def test_comparing_same_matrices(self):
for method in self.methods:
obs = mantel(self.minx, self.minx, method=method)[0]
self.assertAlmostEqual(obs, 1)
obs = mantel(self.miny, self.miny, method=method)[0]
self.assertAlmostEqual(obs, 1)
def test_two_sided(self):
np.random.seed(0)
obs = mantel(self.minx,
self.minx,
method='spearman',
alternative='two-sided')
self.assertEqual(obs[0], 1)
self.assertAlmostEqual(obs[1], 0.328)
self.assertEqual(obs[2], 3)
obs = mantel(self.minx,
self.miny,
method='spearman',
alternative='two-sided')
self.assertAlmostEqual(obs[0], 0.5)
self.assertAlmostEqual(obs[1], 1.0)
self.assertEqual(obs[2], 3)
obs = mantel(self.minx,
self.minz,
method='spearman',
alternative='two-sided')
self.assertAlmostEqual(obs[0], -1)
self.assertAlmostEqual(obs[1], 0.322)
self.assertEqual(obs[2], 3)
def test_comparing_same_matrices(self):
for method in self.methods:
obs = mantel(self.minx, self.minx, method=method)[0]
self.assertAlmostEqual(obs, 1)
obs = mantel(self.miny, self.miny, method=method)[0]
self.assertAlmostEqual(obs, 1)
def test_invalid_distance_matrix(self):
# Single asymmetric, non-hollow distance matrix.
with self.assertRaises(DissimilarityMatrixError):
mantel([[1, 2], [3, 4]], [[0, 0], [0, 0]])
# Two asymmetric distance matrices.
with self.assertRaises(DistanceMatrixError):
mantel([[0, 2], [3, 0]], [[0, 1], [0, 0]])
def test_invalid_distance_matrix(self):
# Single asymmetric, non-hollow distance matrix.
with self.assertRaises(DissimilarityMatrixError):
mantel([[1, 2], [3, 4]], [[0, 0], [0, 0]])
# Two asymmetric distance matrices.
with self.assertRaises(DistanceMatrixError):
mantel([[0, 2], [3, 0]], [[0, 1], [0, 0]])
def test_one_sided_greater(self):
np.random.seed(0)
obs = mantel(self.minx, self.miny, alternative='greater')
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.324)
self.assertEqual(obs[2], 3)
obs = mantel(self.minx, self.minx, alternative='greater')
self.assert_mantel_almost_equal(obs, [1, 0.172, 3])
def test_one_sided_greater(self):
np.random.seed(0)
obs = mantel(self.minx, self.miny, alternative='greater')
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.324)
obs = mantel(self.minx, self.minx, alternative='greater')
self.assertAlmostEqual(obs[0], 1)
self.assertAlmostEqual(obs[1], 0.172)
def get_pairwise_diversity_data(pre_bioms, post_bioms, trim_lengths):
"""For each pre-post pair, gets the pairwise distance matrix of each
sequence set and does a mantel test between pre and post pariwise distance
matrices using both jaccard and bray-curtis metrics
Parameters
----------
pre_bioms: array_like of biom.Table
pre-trimmed Artifacts in descending trim length order. Should be in
same order as post_bioms
post_bioms: array_like of biom.Table
post-trimmed Artifacts in descending trim length order. Should be in
same order as pre_bioms
trim_lengths: array_like
Trim lengths in descending order, should correspond to other arguments
Returns
-------
Pandas dataframe that holds results for each pre-post mantel test
"""
print("enter get_pairwise_diversity")
np.seterr(all="raise")
if(not (len(pre_bioms) == len(post_bioms) == len(trim_lengths))):
