pystatsgenomic 0.1.1

Genomics and computational-biology statistical computing for Python

PyStatsGenomic

Genomics and computational-biology statistical computing for Python.

Status: 0.1.0 — first module shipped. Hardy–Weinberg equilibrium is available now; more statistical-genetics methods are on the roadmap below.

PyStatsGenomic is part of the open-core PyStatistics family:

Package	Layer
pystatistics	Fundamental, general statistics
pystatsbio	Biotech / pharma statistics
pystatsclinical	Clinical-trial / clinical-research statistics
pystatsgenomic	Genomics / computational-biology statistics
pystatsfinance	Financial / quantitative statistics
pystatsinsurance	Actuarial / insurance statistics

Like its siblings, it builds on pystatistics for the general statistical layer and adds methods specific to genomics.

What's available now (0.1.0)

Hardy–Weinberg equilibrium for a biallelic locus, via the exact test (Wigginton, Cui & Abecasis 2005, the default) or the Pearson chi-square test:

from pystatsgenomic import hwe

result = hwe.hardy_weinberg(n_AA=298, n_Aa=489, n_aa=213)
print(result.summary())

# Allele frequencies on their own:
freqs = hwe.allele_frequencies(n_AA=298, n_Aa=489, n_aa=213)

hardy_weinberg reports allele frequencies, expected genotype counts, the chi-square statistic, and the p-value (1 df — HWE estimates an allele frequency). It fails loud on negative/non-integer counts and an empty sample.

Roadmap (candidates, not commitments)

• Linkage disequilibrium (D, D′, r²) and genotyping QC metrics.
• Single-locus association and GWAS-scan orchestration (with genomic control).
• Population-genetics summaries: Fst, nucleotide diversity, Tajima's D.

Installation

pip install pystatsgenomic

License

MIT © Hai-Shuo. Part of the SGCX open-core ecosystem.