Python scipy.stats 模块，binom_test() 实例源码

def burden_test(count, pred, offset, test_method, model, s):
    """ Perform burden test.

    Args:
        count:
        pred:
        offset:
        test_method:
        model:
        s:
        use_gmean:

    Returns:

    """
    if test_method == 'auto':
        test_method = 'binomial' if model['pval_dispersion'] > 0.05 else 'negative_binomial'
    if test_method == 'negative_binomial':
        logger.info('Using negative binomial test with s={}, theta={}'.format(s, model['theta']))
        theta = s * model['theta']
        pvals = np.array([negbinom_test(x, mu, theta, o)
                          for x, mu, o in zip(count, pred, offset)])
    elif test_method == 'binomial':
        logger.info('Using binomial test')
        pvals = np.array([binom_test(x, n, p, 'greater')
                          for x, n, p in zip(count, offset,
                                             pred/offset)])
    else:
        logger.error('Unknown test method: {}. Please use binomial, negative_binomial or auto'.format(test_method))
        sys.exit(1)
    return pvals

def pre(self, df):
        if self.variable not in df:
            df.loc[:, self.variable] = df.apply(lambda row: binom_test(np.round(row['s1_area_fraction_top'] * row['s1']),
                                                                       np.round(row['s1']),
                                                                       self.aft_map(np.sqrt(row['x']**2 + row['y']**2),
                                                                                    row['z'])[0, 0]),
                                                axis=1)
        return df

def binomial_test(mut_on_kmer, mut_on_gene, e):
    '''
    performs binomial tests for mutations on each kmer, returns pvalue
    '''
    pval = binom_test(mut_on_kmer, mut_on_gene, e)
    return(pval)