raise ValueError("Length of 3 arguments lists should be same\n"
"pre: {}, post: {}, lengths: {}".format(len(pre_bioms),
len(post_bioms),
len(trim_lengths)))
cols = ["trim_length", "dist_type", "r", "pval", "nsamples"]
p_div = pd.DataFrame(index=range(2*len(pre_bioms)), columns=cols)
j = 0
for i in range(len(pre_bioms)):
# pairwise distance matrices
pre_biom = pre_bioms[i]
post_biom = post_bioms[i]
pre_d_j = get_pairwise_dist_mat(pre_biom, "jaccard")
post_d_j = get_pairwise_dist_mat(post_biom, "jaccard")
r, p, nsamp = mantel(pre_d_j, post_d_j)
p_div.iloc[j] = [trim_lengths[i], "jaccard", r, p, nsamp]
j += 1
pre_d_bc = get_pairwise_dist_mat(pre_biom, "braycurtis")
post_d_bc = get_pairwise_dist_mat(post_biom, "braycurtis")
print("pre_d_bc, i: {}".format(i))
print(str(pre_d_bc))
print("post_d_bc")
print(str(post_d_bc))
r, p, nsamp = mantel(pre_d_bc, post_d_bc)
print("r: {}, p: {}".format(str(r),str(p)))
p_div.iloc[j] = [trim_lengths[i], "braycurtis", r, p, nsamp]
p_div["r_sq"] = p_div["r"]**2
print("exit get_pairwise_diversity")
return p_div
def test_no_variation_spearman(self):
exp = (np.nan, np.nan, 3)
for alt in self.alternatives:
obs = mantel(self.miny, self.no_variation, method="spearman", alternative=alt)
npt.assert_equal(obs, exp)
obs = mantel(self.no_variation, self.miny, method="spearman", alternative=alt)
npt.assert_equal(obs, exp)
obs = mantel(self.no_variation, self.no_variation, method="spearman", alternative=alt)
npt.assert_equal(obs, exp)
def test_no_side_effects(self):
minx = np.asarray(self.minx, dtype='float')
miny = np.asarray(self.miny, dtype='float')
minx_copy = np.copy(minx)
miny_copy = np.copy(miny)
mantel(minx, miny)
# Make sure we haven't modified the input.
npt.assert_equal(minx, minx_copy)
npt.assert_equal(miny, miny_copy)
def test_no_side_effects(self):
minx = np.asarray(self.minx, dtype='float')
miny = np.asarray(self.miny, dtype='float')
minx_copy = np.copy(minx)
miny_copy = np.copy(miny)
mantel(minx, miny)
# Make sure we haven't modified the input.
npt.assert_equal(minx, minx_copy)
npt.assert_equal(miny, miny_copy)
def test_zero_permutations(self):
for alt in self.alternatives:
for method, exp in (("pearson", self.exp_x_vs_y), ("spearman", 0.5)):
obs = mantel(self.minx, self.miny, permutations=0, method=method, alternative=alt)
self.assertAlmostEqual(obs[0], exp)
npt.assert_equal(obs[1], np.nan)
self.assertEqual(obs[2], 3)
# swapping order of matrices should give same result
obs = mantel(self.miny, self.minx, permutations=0, method=method, alternative=alt)
self.assertAlmostEqual(obs[0], exp)
npt.assert_equal(obs[1], np.nan)
self.assertEqual(obs[2], 3)
def test_no_variation_spearman(self):
exp = (np.nan, np.nan, 3)
for alt in self.alternatives:
obs = mantel(self.miny, self.no_variation, method='spearman',
alternative=alt)
npt.assert_equal(obs, exp)
obs = mantel(self.no_variation, self.miny, method='spearman',
alternative=alt)
npt.assert_equal(obs, exp)
obs = mantel(self.no_variation, self.no_variation,
method='spearman', alternative=alt)
npt.assert_equal(obs, exp)
def test_vegan_example(self):
np.random.seed(0)
# pearson
obs = mantel(self.veg_dm_vegan, self.env_dm_vegan, alternative="greater")
