deployed. However, inherent (technical) difficulties such as missing or noisy data need to be accounted for when analyzing a lower dimensional representation of genotype data, and the intrinsic uncertainty of such analyses should be reported in all studies. However, to date, there exists no stability assessment technique for genotype data that can estimate this uncertainty.

Here, we present Pandora, a stability estimation framework for genotype data based on bootstrapping. Pandora computes an overall score to quantify the stability of the entire embedding, infers per-individual support values, and also deploys a $$ -means clustering approach to assess the uncertainty of assignments to potential cultural groups. Using published empirical and simulated datasets, we demonstrate the usage and utility of Pandora for studies that rely on dimensionality reduction techniques.

Pandora is available on GitHub: https://github.com/tschuelia/Pandora.