if '&&' in col:
for _col in col.split('&&'):
if _col not in lookup_header:
offending_fields.append(col)
elif col not in lookup_header:
offending_fields.append(col)
else:
# if the user didn't specify the header names display everything
color_by_column_names = header[:]
# extract a list of the custom axes provided and each element is numeric
if custom_axes:
custom_axes = custom_axes.strip().strip("'").strip('"').split(',')
# the MetadataMap object makes some checks easier
map_object = MetadataMap(mapping_file_to_dict(mapping_data, header), [])
for axis in custom_axes:
# append the field to the error queue that it belongs to
if axis not in lookup_header:
offending_fields.append(axis)
break
# make sure this value is in the mapping file
elif axis not in color_by_column_names:
color_by_column_names.append(axis)
# perform only if the for loop does not call break
else:
# make sure all these axes are numeric
for axis in custom_axes:
if map_object.isNumericCategory(axis) == False:
non_numeric_categories.append(axis)
def preprocess_mapping_file(data,
headers,
columns,
unique=False,
single=False,
clones=0):
"""Process a mapping file to expand the data or remove unuseful fields
Inputs:
data: mapping file data
headers: mapping file headers
columns: list of headers to keep, if one of these headers includes two
ampersands, this function will create a new column by merging the delimited
columns.
unique: keep columns where all values are unique
single: keep columns where all values are the same
clones: number of times to replicate the metadata
Outputs:
data: processed mapping file data
headers: processed mapping file headers
"""
# The sample ID must always be there, else it's meaningless data
if 'SampleID' != columns[0]:
columns = ['SampleID'] + columns
# process concatenated columns if needed
merge = []
for column in columns:
if '&&' in column:
merge.append(column)
# each element needs several columns to be merged
for new_column in merge:
indices = [
headers.index(header_name)
for header_name in new_column.split('&&')
]
# join all the fields of the metadata that are listed in indices
for line in data:
line.append(''.join([line[index] for index in indices]))
headers.append(new_column)
# remove all unique or singled valued columns
if unique or single:
columns_to_remove = []
metadata = MetadataMap(mapping_file_to_dict(data, headers), [])
# find columns that have values that are all unique
if unique == True:
columns_to_remove += [
column_name for column_name in headers[1::]
if metadata.hasUniqueCategoryValues(column_name)
]
# remove categories where there is only one value
if single == True:
columns_to_remove += [
column_name for column_name in headers[1::]
if metadata.hasSingleCategoryValue(column_name)
]
columns_to_remove = list(set(columns_to_remove))
# remove the single or unique columns
data, headers = keep_columns_from_mapping_file(data,
headers,
columns_to_remove,
negate=True)
# remove anything not specified in the input
data, headers = keep_columns_from_mapping_file(data, headers, columns)
# sanitize the mapping file data and headers
data, headers = sanitize_mapping_file(data, headers)
# clones mean: replicate the metadata retagging the sample ids with a suffix
if clones:
out_data = []
for index in range(0, clones):
out_data.extend([[element[0] + '_%d' % index] + element[1::]
for element in data])
data = out_data
return data, headers
def run_core_diversity_analyses(biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_group_significance=False,
status_update_callback=print_to_stdout):
"""
"""
if categories is not None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp, 'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError(
"Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" %
(c, ', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError(
"Category '%s' contains only one value. "
"Categories analyzed here require at least two values." %
c)
else:
categories = []
comma_separated_categories = ','.join(categories)
# prep some variables
if params is None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
# begin logging
old_log_fps = glob(join(output_dir, 'log_20*txt'))
log_fp = generate_log_fp(output_dir)
index_links.append(
('Master run log', log_fp, _index_headers['run_summary']))
for old_log_fp in old_log_fps:
index_links.append(
('Previous run log', old_log_fp, _index_headers['run_summary']))
logger = WorkflowLogger(log_fp, params=params, qiime_config=qiime_config)
input_fps = [biom_fp, mapping_fp]
if tree_fp is not None:
input_fps.append(tree_fp)