self.assertAlmostEqual(obs[0], 0.3047454)
self.assertAlmostEqual(obs[1], 0.002)
self.assertEqual(obs[2], 24)
# spearman
obs = mantel(self.veg_dm_vegan, self.env_dm_vegan, alternative="greater", method="spearman")
self.assertAlmostEqual(obs[0], 0.283791)
self.assertAlmostEqual(obs[1], 0.003)
self.assertEqual(obs[2], 24)
def test_distance_matrix_instances_with_reordering_and_nonmatching(self):
x = self.minx_dm_extra.filter(['1', '0', 'foo', '2'])
y = self.miny_dm.filter(['0', '2', '1'])
# strict=True should disallow IDs that aren't found in both matrices
with self.assertRaises(ValueError):
mantel(x, y, alternative='less', strict=True)
np.random.seed(0)
# strict=False should ignore IDs that aren't found in both matrices
obs = mantel(x, y, alternative='less', strict=False)
self.assert_mantel_almost_equal(obs, [self.exp_x_vs_y, 0.843, 3])
def test_no_variation_pearson(self):
# Output doesn't match vegan::mantel with method='pearson'. Consider
# revising output and this test depending on outcome of
# https://github.com/scipy/scipy/issues/3728
for alt in self.alternatives:
# test one or both inputs having no variation in their
# distances
obs = mantel(self.miny, self.no_variation, method="pearson", alternative=alt)
npt.assert_equal(obs, (0.0, 1.0, 3))
obs = mantel(self.no_variation, self.miny, method="pearson", alternative=alt)
npt.assert_equal(obs, (0.0, 1.0, 3))
obs = mantel(self.no_variation, self.no_variation, method="pearson", alternative=alt)
npt.assert_equal(obs, (1.0, 1.0, 3))
def test_vegan_example(self):
np.random.seed(0)
# pearson
obs = mantel(self.veg_dm_vegan,
self.env_dm_vegan,
alternative='greater')
self.assert_mantel_almost_equal(obs, [0.3047454, 0.002, 24])
# spearman
obs = mantel(self.veg_dm_vegan,
self.env_dm_vegan,
alternative='greater',
method='spearman')
self.assert_mantel_almost_equal(obs, [0.283791, 0.003, 24])
def compare_clusters(args):
ref_df = pd.read_table(args['ref'], sep='\t', skipinitialspace=True, index_col=0).as_matrix()
check_symmetry(ref_df)
linkage_ref = linkage(ref_df, 'average')
c_ref, coph_dists_ref = cophenet(linkage_ref, pdist(ref_df))
outfile = open(args['output'],"w")
outfile.write("Tree_cluster\tMantel_Correlation_Coefficient\tManter_P-value\tCophenetic_Pearson\tCophenetic_P-value\n")
for i in args['all']:
fst_df = pd.read_table(i, sep='\t', skipinitialspace=True, index_col=0).as_matrix()
check_symmetry(fst_df)
mantel_coeff = 0.0
p_value_mantel = 0.0
cophenetic_pearson = 0.0
p_value_cophenetic = 0.0
n = 0
try:
mantel_coeff, p_value_mantel, n = mantel(ref_df, fst_df)
linkage_fst = linkage(fst_df, 'average')
c_fst, coph_dists_fst = cophenet(linkage_fst, pdist(fst_df))
cophenetic_pearson, p_value_cophenetic = pearsonr(coph_dists_ref, coph_dists_fst)
except Exception as e:
print("Error : %s" % str(e))
mantel_coeff = "Failed"
p_value_manel = "Failed"
cophenetic_pearson = "Failed"
p_value_cophenetic = "Failed"
outfile.write(i+"\t"+str(mantel_coeff)+"\t"+str(p_value_mantel)+"\t"+str(cophenetic_pearson)+"\t"+str(p_value_cophenetic)+"\n")
outfile.close()
def test_distance_matrix_instances_as_input(self):
