def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
otu_table = parse_biom_table(open(opts.otu_table_fp, 'U'))
lineages = []
if (otu_table.ObservationMetadata is None
or 'taxonomy' not in otu_table.ObservationMetadata[0]):
print '\n\nWarning: The lineages are missing from the OTU table. If you used single_rarefaction.py to create your otu_table, make sure you included the OTU lineages.\n'
lineages = [''] * len(otu_table.ObservationIds)
else:
for val, id, meta in otu_table.iterObservations():
lineages.append([v for v in meta['taxonomy']])
otu_labels = make_otu_labels(otu_table.ObservationIds, lineages)
# Convert to relative abundance if requested
if not opts.absolute_abundance:
otu_table = otu_table.normObservationBySample()
# Get log transform if requested
if not opts.no_log_transform:
if not opts.log_eps is None and opts.log_eps <= 0:
print "Parameter 'log_eps' must be positive. Value was", opts.log_eps
exit(1)
otu_table = get_log_transform(otu_table, opts.log_eps)
if opts.output_dir:
if os.path.exists(opts.output_dir):
dir_path = opts.output_dir
else:
try:
os.mkdir(opts.output_dir)
dir_path = opts.output_dir
except OSError:
pass
else:
dir_path = './'
# Re-order samples by tree if provided
if not opts.sample_tree is None:
sample_order = get_order_from_tree(otu_table.SampleIds,
open(opts.sample_tree, 'U'))
# if there's no sample tree, sort samples by mapping file
elif not opts.map_fname is None:
lines = open(opts.map_fname, 'U').readlines()
metadata = list(parse_mapping_file(lines))
new_map, otu_table = get_overlapping_samples(metadata[0], otu_table)
metadata[0] = new_map
map_sample_ids = zip(*metadata[0])[0]
# if there's a category, do clustering within each category
if not opts.category is None:
category_labels = \
extract_metadata_column(otu_table.SampleIds, \
metadata, opts.category)
sample_order = \
get_order_from_categories(otu_table, category_labels)
# else: just use the mapping file order
else:
ordered_sample_ids = []
for sample_id in map_sample_ids:
if sample_id in otu_table.SampleIds:
ordered_sample_ids.append(sample_id)
sample_order = names_to_indices(otu_table.SampleIds,
ordered_sample_ids)
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_column_clustering:
data = asarray([i for i in otu_table.iterObservationData()])
sample_order = get_clusters(data, axis='column')
# else just use OTU table ordering
else:
sample_order = arange(len(otu_table.SampleIds))
# re-order OTUs by tree (if provided), or clustering
if not opts.otu_tree is None:
# open tree file
try:
f = open(opts.otu_tree, 'U')
except (TypeError, IOError):
raise MissingFileError, \
"Couldn't read tree file at path: %s" % opts.otu_tree
otu_order = get_order_from_tree(otu_table.ObservationIds, f)
f.close()
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_row_clustering:
data = asarray([i for i in otu_table.iterObservationData()])
otu_order = get_clusters(data, axis='row')
# else just use OTU table ordering
else:
otu_order = arange(len(otu_table.ObservationIds))
# otu_order and sample_order should be ids, rather than indices
# to use in sortObservationOrder/sortSampleOrder
otu_id_order = [otu_table.ObservationIds[i] for i in otu_order]
sample_id_order = [otu_table.SampleIds[i] for i in sample_order]
# Re-order otu table, sampleids, etc. as necessary
otu_table = otu_table.sortObservationOrder(otu_id_order)
# otu_ids not used after: tagged for deletion
otu_ids = array(otu_table.ObservationIds)[otu_order]
otu_labels = array(otu_labels)[otu_order]
otu_table = otu_table.sortSampleOrder(sample_id_order)
sample_ids = array(otu_table.SampleIds)[sample_order]