log_input_md5s(logger, input_fps)
# run 'biom summarize-table' on input BIOM table
try:
params_str = get_params_str(params['biom-summarize-table'])
except KeyError:
params_str = ''
biom_table_stats_output_fp = '%s/biom_table_summary.txt' % output_dir
if not exists(biom_table_stats_output_fp):
biom_table_summary_cmd = \
"biom summarize-table -i %s -o %s %s" % \
(biom_fp, biom_table_stats_output_fp, params_str)
commands.append([('Generate BIOM table summary',
biom_table_summary_cmd)])
else:
logger.write("Skipping 'biom summarize-table' as %s exists.\n\n" %
biom_table_stats_output_fp)
index_links.append(('BIOM table statistics', biom_table_stats_output_fp,
_index_headers['run_summary']))
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
if not exists(filtered_biom_fp):
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" %\
(biom_fp, filtered_biom_fp, sampling_depth)
commands.append([(
'Filter low sequence count samples from table (minimum sequence count: %d)'
% sampling_depth, filter_samples_cmd)])
else:
logger.write(
"Skipping filter_samples_from_otu_table.py as %s exists.\n\n" %
filtered_biom_fp)
biom_fp = filtered_biom_fp
# rarify the BIOM table to sampling_depth
rarefied_biom_fp = "%s/table_even%d.biom" % (output_dir, sampling_depth)
if not exists(rarefied_biom_fp):
single_rarefaction_cmd = "single_rarefaction.py -i %s -o %s -d %d" %\
(biom_fp, rarefied_biom_fp, sampling_depth)
commands.append([
('Rarify the OTU table to %d sequences/sample' % sampling_depth,
single_rarefaction_cmd)
])
else:
logger.write("Skipping single_rarefaction.py as %s exists.\n\n" %
rarefied_biom_fp)
# run initial commands and reset the command list
if len(commands) > 0:
command_handler(commands,
status_update_callback,
logger,
close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir, sampling_depth)
# Need to check for the existence of any distance matrices, since the user
# can select which will be generated.
existing_dm_fps = glob('%s/*_dm.txt' % bdiv_even_output_dir)
if len(existing_dm_fps) == 0:
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=rarefied_biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
# Note: we pass sampling depth=None here as
# we rarify the BIOM table above and pass that
# in here.
sampling_depth=None,
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping beta_diversity_through_plots.py as %s exist(s).\n\n"
% ', '.join(existing_dm_fps))
even_dm_fps = [(split(fp)[1].strip('_dm.txt'), fp)
for fp in existing_dm_fps]
# Get make_distance_boxplots parameters
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (
bdiv_even_output_dir, bdiv_metric)
plot_output_fp = '%s/%s_Distances.pdf' % (boxplots_output_dir,
category)
stats_output_fp = '%s/%s_Stats.txt' % (boxplots_output_dir,
category)
if not exists(plot_output_fp):
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir,
mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category, boxplots_cmd)
])
else:
logger.write(
"Skipping make_distance_boxplots.py for %s as %s exists.\n\n"
% (category, plot_output_fp))
index_links.append(
('Distance boxplots (%s)' % bdiv_metric, plot_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Distance boxplots statistics (%s)' % bdiv_metric,
stats_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('PCoA plot (%s)' % bdiv_metric,
'%s/%s_emperor_pcoa_plot/index.html' %
(bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % (bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % (bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
if not suppress_alpha_diversity:
# Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,
sampling_depth)
rarefaction_plots_output_fp = \
'%s/alpha_rarefaction_plots/rarefaction_plots.html' % arare_full_output_dir
if not exists(rarefaction_plots_output_fp):
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback,
retain_intermediate_files=False)
else:
logger.write("Skipping alpha_rarefaction.py as %s exists.\n\n" %
rarefaction_plots_output_fp)
index_links.append(
('Alpha rarefaction plots', rarefaction_plots_output_fp,
_index_headers['alpha_diversity']))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
if len(categories) > 0:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(
split(collated_alpha_diversity_fp)[1])[0]
compare_alpha_output_dir = '%s/compare_%s' % \
(arare_full_output_dir, alpha_metric)
if not exists(compare_alpha_output_dir):
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp,
mapping_fp,
comma_separated_categories,
compare_alpha_output_dir,
params_str)
commands.append([
('Compare alpha diversity (%s)' % alpha_metric,