# Matrices with all matching IDs in the same order.
np.random.seed(0)
obs = mantel(self.minx_dm, self.miny_dm, alternative='less')
self.assert_mantel_almost_equal(obs, [self.exp_x_vs_y, 0.843, 3])
def compare_multiple(pose_data,
method='distance',
figure_type='aligned_figures'):
""" For multi-dancer videos: Get the mean and standard deviation of inter-pose
similarities for each frame
"""
frame_means = []
frame_stdevs = []
for f, frame in enumerate(pose_data):
print("Processing frame", f, "of", len(pose_data))
frame_similarities = []
for i, figure_i in enumerate(frame[figure_type]):
for j, figure_j in enumerate(frame[figure_type]):
if i < j:
if method == 'distance':
mi = get_pose_matrix(frame, i)
mj = get_pose_matrix(frame, j)
if mi is None or mj is None:
similarity = np.nan
else:
similarity = mantel(mi, mj)[0]
else: # method == 'laplacian'
mi = get_laplacian_matrix(frame, i)
mj = get_laplacian_matrix(frame, j)
if mi is None or mj is None:
similarity = np.nan
else:
similarity = 1 - abs(
np.subtract(mi.todense(), mj.todense()).sum())
frame_similarities.append(similarity)
frame_means.append(np.nanmean(frame_similarities))
frame_stdevs.append(np.nanstd(frame_similarities))
return [frame_means, frame_stdevs]
def test_distance_matrix_instances_with_reordering_and_nonmatching(self):
x = self.minx_dm_extra.filter(['1', '0', 'foo', '2'])
y = self.miny_dm.filter(['0', '2', '1'])
# strict=True should disallow IDs that aren't found in both matrices
with self.assertRaises(ValueError):
mantel(x, y, alternative='less', strict=True)
np.random.seed(0)
# strict=False should ignore IDs that aren't found in both matrices
obs = mantel(x, y, alternative='less', strict=False)
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.843)
self.assertEqual(obs[2], 3)
def test_vegan_example(self):
np.random.seed(0)
# pearson
obs = mantel(self.veg_dm_vegan, self.env_dm_vegan,
alternative='greater')
self.assertAlmostEqual(obs[0], 0.3047454)
self.assertAlmostEqual(obs[1], 0.002)
self.assertEqual(obs[2], 24)
# spearman
obs = mantel(self.veg_dm_vegan, self.env_dm_vegan,
alternative='greater', method='spearman')
self.assertAlmostEqual(obs[0], 0.283791)
self.assertAlmostEqual(obs[1], 0.003)
self.assertEqual(obs[2], 24)
def calc_dist_mat_corr(sim_mat, distance_dict):
"""Calculate correlation of different distance matrices with similarity matrix."""
dist_mat = create_symm_dist_mat(sim_mat)
for feature_type in FEAT_LIST:
coeff, p_value, n = mantel(dist_mat, distance_dict[feature_type])
print("Feature type: %s Coeff: %.3f" % (feature_type, coeff))
def test_zero_permutations(self):
for alt in self.alternatives:
for method, exp in (('pearson', self.exp_x_vs_y),
('spearman', 0.5)):
obs = mantel(self.minx, self.miny, permutations=0,
method=method, alternative=alt)
self.assertAlmostEqual(obs[0], exp)
npt.assert_equal(obs[1], np.nan)
self.assertEqual(obs[2], 3)
# swapping order of matrices should give same result
obs = mantel(self.miny, self.minx, permutations=0,
method=method, alternative=alt)
self.assertAlmostEqual(obs[0], exp)
npt.assert_equal(obs[1], np.nan)
self.assertEqual(obs[2], 3)
def test_hommola_vs_mantel(self):
# we don't compare p-values because the two methods use different
# permutation strategies
r_mantel, p_mantel, _ = mantel(self.hdist, self.pdist, method="pearson", permutations=0, alternative="greater")
r_hommola, p_hommola, _ = hommola_cospeciation(self.hdist, self.pdist, self.interact_1to1, permutations=0)
self.assertAlmostEqual(r_hommola, r_mantel)
npt.assert_equal(p_hommola, p_mantel)
def test_two_sided(self):
np.random.seed(0)
obs = mantel(self.minx, self.minx, method="spearman", alternative="two-sided")
self.assertEqual(obs[0], 1)
self.assertAlmostEqual(obs[1], 0.328)
self.assertEqual(obs[2], 3)
obs = mantel(self.minx, self.miny, method="spearman", alternative="two-sided")
self.assertAlmostEqual(obs[0], 0.5)
self.assertAlmostEqual(obs[1], 1.0)
self.assertEqual(obs[2], 3)
obs = mantel(self.minx, self.minz, method="spearman", alternative="two-sided")
self.assertAlmostEqual(obs[0], -1)
self.assertAlmostEqual(obs[1], 0.322)
self.assertEqual(obs[2], 3)
def calc_corr(sim_mat1, sim_mat2):
"""Calculate correlation between symmetric and non-symmetric matrices."""
