ArticleBioinformatics advances2025
DAPCy: a Python package for the discriminant analysis of principal components method for population genetic analyses.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Summary: The Discriminant Analysis of Principal Components method is a pivotal tool in population genetics, combining principal component analysis and linear discriminant analysis to assess the genetic structure of populations using genetic markers, focusing on the description of variation between genetic clusters. Despite its utility, the original R implementation in the adegenet package faces computational challenges with large genomic datasets. To address these limitations, we introduce DAPCy, a Python package leveraging the scikit-learn library to enhance the method's scalability and efficiency. DAPCy supports large datasets by utilizing compressed sparse matrices and truncated singular value decomposition for dimensionality reduction, coupled with training-test cross-validation for robust model evaluation. It also includes modules for Availability and implementation: DAPCy can be installed as a Python package through pip. Source code is available on https://gitlab.com/uhasselt-bioinfo/dapcy. Documentation and a tutorial can be found on https://uhasselt-bioinfo.gitlab.io/dapcy/.
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