def _generate_taxa_summary_plots(
otu_table_fp,
personal_map_fp,
personal_id,
personal_cat,
personal_cat_values,
body_site_cat,
body_site_cat_values,
time_series_cat,
output_dir,
command_handler,
status_update_callback,
logger,
):
files_to_remove = []
dirs_to_remove = []
## Split OTU table into self/other per-body-site tables
commands = []
cmd_title = "Splitting OTU table into self/other (%s)" % personal_id
cmd = "split_otu_table.py -i %s -m %s -f %s -o %s" % (otu_table_fp, personal_map_fp, personal_cat, output_dir)
commands.append([(cmd_title, cmd)])
command_handler(commands, status_update_callback, logger, close_logger_on_success=False)
# Prefix to be used for taxa summary dirs. Will be
# <taxa_summary_dir_prefix>_<self|other>_<body site>/.
ts_dir_prefix = "taxa_summaries"
# Create taxa summaries for self and other, per body site.
for personal_cat_value in personal_cat_values:
personal_cat_biom_fp = join(output_dir, add_filename_suffix(otu_table_fp, "_%s" % personal_cat_value))
personal_cat_map_fp = join(output_dir, "mapping_%s.txt" % personal_cat_value)
files_to_remove.append(personal_cat_biom_fp)
files_to_remove.append(personal_cat_map_fp)
body_site_dir = join(output_dir, personal_cat_value)
commands = []
cmd_title = 'Splitting "%s" OTU table by body site (%s)' % (personal_cat_value, personal_id)
cmd = "split_otu_table.py -i %s -m %s -f %s -o %s" % (
personal_cat_biom_fp,
personal_map_fp,
body_site_cat,
body_site_dir,
)
commands.append([(cmd_title, cmd)])
dirs_to_remove.append(body_site_dir)
command_handler(commands, status_update_callback, logger, close_logger_on_success=False)
commands = []
for body_site_cat_value in body_site_cat_values:
body_site_otu_table_fp = join(
body_site_dir, add_filename_suffix(personal_cat_biom_fp, "_%s" % body_site_cat_value)
)
# We won't always get an OTU table if the mapping file
# category contains samples that aren't in the OTU table
# (e.g. the 'na' state for body site).
if exists(body_site_otu_table_fp):
ts_dir = join(output_dir, "%s_%s_%s" % (ts_dir_prefix, personal_cat_value, body_site_cat_value))
create_dir(ts_dir)
dirs_to_remove.append(ts_dir)
# Summarize.
summarized_otu_table_fp = join(ts_dir, "%s_otu_table.biom" % time_series_cat)
cmd_title = "Summarizing OTU table by category (%s)" % personal_id
cmd = "summarize_otu_by_cat.py -i %s -c %s -o %s " "-m %s " % (
personal_map_fp,
body_site_otu_table_fp,
summarized_otu_table_fp,
time_series_cat,
)
commands.append([(cmd_title, cmd)])
# Sort.
sorted_otu_table_fp = join(ts_dir, "%s_otu_table_sorted.biom" % time_series_cat)
cmd_title = "Sorting OTU table (%s)" % personal_id
cmd = "sort_otu_table.py -i %s -o %s" % (summarized_otu_table_fp, sorted_otu_table_fp)
commands.append([(cmd_title, cmd)])
# Summarize taxa.
cmd_title = "Summarizing taxa (%s)" % personal_id
cmd = "summarize_taxa.py -i %s -o %s" % (sorted_otu_table_fp, ts_dir)
commands.append([(cmd_title, cmd)])
create_comparative_taxa_plots_html(
body_site_cat_value, join(output_dir, "%s_comparative.html" % body_site_cat_value)
)
command_handler(commands, status_update_callback, logger, close_logger_on_success=False)
# Make each corresponding taxa summary compatible so that coloring matches
# between them. We want to be able to compare self versus other at each
# body site.
commands = []
valid_body_sites = []
for body_site_cat_value in body_site_cat_values:
personal_cat_vals = list(personal_cat_values)
ts_dir = join(output_dir, "%s_%s_%s" % (ts_dir_prefix, personal_cat_vals[0], body_site_cat_value))
if not exists(ts_dir):