non_symm_corr = pearsonr(get_non_diagonal_entries(sim_mat1),
get_non_diagonal_entries(sim_mat2))[0]
symm_corr = mantel(create_symm_dist_mat(sim_mat1),
create_symm_dist_mat(sim_mat2))[0]
print ("Correlation between non-diagonal entries: %.3f" %non_symm_corr)
print ("Mantel correlation: %.3f" %symm_corr)
def test_one_sided_less(self):
# no need to seed here as permuted test statistics will all be less
# than or equal to the observed test statistic (1.0)
for method in self.methods:
obs = mantel(self.minx, self.minx, method=method,
alternative='less')
self.assertEqual(obs, (1, 1))
np.random.seed(0)
obs = mantel(self.minx, self.miny, alternative='less')
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.843)
obs = mantel(self.minx, self.minz, alternative='less')
self.assertAlmostEqual(obs[0], self.exp_x_vs_z)
self.assertAlmostEqual(obs[1], 0.172)
def test_one_sided_less(self):
# no need to seed here as permuted test statistics will all be less
# than or equal to the observed test statistic (1.0)
for method in self.methods:
obs = mantel(self.minx,
self.minx,
method=method,
alternative='less')
npt.assert_almost_equal(obs, (1, 1, 3))
np.random.seed(0)
obs = mantel(self.minx, self.miny, alternative='less')
self.assert_mantel_almost_equal(obs, [self.exp_x_vs_y, 0.843, 3])
obs = mantel(self.minx, self.minz, alternative='less')
self.assert_mantel_almost_equal(obs, [self.exp_x_vs_z, 0.172, 3])
def compare_tip_to_tip_distances(tree_fh1, tree_fh2, method="pearson"):
tree1 = TreeNode.read(tree_fh1)
tree2 = TreeNode.read(tree_fh2)
dm1 = tree1.tip_tip_distances()
dm2 = tree2.tip_tip_distances()
return mantel(dm1, dm2, strict=False, method=method)
def test_statistic_same_across_alternatives_and_permutations(self):
# Varying permutations and alternative hypotheses shouldn't affect the
# computed test statistics.
for n in (0, 99, 999):
for alt in self.alternatives:
for method, exp in (("pearson", self.exp_x_vs_y), ("spearman", 0.5)):
obs = mantel(self.minx, self.miny, method=method, permutations=n, alternative=alt)[0]
self.assertAlmostEqual(obs, exp)
def diversity_analysis(wu_dm_list,bc_dm_list):
from skbio.stats.distance import mantel
#do the UniFrac and Bray-Curtis distances correlate?
r, p_value, n = mantel(wu_dm_list[0],bc_dm_list[0])
print("Mantel Correlation COEF=",r)
print("At significance of 0.05, the p-value for the correlation is = ",p_value)
#next perform principle coordinate analysis (PCoA) on the weighted UniFrac distance matrix:
from skbio.stats.ordination import pcoa
wu_pc = pcoa(wu_dm_list[0])
def test_no_variation_pearson(self):
# Output doesn't match vegan::mantel with method='pearson'. Consider
# revising output and this test depending on outcome of
# https://github.com/scipy/scipy/issues/3728
for alt in self.alternatives:
# test one or both inputs having no variation in their
# distances
obs = mantel(self.miny, self.no_variation, method='pearson',
alternative=alt)
npt.assert_equal(obs, (0.0, 1.0, 3))
obs = mantel(self.no_variation, self.miny, method='pearson',
alternative=alt)
npt.assert_equal(obs, (0.0, 1.0, 3))
obs = mantel(self.no_variation, self.no_variation,
method='pearson', alternative=alt)
npt.assert_equal(obs, (1.0, 1.0, 3))
def find_nearest_pose(pose_matrix, cluster_averages):
best_corr = 0
best_label = -1
for label in cluster_averages:
corr = mantel(pose_matrix, cluster_averages[label])[0]
if corr > best_corr:
best_label = label
best_corr = corr
return best_label
def test_distance_matrix_instances_as_input(self):
# Matrices with all matching IDs in the same order.
np.random.seed(0)
obs = mantel(self.minx_dm, self.miny_dm, alternative='less')
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.843)
self.assertEqual(obs[2], 3)
def test_distance_matrix_instances_as_input(self):