plot_heatmap(otu_table,
otu_labels,
sample_ids,
filename=join(dir_path, 'heatmap.pdf'))
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
otu_table = load_table(opts.otu_table_fp)
obs_md_category = opts.obs_md_category
obs_md_level = opts.obs_md_level
if obs_md_level is None:
# grab the last level if the user didn't specify a level
obs_md_level = -1
else:
# convert to 0-based indexing
obs_md_level -= 1
obs_md = otu_table.metadata(axis='observation')
# create reference to the observation metadata for the first
# observation for convenient lookup
obs_md_0 = obs_md[0]
obs_md_labels = []
if (obs_md is None or obs_md_category not in obs_md_0):
obs_md_labels = [['']] * len(otu_table.ids(axis='observation'))
else:
for _, _, md in otu_table.iter(axis='observation'):
current_md = md[obs_md_category]
if obs_md_level < len(current_md):
current_md_at_level = current_md[obs_md_level]
else:
current_md_at_level = ''
obs_md_labels.append([current_md_at_level])
otu_labels = make_otu_labels(otu_table.ids(axis='observation'),
obs_md_labels)
# Convert to relative abundance if requested
if not opts.absolute_abundance:
otu_table = otu_table.norm(axis='observation')
# Get log transform if requested
if not opts.no_log_transform:
otu_table = get_log_transform(otu_table)
# Re-order samples by tree if provided
if opts.sample_tree is not None:
sample_order = get_order_from_tree(otu_table.ids(),
open(opts.sample_tree, 'U'))
# if there's no sample tree, sort samples by mapping file
elif opts.map_fname is not None:
lines = open(opts.map_fname, 'U').readlines()
metadata = list(parse_mapping_file(lines))
new_map, otu_table = get_overlapping_samples(metadata[0], otu_table)
metadata[0] = new_map
map_sample_ids = zip(*metadata[0])[0]
# if there's a category, do clustering within each category
if opts.category is not None:
category_labels = extract_metadata_column(otu_table.ids(),
metadata, opts.category)
sample_order = get_order_from_categories(otu_table,
category_labels)
# else: just use the mapping file order
else:
ordered_sample_ids = []
for sample_id in map_sample_ids:
if otu_table.exists(sample_id):
ordered_sample_ids.append(sample_id)
sample_order = names_to_indices(
otu_table.ids(),
ordered_sample_ids)
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_column_clustering:
data = np.asarray([i for i in otu_table.iter_data(axis='observation')])
sample_order = get_clusters(data, axis='column')
# else just use OTU table ordering
else:
sample_order = np.arange(len(otu_table.ids()))
# re-order OTUs by tree (if provided), or clustering
if opts.otu_tree is not None:
# open tree file
try:
f = open(opts.otu_tree, 'U')
except (TypeError, IOError):
raise MissingFileError("Couldn't read tree file at path: %s" %
opts.otu_tree)
otu_order = get_order_from_tree(otu_table.ids(axis='observation'), f)
f.close()
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_row_clustering:
data = np.asarray([i for i in otu_table.iter_data(axis='observation')])
otu_order = get_clusters(data, axis='row')
# else just use OTU table ordering
else:
otu_order = np.arange(len(otu_table.ids(axis='observation')))
# otu_order and sample_order should be ids, rather than indices
# to use in sortObservationOrder/sortSampleOrder
otu_id_order = [otu_table.ids(axis='observation')[i] for i in otu_order]
sample_id_order = [otu_table.ids()[i] for i in sample_order]
# Re-order otu table, sampleids, etc. as necessary
otu_table = otu_table.sort_order(otu_id_order, axis='observation')
# otu_ids not used after: tagged for deletion
otu_ids = np.array(otu_table.ids(axis='observation'))[otu_order]
otu_labels = np.array(otu_labels)[otu_order]