compare_alpha_cmd)
])
for category in categories:
alpha_comparison_stat_fp = '%s/%s_stats.txt' % \
(compare_alpha_output_dir, category)
alpha_comparison_boxplot_fp = '%s/%s_boxplots.pdf' % \
(compare_alpha_output_dir, category)
index_links.append(
('Alpha diversity statistics (%s, %s)' %
(category, alpha_metric),
alpha_comparison_stat_fp,
_index_headers['alpha_diversity']))
index_links.append(
('Alpha diversity boxplots (%s, %s)' %
(category, alpha_metric),
alpha_comparison_boxplot_fp,
_index_headers['alpha_diversity']))
else:
logger.write("Skipping compare_alpha_diversity.py"
" for %s as %s exists.\n\n" %
(alpha_metric, compare_alpha_output_dir))
else:
logger.write("Skipping compare_alpha_diversity.py as"
" no categories were provided.\n\n")
if not suppress_taxa_summary:
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob(
join(taxa_plots_output_dir, 'taxa_summary_plots', '*.html'))
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping summarize_taxa_through_plots.py for as %s exist(s).\n\n"
% ', '.join(existing_taxa_plot_html_fps))
index_links.append(
('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html' % taxa_plots_output_dir,
_index_headers['taxa_summary']))
index_links.append(
('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html' % taxa_plots_output_dir,
_index_headers['taxa_summary']))
for category in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,
category)
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob('%s/taxa_summary_plots/*.html' %
taxa_plots_output_dir)
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping summarize_taxa_through_plots.py for %s as %s exist(s).\n\n"
% (category, ', '.join(existing_taxa_plot_html_fps)))
index_links.append(
('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html' %
taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
index_links.append(
('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html' %
taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
if not suppress_group_significance:
params_str = get_params_str(params['group_significance'])
# group significance tests, aka category significance
for category in categories:
group_signifance_fp = \
'%s/group_significance_%s.txt' % (output_dir, category)
if not exists(group_signifance_fp):
# Build the OTU cateogry significance command
group_significance_cmd = \
'group_significance.py -i %s -m %s -c %s -o %s %s' %\
(rarefied_biom_fp, mapping_fp, category,
group_signifance_fp, params_str)
commands.append([('Group significance (%s)' % category,
group_significance_cmd)])
else:
logger.write(
"Skipping group_significance.py for %s as %s exists.\n\n" %
(category, group_signifance_fp))
index_links.append(
('Category significance (%s)' % category, group_signifance_fp,
_index_headers['group_significance']))
filtered_biom_gzip_fp = '%s.gz' % filtered_biom_fp
if not exists(filtered_biom_gzip_fp):
commands.append([('Compress the filtered BIOM table',
'gzip %s' % filtered_biom_fp)])
else:
logger.write(
"Skipping compressing of filtered BIOM table as %s exists.\n\n" %
filtered_biom_gzip_fp)
index_links.append(
('Filtered BIOM table (minimum sequence count: %d)' % sampling_depth,
filtered_biom_gzip_fp, _index_headers['run_summary']))
rarified_biom_gzip_fp = '%s.gz' % rarefied_biom_fp
if not exists(rarified_biom_gzip_fp):
commands.append([('Compress the rarified BIOM table',
'gzip %s' % rarefied_biom_fp)])
else:
logger.write(
"Skipping compressing of rarified BIOM table as %s exists.\n\n" %
rarified_biom_gzip_fp)
index_links.append(
('Rarified BIOM table (sampling depth: %d)' % sampling_depth,
rarified_biom_gzip_fp, _index_headers['run_summary']))
if len(commands) > 0:
command_handler(commands, status_update_callback, logger)
else:
logger.close()
generate_index_page(index_links, index_fp)
def run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_otu_category_significance=False,
status_update_callback=print_to_stdout):
"""
"""
if categories != None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp,'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError, ("Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c,', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError, ("Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c)
else:
categories= []
# prep some variables
if params == None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
# begin logging
log_fp = generate_log_fp(output_dir)
index_links.append(('Master run log',log_fp,_index_headers['run_summary']))
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
