def test_confirm_betadispr_results(self):
mp_dm = DistanceMatrix.read(get_data_path('moving_pictures_dm.tsv'))
mp_mf = pd.read_csv(get_data_path('moving_pictures_mf.tsv'), sep='\t')
mp_mf.set_index('#SampleID', inplace=True)
obs_med_mp = permdisp(mp_dm, mp_mf,
column='BodySite')
obs_cen_mp = permdisp(mp_dm, mp_mf, column='BodySite',
test='centroid')
exp_data_m = ['PERMDISP', 'F-value', 33, 4, 10.1956, 0.001, 999]
exp_data_c = ['PERMDISP', 'F-value', 33, 4, 17.4242, 0.001, 999]
exp_ind = ['method name', 'test statistic name', 'sample size',
'number of groups', 'test statistic', 'p-value',
'number of permutations']
exp_med_mp = pd.Series(data=exp_data_m, index=exp_ind, dtype='object',
name='PERMDISP results')
exp_cen_mp = pd.Series(data=exp_data_c, index=exp_ind, dtype='object',
name='PERMDISP results')
self.assert_series_equal(exp_med_mp, obs_med_mp)
self.assert_series_equal(exp_cen_mp, obs_cen_mp)
def test_confirm_betadispr_results(self):
mp_dm = DistanceMatrix.read(get_data_path('moving_pictures_dm.tsv'))
mp_mf = pd.read_csv(get_data_path('moving_pictures_mf.tsv'), sep='\t')
mp_mf.set_index('#SampleID', inplace=True)
obs_med_mp = permdisp(mp_dm, mp_mf,
column='BodySite')
obs_cen_mp = permdisp(mp_dm, mp_mf, column='BodySite',
test='centroid')
exp_data_m = ['PERMDISP', 'F-value', 33, 4, 10.1956, 0.001, 999]
exp_data_c = ['PERMDISP', 'F-value', 33, 4, 17.4242, 0.001, 999]
exp_ind = ['method name', 'test statistic name', 'sample size',
'number of groups', 'test statistic', 'p-value',
'number of permutations']
exp_med_mp = pd.Series(data=exp_data_m, index=exp_ind, dtype='object',
name='PERMDISP results')
exp_cen_mp = pd.Series(data=exp_data_c, index=exp_ind, dtype='object',
name='PERMDISP results')
self.assert_series_equal(exp_med_mp, obs_med_mp)
self.assert_series_equal(exp_cen_mp, obs_cen_mp)
def permanova_permdisp(self):
# compute the permanova
print('running permdisp\n\n')
print(permdisp(distance_matrix=DistanceMatrix(self.dist_df),
grouping=[_.split('_')[0] for _ in list(self.dist_df)], permutations=999))
print('running permanova\n\n')
print(permanova(distance_matrix=DistanceMatrix(self.dist_df),
grouping=[_.split('_')[0] for _ in list(self.dist_df)], permutations=9999))
def test_median_fsvd(self):
exp = pd.Series(index=self.exp_index,
data=['PERMDISP', 'F-value', 9, 2, 0.04078077215673714,
0.8, 99],
name='PERMDISP results')
np.random.seed(0)
obs = permdisp(self.unifrac_dm, self.unif_grouping, test='median',
permutations=99,
method='fsvd', number_of_dimensions=3)
self.assert_series_equal(obs, exp)
np.random.seed(0)
po = pcoa(self.unifrac_dm, method='fsvd', number_of_dimensions=3)
obs = permdisp(po, self.unif_grouping, test='median',
permutations=99)
self.assert_series_equal(obs, exp)
def test_median_normal(self):
exp = pd.Series(index=self.exp_index,
data=['PERMDISP', 'F-value', 9, 2, 0.139475441876,
0.61, 99],
name='PERMDISP results')
np.random.seed(0)
obs = permdisp(self.unifrac_dm, self.unif_grouping, test='median',
permutations=99)
self.assert_series_equal(obs, exp)
np.random.seed(0)
po = pcoa(self.unifrac_dm)
obs2 = permdisp(po, self.unif_grouping, test='median',
permutations=99)
self.assert_series_equal(obs2, exp)
def test_median_normal(self):
exp = pd.Series(index=self.exp_index,
data=['PERMDISP', 'F-value', 9, 2, 0.139475441876,
0.61, 99],
name='PERMDISP results')
np.random.seed(0)
obs = permdisp(self.unifrac_dm, self.unif_grouping, test='median',
permutations=99)
self.assert_series_equal(obs, exp)
def test_centroid_normal(self):
exp = pd.Series(index=self.exp_index,
data=['PERMDISP', 'F-value', 9, 2, 0.244501519876,
0.63, 99],
name='PERMDISP results')
grouping = ['Control', 'Control', 'Control', 'Control', 'Control',
'Fast', 'Fast', 'Fast', 'Fast']
np.random.seed(0)
obs = permdisp(self.unifrac_dm, grouping, test='centroid',
permutations=99)
self.assert_series_equal(obs, exp)
def test_centroid_normal(self):
exp = pd.Series(index=self.exp_index,
data=['PERMDISP', 'F-value', 9, 2, 0.244501519876,
0.63, 99],
name='PERMDISP results')
grouping = ['Control', 'Control', 'Control', 'Control', 'Control',
'Fast', 'Fast', 'Fast', 'Fast']
np.random.seed(0)
obs = permdisp(self.unifrac_dm, grouping, test='centroid',
permutations=99)
self.assert_series_equal(obs, exp)
def test_no_permuations(self):
obs = permdisp(self.eq_mat, self.grouping_eq, permutations=0)
pval = obs['p-value']
np.isnan(pval)
for a in range(len(rows[0])):
if a > 0:
this_sample = []
for b in range(len(rows)):
if b > 0:
this_sample.append(float(rows[b][a]))
samples.append(this_sample)
"""
only_samples = ['LR', 'SR']
new_samples, new_names = [], []
for a in range(len(sample_names)):
for b in range(len(only_samples)):
if sample_names[a] == only_samples[b]:
new_samples.append(samples[a])
new_names.append(sample_names[a])
samples = new_samples
sample_names = new_names
print(len(samples), len(sample_names))
"""
sam_dm = dm.from_iterable(samples, metric=braycurtis)
pdisp = permdisp(sam_dm,
sample_names,
column=None,
test='median',
permutations=999)
print(pdisp)
asim = anosim(sam_dm, sample_names, column=None, permutations=999)
print(asim)
perm = permanova(sam_dm, sample_names, column=None, permutations=999)
print(perm)
def test_no_permuations(self):
obs = permdisp(self.eq_mat, self.grouping_eq, permutations=0)
pval = obs['p-value']
np.isnan(pval)
