claw-ancestry-pca

To run PCA on VCF files for ancestry analysis, execute the 'ancestry_pca' script with your study cohort VCF and a population map to compute genetic structure against SGDP data. It requires PLINK and bcftools to process the VCF and generate multi-panel PCA figures.

PCA for population genetics is a statistical method that decomposes genetic diversity into principal components to map ancestry. By projecting your cohort against the Simons Genome Diversity Project data, it assigns population labels and determines global genetic affinities.

Yes, you need PLINK and bcftools installed to compute genetic diversity with SGDP data. These dependencies handle the underlying VCF processing and genetic data manipulation required before Python libraries like pandas and scikit-learn can perform the statistical analysis.

Yes, you can visualize population structure from a VCF file using matplotlib. The skill generates multi-panel PCA composite figures with adjusted text labels to plot the computed ancestry decomposition and population mapping results for visual analysis.

The best way to assign population labels is to use PCA against the Simons Genome Diversity Project dataset. This maps your study cohort samples to known population affinities and generates a markdown report containing the population assignments and statistics.

When analyzing genetic diversity with scikit-learn, limitations include the need for prerequisite VCF formatting via bcftools and PLINK. The analysis relies on Python libraries like pandas and numpy for statistical computation, requiring proper environment setup to avoid processing errors.