otu_table = otu_table.sort_order(sample_id_order)
sample_ids = np.array(otu_table.ids())[sample_order]
plot_heatmap(otu_table, otu_labels, sample_ids, opts.output_fp,
imagetype=opts.imagetype, width=opts.width,
height=opts.height, dpi=opts.dpi,
color_scheme=opts.color_scheme)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
otu_table = parse_biom_table(open(opts.otu_table_fp, 'U'))
obs_md_category = opts.obs_md_category
obs_md_level = opts.obs_md_level
if obs_md_level is None:
# grab the last level if the user didn't specify a level
obs_md_level = -1
else:
# convert to 0-based indexing
obs_md_level -= 1
obs_md = otu_table.ObservationMetadata
# create reference to the observation metadata for the first
# observation for convenient lookup
obs_md_0 = obs_md[0]
obs_md_labels = []
if (obs_md is None or obs_md_category not in obs_md_0):
obs_md_labels = [['']] * len(otu_table.ObservationIds)
else:
for _, _, md in otu_table.iterObservations():
current_md = md[obs_md_category]
if obs_md_level < len(current_md):
current_md_at_level = current_md[obs_md_level]
else:
current_md_at_level = ''
obs_md_labels.append([current_md_at_level])
otu_labels = make_otu_labels(otu_table.ObservationIds,
obs_md_labels)
# Convert to relative abundance if requested
if not opts.absolute_abundance:
otu_table = otu_table.normObservationBySample()
# Get log transform if requested
if not opts.no_log_transform:
if not opts.log_eps is None and opts.log_eps <= 0:
print "Parameter 'log_eps' must be positive. Value was", opts.log_eps
exit(1)
otu_table = get_log_transform(otu_table, opts.log_eps)
if opts.output_dir:
if os.path.exists(opts.output_dir):
dir_path = opts.output_dir
else:
try:
os.mkdir(opts.output_dir)
dir_path = opts.output_dir
except OSError:
pass
else:
dir_path = './'
# Re-order samples by tree if provided
if not opts.sample_tree is None:
sample_order = get_order_from_tree(otu_table.SampleIds,
open(opts.sample_tree, 'U'))
# if there's no sample tree, sort samples by mapping file
elif not opts.map_fname is None:
lines = open(opts.map_fname, 'U').readlines()
metadata = list(parse_mapping_file(lines))
new_map, otu_table = get_overlapping_samples(metadata[0], otu_table)
metadata[0] = new_map
map_sample_ids = zip(*metadata[0])[0]
# if there's a category, do clustering within each category
if not opts.category is None:
category_labels = \
extract_metadata_column(otu_table.SampleIds,
metadata, opts.category)
sample_order = \
get_order_from_categories(otu_table, category_labels)
# else: just use the mapping file order
else:
ordered_sample_ids = []
for sample_id in map_sample_ids:
if sample_id in otu_table.SampleIds:
ordered_sample_ids.append(sample_id)
sample_order = names_to_indices(
otu_table.SampleIds,
ordered_sample_ids)
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_column_clustering:
data = asarray([i for i in otu_table.iterObservationData()])
sample_order = get_clusters(data, axis='column')
# else just use OTU table ordering
else:
sample_order = arange(len(otu_table.SampleIds))
# re-order OTUs by tree (if provided), or clustering
if not opts.otu_tree is None:
# open tree file
try:
f = open(opts.otu_tree, 'U')
except (TypeError, IOError):
raise MissingFileError(
"Couldn't read tree file at path: %s" %
opts.otu_tree)
otu_order = get_order_from_tree(otu_table.ObservationIds, f)
f.close()
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_row_clustering:
data = asarray([i for i in otu_table.iterObservationData()])
otu_order = get_clusters(data, axis='row')
# else just use OTU table ordering
else:
otu_order = arange(len(otu_table.ObservationIds))