input_fps = [biom_fp,mapping_fp]
if tree_fp != None:
input_fps.append(tree_fp)
log_input_md5s(logger,input_fps)
# run print_biom_table_summary.py on input BIOM table
try:
params_str = get_params_str(params['print_biom_table_summary'])
except KeyError:
params_str = ''
biom_table_stats_output_fp = '%s/biom_table_summary.txt' % output_dir
print_biom_table_summary_cmd = \
"print_biom_table_summary.py -i %s -o %s --suppress_md5 %s" % \
(biom_fp, biom_table_stats_output_fp,params_str)
index_links.append(('BIOM table statistics',
biom_table_stats_output_fp,
_index_headers['run_summary']))
commands.append([('Generate BIOM table summary',
print_biom_table_summary_cmd)])
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" %\
(biom_fp,filtered_biom_fp,sampling_depth)
commands.append([('Filter low sequence count samples from table (minimum sequence count: %d)' % sampling_depth,
filter_samples_cmd)])
biom_fp = filtered_biom_fp
# run initial commands and reset the command list
command_handler(commands,
status_update_callback,
logger,
close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir,sampling_depth)
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
sampling_depth=sampling_depth,
# force suppression of distance histograms - boxplots work better
# in this context, and are created below.
histogram_categories=[],
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (bdiv_even_output_dir,bdiv_metric)
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir, mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category,
boxplots_cmd)])
index_links.append(('Distance boxplots (%s)' % bdiv_metric,
'%s/%s_Distances.pdf' % \
(boxplots_output_dir,category),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance boxplots statistics (%s)' % bdiv_metric,
'%s/%s_Stats.txt' % \
(boxplots_output_dir,category),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('3D plot (%s, continuous coloring)' % bdiv_metric,
'%s/%s_3d_continuous/%s_pc_3D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('3D plot (%s, discrete coloring)' % bdiv_metric,
'%s/%s_3d_discrete/%s_pc_3D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('2D plot (%s, continuous coloring)' % bdiv_metric,
'%s/%s_2d_continuous/%s_pc_2D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('2D plot (%s, discrete coloring)' % bdiv_metric,
'%s/%s_2d_discrete/%s_pc_2D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
if not suppress_alpha_diversity:
## Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,sampling_depth)
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Alpha rarefaction plots',
'%s/alpha_rarefaction_plots/rarefaction_plots.html'\
% arare_full_output_dir,
_index_headers['alpha_diversity']))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
for category in categories:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
alpha_comparison_output_fp = '%s/%s_%s.txt' % \
(arare_full_output_dir,category,alpha_metric)
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp, mapping_fp, category,
alpha_comparison_output_fp, params_str)
commands.append([('Compare alpha diversity (%s, %s)' %\
(category,alpha_metric),
compare_alpha_cmd)])
index_links.append(
('Alpha diversity statistics (%s, %s)' % (category,alpha_metric),
alpha_comparison_output_fp,
_index_headers['alpha_diversity']))
if not suppress_taxa_summary:
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
for category in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,category)
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
if not suppress_otu_category_significance:
# OTU category significance
for category in categories:
category_signifance_fp = \
'%s/category_significance_%s.txt' % (output_dir, category)
try:
params_str = get_params_str(params['otu_category_significance'])
except KeyError:
params_str = ''
# Build the OTU cateogry significance command
category_significance_cmd = \
'otu_category_significance.py -i %s -m %s -c %s -o %s %s' %\
(biom_fp, mapping_fp, category,
category_signifance_fp, params_str)
commands.append([('OTU category significance (%s)' % category,
category_significance_cmd)])
index_links.append(('Category significance (%s)' % category,
category_signifance_fp,
_index_headers['otu_category_sig']))
commands.append([('Compress the filtered BIOM table','gzip %s' % filtered_biom_fp)])
index_links.append(('Filtered BIOM table (minimum sequence count: %d)' % sampling_depth,
'%s.gz' % filtered_biom_fp,
_index_headers['run_summary']))
command_handler(commands, status_update_callback, logger)
generate_index_page(index_links,index_fp)