# Matrices with all matching IDs in the same order.
np.random.seed(0)
obs = mantel(self.minx_dm, self.miny_dm, alternative='less')
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.843)
self.assertEqual(obs[2], 3)
def test_one_sided_less(self):
# no need to seed here as permuted test statistics will all be less
# than or equal to the observed test statistic (1.0)
for method in self.methods:
obs = mantel(self.minx, self.minx, method=method,
alternative='less')
self.assertEqual(obs, (1, 1, 3))
np.random.seed(0)
obs = mantel(self.minx, self.miny, alternative='less')
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.843)
self.assertEqual(obs[2], 3)
obs = mantel(self.minx, self.minz, alternative='less')
self.assertAlmostEqual(obs[0], self.exp_x_vs_z)
self.assertAlmostEqual(obs[1], 0.172)
self.assertEqual(obs[2], 3)
def test_statistic_same_across_alternatives_and_permutations(self):
# Varying permutations and alternative hypotheses shouldn't affect the
# computed test statistics.
for n in (0, 99, 999):
for alt in self.alternatives:
for method, exp in (('pearson', self.exp_x_vs_y),
('spearman', 0.5)):
obs = mantel(self.minx, self.miny, method=method,
permutations=n, alternative=alt)[0]
self.assertAlmostEqual(obs, exp)
def test_distance_matrix_instances_with_lookup(self):
self.minx_dm.ids = ("a", "b", "c")
self.miny_dm.ids = ("d", "e", "f")
lookup = {"a": "A", "b": "B", "c": "C", "d": "A", "e": "B", "f": "C"}
np.random.seed(0)
obs = mantel(self.minx_dm, self.miny_dm, alternative="less", lookup=lookup)
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.843)
self.assertEqual(obs[2], 3)
def test_two_sided(self):
np.random.seed(0)
obs = mantel(self.minx,
self.minx,
method='spearman',
alternative='two-sided')
self.assert_mantel_almost_equal(obs, [1.0, 0.328, 3])
obs = mantel(self.minx,
self.miny,
method='spearman',
alternative='two-sided')
self.assert_mantel_almost_equal(obs, [0.5, 1.0, 3])
obs = mantel(self.minx,
self.minz,
method='spearman',
alternative='two-sided')
self.assert_mantel_almost_equal(obs, [-1, 0.322, 3])
def test_invalid_input(self):
# invalid correlation method
with self.assertRaises(ValueError):
mantel([[1]], [[1]], method='brofist')
# invalid permutations
with self.assertRaises(ValueError):
mantel([[1]], [[1]], permutations=-1)
# invalid alternative
with self.assertRaises(ValueError):
mantel([[1]], [[1]], alternative='no cog yay')
# mismatched shape
with self.assertRaises(ValueError):
mantel(self.minx, [[0, 2], [2, 0]])
# too small dms
with self.assertRaises(ValueError):
mantel([[0, 3], [3, 0]], [[0, 2], [2, 0]])
def test_distance_matrix_instances_as_input(self):
# IDs shouldn't matter -- the function should only care about the
# matrix data
dmx = DistanceMatrix(self.minx)
dmy = DistanceMatrix(self.miny, ['no', 'cog', 'yay'])
np.random.seed(0)
obs = mantel(dmx, dmy, alternative='less')
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.843)
def test_no_variation_pearson(self):
for alt in self.alternatives:
# test one or both inputs having no variation in their
# distances
obs = mantel(self.miny,
self.no_variation,
method='pearson',
alternative=alt)
npt.assert_equal(obs, (np.nan, np.nan, 3))
obs = mantel(self.no_variation,
self.miny,
method='pearson',
alternative=alt)
npt.assert_equal(obs, (np.nan, np.nan, 3))
obs = mantel(self.no_variation,
self.no_variation,
method='pearson',
alternative=alt)
npt.assert_equal(obs, (np.nan, np.nan, 3))
def test_distance_matrix_instances_with_lookup(self):
self.minx_dm.ids = ('a', 'b', 'c')
self.miny_dm.ids = ('d', 'e', 'f')
lookup = {'a': 'A', 'b': 'B', 'c': 'C', 'd': 'A', 'e': 'B', 'f': 'C'}
np.random.seed(0)
obs = mantel(self.minx_dm,
self.miny_dm,
alternative='less',
lookup=lookup)
self.assert_mantel_almost_equal(obs, [self.exp_x_vs_y, 0.843, 3])
def test_distance_matrix_instances_with_lookup(self):
self.minx_dm.ids = ('a', 'b', 'c')
self.miny_dm.ids = ('d', 'e', 'f')