# otu_order and sample_order should be ids, rather than indices
# to use in sortObservationOrder/sortSampleOrder
otu_id_order = [otu_table.ObservationIds[i] for i in otu_order]
sample_id_order = [otu_table.SampleIds[i] for i in sample_order]
# Re-order otu table, sampleids, etc. as necessary
otu_table = otu_table.sortObservationOrder(otu_id_order)
# otu_ids not used after: tagged for deletion
otu_ids = array(otu_table.ObservationIds)[otu_order]
otu_labels = array(otu_labels)[otu_order]
otu_table = otu_table.sortSampleOrder(sample_id_order)
sample_ids = array(otu_table.SampleIds)[sample_order]
plot_heatmap(otu_table, otu_labels, sample_ids,
filename=join(dir_path, 'heatmap.pdf'),
color_scheme=opts.color_scheme)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
otu_table = parse_biom_table(open(opts.otu_table_fp, 'U'))
lineages = []
if (otu_table.ObservationMetadata is None or 'taxonomy' not in otu_table.ObservationMetadata[0]):
print '\n\nWarning: The lineages are missing from the OTU table. If you used single_rarefaction.py to create your otu_table, make sure you included the OTU lineages.\n'
lineages = [''] * len(otu_table.ObservationIds)
else:
for val, id, meta in otu_table.iterObservations():
lineages.append([v for v in meta['taxonomy']])
otu_labels = make_otu_labels(otu_table.ObservationIds, lineages)
# Convert to relative abundance if requested
if not opts.absolute_abundance:
otu_table = otu_table.normObservationBySample()
# Get log transform if requested
if not opts.no_log_transform:
if not opts.log_eps is None and opts.log_eps <= 0:
print "Parameter 'log_eps' must be positive. Value was", opts.log_eps
exit(1)
otu_table = get_log_transform(otu_table, opts.log_eps)
if opts.output_dir:
if os.path.exists(opts.output_dir):
dir_path = opts.output_dir
else:
try:
os.mkdir(opts.output_dir)
dir_path = opts.output_dir
except OSError:
pass
else:
dir_path = './'
# Re-order samples by tree if provided
if not opts.sample_tree is None:
sample_order = get_order_from_tree(otu_table.SampleIds,
open(opts.sample_tree, 'U'))
# if there's no sample tree, sort samples by mapping file
elif not opts.map_fname is None:
lines = open(opts.map_fname, 'U').readlines()
metadata = list(parse_mapping_file(lines))
new_map, otu_table = get_overlapping_samples(metadata[0], otu_table)
metadata[0] = new_map
map_sample_ids = zip(*metadata[0])[0]
# if there's a category, do clustering within each category
if not opts.category is None:
category_labels = \
extract_metadata_column(otu_table.SampleIds,
metadata, opts.category)
sample_order = \
get_order_from_categories(otu_table, category_labels)
# else: just use the mapping file order
else:
ordered_sample_ids = []
for sample_id in map_sample_ids:
if sample_id in otu_table.SampleIds:
ordered_sample_ids.append(sample_id)
sample_order = names_to_indices(
otu_table.SampleIds,
ordered_sample_ids)
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_column_clustering:
data = asarray([i for i in otu_table.iterObservationData()])
sample_order = get_clusters(data, axis='column')
# else just use OTU table ordering
else:
sample_order = arange(len(otu_table.SampleIds))
# re-order OTUs by tree (if provided), or clustering
if not opts.otu_tree is None:
# open tree file
try:
f = open(opts.otu_tree, 'U')
except (TypeError, IOError):
raise MissingFileError(
"Couldn't read tree file at path: %s" %
opts.otu_tree)
otu_order = get_order_from_tree(otu_table.ObservationIds, f)
f.close()
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_row_clustering:
data = asarray([i for i in otu_table.iterObservationData()])