continue
# Check that we have 2+ weeks (samples were previously collapsed into
# weeks for self and other). If we don't have 2+ weeks,
# plot_taxa_summary.py will fail, so we'll skip this body site.
weeks_otu_table_fp = join(ts_dir, "%s_otu_table_sorted.biom" % time_series_cat)
with open(weeks_otu_table_fp, "U") as weeks_otu_table_f:
if _count_num_samples(weeks_otu_table_f) < 2:
continue
ts_fps1 = sorted(glob(join(ts_dir, "%s_otu_table_sorted_L*.txt" % time_series_cat)))
ts_dir = join(output_dir, "%s_%s_%s" % (ts_dir_prefix, personal_cat_vals[1], body_site_cat_value))
if not exists(ts_dir):
continue
weeks_otu_table_fp = join(ts_dir, "%s_otu_table_sorted.biom" % time_series_cat)
with open(weeks_otu_table_fp, "U") as weeks_otu_table_f:
if _count_num_samples(weeks_otu_table_f) < 2:
continue
ts_fps2 = sorted(glob(join(ts_dir, "%s_otu_table_sorted_L*.txt" % time_series_cat)))
if len(ts_fps1) != len(ts_fps2):
raise ValueError("There are not an equal number of taxa summaries " "to compare between self and other.")
compatible_ts_dir = join(output_dir, "compatible_ts_%s" % body_site_cat_value)
dirs_to_remove.append(compatible_ts_dir)
compatible_ts_fps = defaultdict(list)
for ts_fp1, ts_fp2 in zip(ts_fps1, ts_fps2):
if basename(ts_fp1) != basename(ts_fp2):
raise ValueError("Could not find matching taxa summaries " "between self and other to compare.")
# Make taxa summaries compatible.
cmd_title = "Making compatible taxa summaries (%s)" % personal_id
cmd = "compare_taxa_summaries.py -i %s,%s -o %s -m paired -n 0" % (ts_fp1, ts_fp2, compatible_ts_dir)
commands.append([(cmd_title, cmd)])
compatible_ts_fps[personal_cat_vals[0]].append(
join(compatible_ts_dir, add_filename_suffix(ts_fp1, "_sorted_and_filled_0"))
)
compatible_ts_fps[personal_cat_vals[1]].append(
join(compatible_ts_dir, add_filename_suffix(ts_fp2, "_sorted_and_filled_1"))
)
for personal_cat_value in personal_cat_values:
# Plot taxa summaries.
ts_fps = ",".join(sorted(compatible_ts_fps[personal_cat_value]))
ts_plots_dir = join(
output_dir, "taxa_plots_%s_%s" % (personal_cat_value, body_site_cat_value), "taxa_summary_plots"
)
cmd_title = "Plot taxa summaries (%s)" % personal_id
cmd = "plot_taxa_summary.py -i %s -o %s -a numeric" % (ts_fps, ts_plots_dir)
commands.append([(cmd_title, cmd)])
# If we've gotten this far, we'll be able to process this body site
# (i.e. there are enough weeks).
valid_body_sites.append(body_site_cat_value)
# Hack to allow print-only mode.
if command_handler is not print_commands and not valid_body_sites:
raise ValueError(
"None of the body sites for personal ID '%s' could "
"be processed because there were not enough weeks "
"to create taxa summary plots." % personal_id
)
command_handler(commands, status_update_callback, logger, close_logger_on_success=False)
return files_to_remove, dirs_to_remove
def create_personal_results(output_dir,
mapping_fp,
coord_fp,
collated_dir,
otu_table_fp,
prefs_fp,
personal_id_column,
personal_ids=None,
column_title='Self',
individual_titles=None,
category_to_split='BodySite',
time_series_category='WeeksSinceStart',
rarefaction_depth=10000,
alpha=0.05,
rep_set_fp=None,
parameter_fp=None,
body_site_rarefied_otu_table_dir=None,
retain_raw_data=False,
suppress_alpha_rarefaction=False,
suppress_beta_diversity=False,
suppress_taxa_summary_plots=False,
suppress_alpha_diversity_boxplots=False,
suppress_otu_category_significance=False,
command_handler=call_commands_serially,
status_update_callback=no_status_updates):