lookup = {'a': 'A', 'b': 'B', 'c': 'C',
'd': 'A', 'e': 'B', 'f': 'C'}
np.random.seed(0)
obs = mantel(self.minx_dm, self.miny_dm, alternative='less',
lookup=lookup)
self.assertAlmostEqual(obs[0], self.exp_x_vs_y)
self.assertAlmostEqual(obs[1], 0.843)
self.assertEqual(obs[2], 3)
def test_invalid_input(self):
# invalid correlation method
with self.assertRaises(ValueError):
mantel([[1]], [[1]], method="brofist")
# invalid permutations
with self.assertRaises(ValueError):
mantel([[1]], [[1]], permutations=-1)
# invalid alternative
with self.assertRaises(ValueError):
mantel([[1]], [[1]], alternative="no cog yay")
# too small dms
with self.assertRaises(ValueError):
mantel([[0, 3], [3, 0]], [[0, 2], [2, 0]])
def test_invalid_input(self):
# invalid correlation method
with self.assertRaises(ValueError):
mantel([[1]], [[1]], method='brofist')
# invalid permutations
with self.assertRaises(ValueError):
mantel([[1]], [[1]], permutations=-1)
# invalid alternative
with self.assertRaises(ValueError):
mantel([[1]], [[1]], alternative='no cog yay')
# too small dms
with self.assertRaises(ValueError):
mantel([[0, 3], [3, 0]], [[0, 2], [2, 0]])
def test_hommola_vs_mantel(self):
# we don't compare p-values because the two methods use different
# permutation strategies
r_mantel, p_mantel, _ = mantel(self.hdist,
self.pdist,
method='pearson',
permutations=0,
alternative='greater')
r_hommola, p_hommola, _ = hommola_cospeciation(self.hdist,
self.pdist,
self.interact_1to1,
permutations=0)
self.assertAlmostEqual(r_hommola, r_mantel)
npt.assert_equal(p_hommola, p_mantel)
def compare_trees(tree_fp1, tree_fp2, method):
itstree = TreeNode.read(tree_fp1)
hybridtree = TreeNode.read(tree_fp2)
itstreedm = itstree.tip_tip_distances()
hybridtreedm = hybridtree.tip_tip_distances()
coeff, p_value, n = mantel(itstreedm,
hybridtreedm,
strict=False,
method=method)
click.echo("Correlation coefficient: %f" % coeff)
click.echo("P-value: %f" % p_value)
click.echo("Number of overlapping tips: %d" % n)
def compute_mantel(result_tables,
taxonomy_level=6,
random_trials=999):
""" Compute mantel r and p-values for a set of results
result_tables: 2d list of tables to be compared,
where the data in the inner list is:
[dataset_id, reference_database_id, method_id,
parameter_combination_id, table_fp]
taxonomy_level: level to compute results
random_trials : number of Monte Carlo trials to run in Mantel test
"""
collapse_by_taxonomy = get_taxonomy_collapser(taxonomy_level)
results = []
for dataset_id, reference_id, method_id, params, actual_table_fp in result_tables:
## load the table and collapse it at the specified taxonomic level
try:
full_table = load_table(actual_table_fp)
except ValueError:
raise ValueError("Couldn't parse BIOM table: %s" % actual_table_fp)
collapsed_table = full_table.collapse(collapse_by_taxonomy,
axis='observation',
min_group_size=1)
## Compute Bray-Curtis distances between samples in the full table and
## in the collapsed table, and compare them with Mantel.
# This is way too compute-intensive because we're computing the actual
# dm everytime, which doesn't need to happen.
collapsed_dm = distance_matrix_from_table(collapsed_table)
full_dm = distance_matrix_from_table(full_table)
mantel_r, p = mantel(collapsed_dm, full_dm)
results.append((dataset_id, reference_id, method_id, params, mantel_r, p))
return pd.DataFrame(results, columns=["Dataset", "Reference", "Method",
"Parameters", "Mantel r", "Mantel p"])
def run_mantel_test(method, fps, distmats, num_perms, tail_type, comment,
control_dm_fp=None, control_dm=None,
sample_id_map=None):
"""Runs a Mantel test on all pairs of distance matrices.
Returns a string suitable for writing out to a file containing the results
of the test.
WARNING: Only symmetric, hollow distance matrices may be used as input.
Asymmetric distance matrices, such as those obtained by the UniFrac Gain
metric (i.e. beta_diversity.py -m unifrac_g), should not be used as input.