otu_order = get_clusters(data, axis='row')
# else just use OTU table ordering
else:
otu_order = arange(len(otu_table.ObservationIds))
# otu_order and sample_order should be ids, rather than indices
# to use in sortObservationOrder/sortSampleOrder
otu_id_order = [otu_table.ObservationIds[i] for i in otu_order]
sample_id_order = [otu_table.SampleIds[i] for i in sample_order]
# Re-order otu table, sampleids, etc. as necessary
otu_table = otu_table.sortObservationOrder(otu_id_order)
# otu_ids not used after: tagged for deletion
otu_ids = array(otu_table.ObservationIds)[otu_order]
otu_labels = array(otu_labels)[otu_order]
otu_table = otu_table.sortSampleOrder(sample_id_order)
sample_ids = array(otu_table.SampleIds)[sample_order]
plot_heatmap(otu_table, otu_labels, sample_ids,
filename=join(dir_path, 'heatmap.pdf'),
color_scheme=opts.color_scheme)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
#Get OTU counts
sample_ids, otu_ids, otus, lineages = \
list(parse_otu_table(open(opts.otu_table_fp,'U'),
count_map_f=float))
# set 'blank' lineages if not supplied
if lineages == []:
print '\n\nWarning: The lineages are missing from the OTU table. If you used single_rarefaction.py to create your otu_table, make sure you pass the "--lineages_included" option.\n'
lineages = [''] * len(otu_ids)
otu_labels = make_otu_labels(otu_ids, lineages)
# Convert to relative abundance if requested
if not opts.absolute_abundance:
for i,row in enumerate(otus):
if row.sum() > 0:
otus[i] = row/row.sum()
# Get log transform if requested
if not opts.no_log_transform:
if not opts.log_eps is None and opts.log_eps <= 0:
print "Parameter 'log_eps' must be positive. Value was", opts.log_eps
exit(1)
otus = get_log_transform(otus, opts.log_eps)
if opts.output_dir:
if os.path.exists(opts.output_dir):
dir_path=opts.output_dir
else:
try:
os.mkdir(opts.output_dir)
dir_path=opts.output_dir
except OSError:
pass
else:
dir_path='./'
# Re-order samples by tree if provided
if not opts.sample_tree is None:
sample_order = get_order_from_tree(sample_ids, opts.sample_tree)
# if there's no sample tree, sort samples by mapping file
elif not opts.map_fname is None:
lines = open(opts.map_fname,'U').readlines()
metadata = list(parse_mapping_file(lines))
sample_ids, new_map, otus = \
get_overlapping_samples(sample_ids, metadata[0], otus)
metadata[0] = new_map
map_sample_ids = zip(*metadata[0])[0]
# if there's a category, do clustering within each category
if not opts.category is None:
category_labels = \
extract_metadata_column(sample_ids, \
metadata, opts.category)
sample_order = \
get_order_from_categories(otus, category_labels)
# else: just use the mapping file order
else:
ordered_sample_ids = []
for sample_id in map_sample_ids:
if sample_id in sample_ids:
ordered_sample_ids.append(sample_id)
sample_order = names_to_indices(sample_ids, ordered_sample_ids)
# if no tree or mapping file, use euclidean upgma
else:
sample_order = arange(len(sample_ids))
# re-order OTUs by tree (if provided), or clustering
if not opts.otu_tree is None:
# open tree file
try:
f = open(opts.otu_tree, 'U')
except (TypeError, IOError):
raise MissingFileError, \
"Couldn't read tree file at path: %s" % opts.otu_tree
otu_order = get_order_from_tree(otu_ids, f)
f.close()
# if no tree, use euclidean upgma
else:
otu_order = get_clusters(otus,axis='row')
# Re-order otu table, sampleids, etc. as necessary
otus = otus[otu_order,:]
otu_ids = array(otu_ids)[otu_order]
otu_labels = array(otu_labels)[otu_order]
otus = otus[:,sample_order]
sample_ids = array(sample_ids)[sample_order]
plot_heatmap(otus, otu_labels, sample_ids,
filename=join(dir_path,'heatmap.pdf'))
