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Alejandro Correa Rojo1,2, Pieter Moris3, Hanne Meuwissen1

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We developed DAPCy, a Python package for Discriminant Analysis of Principal Components (DAPC), to efficiently analyze large population genetics datasets. DAPCy offers improved scalability and speed compared to existing R implementations.

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Area of Science:

  • Population genetics
  • Bioinformatics
  • Computational biology

Background:

  • Discriminant Analysis of Principal Components (DAPC) is crucial for population genetics.
  • Existing R implementations of DAPC struggle with large genomic datasets.

Purpose of the Study:

  • Introduce DAPCy, a Python package for enhanced DAPC analysis.
  • Improve scalability and efficiency for large-scale genomic data.

Main Methods:

  • Utilize scikit-learn library in Python.
  • Employ compressed sparse matrices and truncated SVD for dimensionality reduction.
  • Implement training-test cross-validation for model evaluation.

Main Results:

  • DAPCy processes large genomic datasets (thousands of samples/features) faster and with less memory.
  • Demonstrated computational advantages using Plasmodium falciparum and 1000 Genomes Project datasets.
  • DAPCy includes de novo clustering and comprehensive visualization tools.

Conclusions:

  • DAPCy provides a scalable and efficient solution for DAPC analysis.
  • Facilitates genetic structure assessment in large population genomics studies.
  • Offers advanced features for clustering, visualization, and reporting.