Arguments:
method - which Mantel test to run (either 'mantel' or 'partial_mantel')
fps - list of filepaths of the distance matrices
distmats - list of tuples containing dm labels and dm data (i.e. the
output of parse_distmat)
num_perms - the number of permutations to use to calculate the
p-value(s)
tail_type - the type of tail test to use when calculating the
p-value(s). Can be 'two-sided', 'greater', or 'less'. Only applies
when method is mantel
comment - comment string to add to the beginning of the results string
control_dm_fp - filepath of the control distance matrix. Only applies
when method is partial_mantel (it is required then)
control_dm - tuple containing control distance matrix labels and matrix
data. Only applies when method is partial_mantel (it is required
then)
sample_id_map - dict mapping sample IDs (i.e. what is expected by
make_compatible_distance_matrices)
"""
if len(fps) != len(distmats):
raise ValueError("Must provide the same number of filepaths as there "
"are distance matrices.")
if comment is None:
comment = ''
result = comment
if method == 'mantel':
result += 'DM1\tDM2\tNumber of entries\tMantel r statistic\t' + \
'p-value\tNumber of permutations\tTail type\n'
elif method == 'partial_mantel':
if not control_dm_fp or not control_dm:
raise ValueError("You must provide a control matrix filepath and "
"control matrix when running the partial Mantel "
"test.")
result += 'DM1\tDM2\tCDM\tNumber of entries\t' + \
'Mantel r statistic\tp-value\tNumber of permutations\t' +\
'Tail type\n'
else:
raise ValueError("Invalid method '%s'. Must be either 'mantel' or "
"'partial_mantel'." % method)
# Loop over all pairs of dms.
for i, (fp1, (dm1_labels, dm1_data)) in enumerate(zip(fps, distmats)):
for fp2, (dm2_labels, dm2_data) in zip(fps, distmats)[i + 1:]:
# Make the current pair of distance matrices compatible by only
# keeping samples that match between them, and ordering them by
# the same sample IDs.
(dm1_labels, dm1_data), (dm2_labels, dm2_data) = \
make_compatible_distance_matrices((dm1_labels, dm1_data),
(dm2_labels, dm2_data), lookup=sample_id_map)
if method == 'partial_mantel':
# We need to intersect three sets (three matrices).
(dm1_labels, dm1_data), (cdm_labels, cdm_data) = \
make_compatible_distance_matrices(
(dm1_labels, dm1_data), control_dm,
lookup=sample_id_map)
(dm1_labels, dm1_data), (dm2_labels, dm2_data) = \
make_compatible_distance_matrices(
(dm1_labels, dm1_data), (dm2_labels, dm2_data),
lookup=sample_id_map)
if len(dm1_labels) < 3:
result += '%s\t%s\t%s\t%d\tToo few samples\n' % (fp1,
fp2, control_dm_fp, len(dm1_labels))
continue
elif len(dm1_labels) < 3:
result += '%s\t%s\t%d\tToo few samples\n' % (fp1, fp2,
len(dm1_labels))
continue
dm1 = DistanceMatrix(dm1_data, dm1_labels)
dm2 = DistanceMatrix(dm2_data, dm2_labels)
if method == 'mantel':
corr_coeff, p_value, n = mantel(dm1, dm2, method='pearson',
permutations=num_perms, alternative=tail_type,
strict=True)
p_str = p_value_to_str(p_value, num_perms)
result += "%s\t%s\t%d\t%.5f\t%s\t%d\t%s\n" % (
fp1, fp2, n, corr_coeff, p_str, num_perms, tail_type)
elif method == 'partial_mantel':
cdm = DistanceMatrix(cdm_data, cdm_labels)
results = PartialMantel(dm1, dm2, cdm)(num_perms)
p_str = p_value_to_str(results['mantel_p'], num_perms)
result += "%s\t%s\t%s\t%d\t%.5f\t%s\t%d\t%s\n" % (
fp1, fp2, control_dm_fp, len(dm1_labels),
results['mantel_r'], p_str, num_perms, 'greater')
return result
def test_negative_correlation(self):
for method, exp in (('pearson', self.exp_x_vs_z), ('spearman', -1)):
obs = mantel(self.minx, self.minz, method=method)[0]
self.assertAlmostEqual(obs, exp)