hwetests 0.7.4

A python package containing two statistical tests for HWE testing: Gibbs Sampling test and a modified Chi Squared test that handles ambiguity

hwetests

The Hardy-Weinberg Equilibrium (HWE) assumption is essential to many population genetics models, which assumes that allele pairs from a given population are random. An HWE test needs to test whether the pairing are random or not.

Our python package contains three statistical tests for HWE testing:

• ASTA
• UMAT
• UMAT with sampling

Both ASTA and UMAT with sampling assume ambiguity in the observations while UMAT does not.

Table of Contents

• Installation

• Quick tour

• Examples

• Credits

• License

Installation

Use the package manager pip to install hwetests.

pip install hwetests

Quick tour

To immediately use our package, you only need to run a single function.

ASTA

def full_algorithm(file_path,  
                   is_first_row_contains_columns_names=False,  
                   cutoff_value=0.0,  
                   should_save_csv=False,  
                   should_save_plot=False,  
                   title=''):

Suppose

Examples

Here we show how to use our package with simulated data. You can find the scripts and the simulated data in:

├───src
│   ├───test
│   │   ├───data
│   │   │   └───unambiguous_data.csv # for ASTA and UMAT with sampling (contains 50k population, 20 alleles, 0.2 uncertainty, in HWE)
│   │   │   └───ambiguous_data.csv # for UMAT (contains 100k population, in HWE)
│   │   ├───scripts
│   │   │   └───asta_test.py
│   │   │   └───umat_test.py
│   │   │   └───umat_with_sampling_test.py

ASTA test

from hwetests import asta  

if __name__ == '__main__':  
    p_value, statistic, dof = asta.full_algorithm(file_path='../data/ambiguous_data.csv',  
                                                  cutoff_value=4.0)  
    print(f'p-value: {p_value}')  
    print(f'statistic: {statistic}')  
    print(f'degrees of freedom: {dof}')

UMAT test

from hwetests import umat  
import numpy as np  

if __name__ == '__main__':  
    # import data from csv file as a numpy array  
    data = np.genfromtxt('../data/unambiguous_data.csv', delimiter=',')  
    # run the test  
    p_value = umat.full_algorithm(data)  
    print(f'p-value: {p_value}')

UMAT with sampling test

from hwetests import umat_with_uncertainty  

if __name__ == '__main__':  
    p_value = umat_with_uncertainty.full_algorithm(file_path='../data/ambiguous_data.csv')

License

asasda