def __run_iteration__rows(title, desc, data, tree, samples, otus, rows_filter, cols_filter, table):
rows_dist, _ = ctwc__distance_matrix.get_distance_matrices(data,
tree,
samples,
otus,
sample_filter=cols_filter,
otu_filter=rows_filter,
skip_cols=True)
ctwc__plot.plot_mat(rows_dist, header="{0}: {1}".format(title, "OTUs Distance Matrix"))
picked_indices, last_rank, _, _, _, _ = ctwc__cluster_rank.filter_rows_by_top_rank(data,
rows_dist,
otus)
selected_rows_filter, compliment_rows_filter = __prepare_otu_filters_from_indices(picked_indices, otus, rows_filter)
sorted_rows_mat = __sort_matrix_rows_by_selection(rows_dist, picked_indices)
sorted_mat = __sort_matrix_cols_by_selection(sorted_rows_mat, picked_indices)
ctwc__plot.plot_mat(sorted_mat, header="{0}: {1}".format(title, "OTUs Distance Matrix - sorted"))
if table is not None:
picked_otus = ctwc__data_handler.get_otus_by_indices(picked_indices, table)
taxonomies = ctwc__data_handler.get_taxonomies_for_otus(picked_otus)
INFO("Picked OTUs:")
for taxonomy in taxonomies:
INFO(taxonomy)
num_otus = len(picked_indices)
num_samples = len(samples) - len(cols_filter)
return (num_otus, num_samples), selected_rows_filter, compliment_rows_filter
def test_otu_distance_matrix(samples, otus, tree, data, table):
INFO("Test OTU distance matrix and filtering...")
data[::4, :] = 50
indices = range(36)
otu_filter_1, _ = ctwc.__prepare_otu_filters_from_indices(indices,
otus,
[])
indices = range(36, 96)
otu_filter, _ = ctwc.__prepare_otu_filters_from_indices(indices,
otus,
otu_filter_1)
rows_dist, _ = ctwc__distance_matrix.get_distance_matrices(data,
tree,
samples,
otus,
otu_filter=otu_filter,
sample_filter=[],
skip_cols=True)
INFO("Test filtering...")
for row_ind, row in enumerate(rows_dist):
count = len( [ cell for cell in row if cell == ctwc__distance_matrix.INF_VALUE ] )
if row_ind < 96:
ASSERT(count == data.shape[0] - 1)
else:
ASSERT(count == 96)
INFO("Passed OTU distance matrix and filtering tests")
def test_sample_distance_matrix(samples, otus, tree, data, table):
INFO("Test sample distance matrix and filtering...")
data[:,::15] = 50
indices = range(0, 36)
samp_filter_1, _ = ctwc.__prepare_sample_filters_from_indices(indices,
samples,
[])
indices = range(36, 96)
sample_filter, _ = ctwc.__prepare_sample_filters_from_indices(indices,
samples,
samp_filter_1)
_, cols_dist = ctwc__distance_matrix.get_distance_matrices(data,
tree,
samples,
otus,
otu_filter=[],
sample_filter=sample_filter,
skip_rows=True)
INFO("Test filtering...")
for row_ind, row in enumerate(cols_dist):
count = len( [ cell for cell in row if cell == ctwc__distance_matrix.INF_VALUE ] )
if row_ind < 96:
ASSERT(count == data.shape[1] - 1)
else:
ASSERT(count == 96)
def test():
data, otus, samples = ctwc__data_handler.get_sample_biom_table()
tree = ctwc__data_handler.get_gg_97_otu_tree()
_, cols_dist = ctwc__distance_matrix.get_distance_matrices(data, tree, samples, otus, skip_rows=True)
picked_indices, max_rank, filtered_data, filtered_dist_matrix, _ , _ = filter_cols_by_top_rank(data, cols_dist, otus, True)
INFO("Picked {0} indices".format(len(picked_indices)))
clust, labels, ag = ctwc__cluster_1d.cluster_rows(filtered_data.transpose(), cols_dist)
def __run_iteration__cols(title, desc, data, tree, samples, otus, rows_filter, cols_filter, table):
_, cols_dist = ctwc__distance_matrix.get_distance_matrices(data,
tree,
samples,
otus,
otu_filter=rows_filter,
sample_filter=cols_filter,
skip_rows=True)
ctwc__plot.plot_mat(cols_dist, header="{0}: {1}".format(title, "Samples Distance Matrix"))
picked_indices, last_rank, _, _, _, _ = ctwc__cluster_rank.filter_cols_by_top_rank(data,
cols_dist,
samples)
selected_cols_filter, compliment_cols_filter = __prepare_sample_filters_from_indices(picked_indices, samples, cols_filter)
sorted_rows_mat = __sort_matrix_rows_by_selection(cols_dist, picked_indices)
sorted_mat = __sort_matrix_cols_by_selection(sorted_rows_mat, picked_indices)
ctwc__plot.plot_mat(sorted_mat, header="{0}: {1}".format(title, "Samples Distance Matrix - sorted"))
INFO("Selected {0} samples:".format(len(picked_indices)))
DEBUG(picked_indices)
if table is not None:
picked_samples = ctwc__data_handler.get_samples_by_indices(picked_indices, table)
DEBUG(picked_samples)
dates = ctwc__data_handler.get_collection_dates_for_samples(picked_samples)
INFO("Collection dates for selected samples:")
for row in dates:
INFO(row)
num_otus = len(otus) - len(rows_filter)
num_samples = len(picked_indices)
return (num_otus, num_samples), selected_cols_filter, compliment_cols_filter