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
otu_table = load_table(opts.otu_table_fp)
obs_md_category = opts.obs_md_category
obs_md_level = opts.obs_md_level
if obs_md_level is None:
# grab the last level if the user didn't specify a level
obs_md_level = -1
else:
# convert to 0-based indexing
obs_md_level -= 1
obs_md = otu_table.metadata(axis='observation')
obs_md_labels = []
if (obs_md is None or obs_md_category not in obs_md[0]):
obs_md_labels = [['']] * len(otu_table.ids(axis='observation'))
else:
for _, _, md in otu_table.iter(axis='observation'):
current_md = md[obs_md_category]
if obs_md_level < len(current_md):
current_md_at_level = current_md[obs_md_level]
else:
current_md_at_level = ''
obs_md_labels.append([current_md_at_level])
otu_labels = make_otu_labels(otu_table.ids(axis='observation'),
obs_md_labels)
# Convert to relative abundance if requested
if not opts.absolute_abundance:
otu_table = otu_table.norm(axis='observation')
# Get log transform if requested
if not opts.no_log_transform:
otu_table = get_log_transform(otu_table)
# Re-order samples by tree if provided
if opts.sample_tree is not None:
sample_order = get_order_from_tree(otu_table.ids(),
open(opts.sample_tree, 'U'))
# if there's no sample tree, sort samples by mapping file
elif opts.map_fname is not None:
lines = open(opts.map_fname, 'U').readlines()
metadata = list(parse_mapping_file(lines))
new_map, otu_table = get_overlapping_samples(metadata[0], otu_table)
metadata[0] = new_map
map_sample_ids = zip(*metadata[0])[0]
# if there's a category, do clustering within each category
if opts.category is not None:
category_labels = extract_metadata_column(otu_table.ids(),
metadata, opts.category)
sample_order = get_order_from_categories(otu_table,
category_labels)
# else: just use the mapping file order
else:
ordered_sample_ids = []
for sample_id in map_sample_ids:
if otu_table.exists(sample_id):
ordered_sample_ids.append(sample_id)
sample_order = names_to_indices(otu_table.ids(),
ordered_sample_ids)
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_column_clustering:
data = np.asarray([i for i in otu_table.iter_data(axis='observation')])
sample_order = get_clusters(data, axis='column')
# else just use OTU table ordering
else:
sample_order = np.arange(len(otu_table.ids()))
# re-order OTUs by tree (if provided), or clustering
if opts.otu_tree is not None:
# open tree file
try:
f = open(opts.otu_tree, 'U')
except (TypeError, IOError):
raise MissingFileError("Couldn't read tree file at path: %s" %
opts.otu_tree)
otu_order = get_order_from_tree(otu_table.ids(axis='observation'), f)
f.close()
# if no tree or mapping file, perform upgma euclidean
elif not opts.suppress_row_clustering:
data = np.asarray([i for i in otu_table.iter_data(axis='observation')])
otu_order = get_clusters(data, axis='row')
# else just use OTU table ordering
else:
otu_order = np.arange(len(otu_table.ids(axis='observation')))
# otu_order and sample_order should be ids, rather than indices
# to use in sortObservationOrder/sortSampleOrder
otu_id_order = [otu_table.ids(axis='observation')[i] for i in otu_order]
sample_id_order = [otu_table.ids()[i] for i in sample_order]
# Re-order otu table, sampleids, etc. as necessary
otu_table = otu_table.sort_order(otu_id_order, axis='observation')
otu_labels = np.array(otu_labels)[otu_order]
otu_table = otu_table.sort_order(sample_id_order)
sample_labels = otu_table.ids()
plot_heatmap(otu_table,
otu_labels,
sample_labels,
opts.output_fp,
imagetype=opts.imagetype,
width=opts.width,
height=opts.height,
dpi=opts.dpi,
color_scheme=opts.color_scheme)