# Create our output directory and copy over the resources the personalized
# pages need (e.g. javascript, images, etc.).
create_dir(output_dir)
support_files_dir = join(output_dir, 'support_files')
if not exists(support_files_dir):
copytree(join(get_project_dir(), 'my_microbes', 'support_files'),
support_files_dir)
logger = WorkflowLogger(generate_log_fp(output_dir))
mapping_data, header, comments = parse_mapping_file(open(mapping_fp, 'U'))
try:
personal_id_index = header.index(personal_id_column)
except ValueError:
raise ValueError("Personal ID field '%s' is not a mapping file column "
"header." % personal_id_column)
try:
bodysite_index = header.index(category_to_split)
except ValueError:
raise ValueError("Category to split field '%s' is not a mapping file "
"column header." % category_to_split)
header = header[:-1] + [column_title] + [header[-1]]
# column that differentiates between body-sites within a single individual
# used for the creation of the vectors in make_3d_plots.py, this data is
# created by concatenating the two columns when writing the mapping file
site_id_category = '%s&&%s' % (personal_id_column, category_to_split)
header.insert(len(header)-1, site_id_category)
all_personal_ids = get_personal_ids(mapping_data, personal_id_index)
if personal_ids == None:
personal_ids = all_personal_ids
else:
for pid in personal_ids:
if pid not in all_personal_ids:
raise ValueError("'%s' is not a personal ID in the mapping "
"file column '%s'." %
(pid, personal_id_column))
if time_series_category not in header:
raise ValueError("Time series field '%s' is not a mapping file column "
"header." % time_series_category)
otu_table_title = splitext(basename(otu_table_fp))
output_directories = []
raw_data_files = []
raw_data_dirs = []
# Rarefy the OTU table and split by body site here (instead of on a
# per-individual basis) as we can use the same rarefied and split tables
# for each individual.
if not suppress_otu_category_significance:
rarefied_otu_table_fp = join(output_dir,
add_filename_suffix(otu_table_fp,
'_even%d' % rarefaction_depth))
if body_site_rarefied_otu_table_dir is None:
commands = []
cmd_title = 'Rarefying OTU table'
cmd = 'single_rarefaction.py -i %s -o %s -d %s' % (otu_table_fp,
rarefied_otu_table_fp, rarefaction_depth)
commands.append([(cmd_title, cmd)])
raw_data_files.append(rarefied_otu_table_fp)
per_body_site_dir = join(output_dir, 'per_body_site_otu_tables')
cmd_title = 'Splitting rarefied OTU table by body site'
cmd = 'split_otu_table.py -i %s -m %s -f %s -o %s' % (
rarefied_otu_table_fp, mapping_fp, category_to_split,
per_body_site_dir)
commands.append([(cmd_title, cmd)])
raw_data_dirs.append(per_body_site_dir)
command_handler(commands, status_update_callback, logger,
close_logger_on_success=False)
else:
per_body_site_dir = body_site_rarefied_otu_table_dir
for person_of_interest in personal_ids:
create_dir(join(output_dir, person_of_interest))
personal_mapping_file_fp = join(output_dir, person_of_interest,
'mapping_file.txt')
html_fp = join(output_dir, person_of_interest, 'index.html')
personal_mapping_data = create_personal_mapping_file(mapping_data,
person_of_interest, personal_id_index, bodysite_index,
individual_titles)
personal_mapping_f = open(personal_mapping_file_fp, 'w')
personal_mapping_f.write(
format_mapping_file(header, personal_mapping_data, comments))
personal_mapping_f.close()
raw_data_files.append(personal_mapping_file_fp)
column_title_index = header.index(column_title)
column_title_values = set([e[column_title_index]
for e in personal_mapping_data])
cat_index = header.index(category_to_split)
cat_values = set([e[cat_index] for e in personal_mapping_data])
# Generate alpha diversity boxplots, split by body site, one per
# metric. We run this one first because it completes relatively
# quickly and it does not call any QIIME scripts.
alpha_diversity_boxplots_html = ''
if not suppress_alpha_diversity_boxplots:
adiv_boxplots_dir = join(output_dir, person_of_interest,
'adiv_boxplots')
create_dir(adiv_boxplots_dir)
output_directories.append(adiv_boxplots_dir)
logger.write("\nGenerating alpha diversity boxplots (%s)\n\n" %
person_of_interest)
plot_filenames = _generate_alpha_diversity_boxplots(
collated_dir, personal_mapping_file_fp,
category_to_split, column_title, rarefaction_depth,
adiv_boxplots_dir)
# Create relative paths for use with the index page.
rel_boxplot_dir = basename(normpath(adiv_boxplots_dir))
plot_fps = [join(rel_boxplot_dir, plot_filename)
for plot_filename in plot_filenames]
alpha_diversity_boxplots_html = \
create_alpha_diversity_boxplots_html(plot_fps)
## Alpha rarefaction steps
if not suppress_alpha_rarefaction:
rarefaction_dir = join(output_dir, person_of_interest,
'alpha_rarefaction')
output_directories.append(rarefaction_dir)
commands = []
cmd_title = 'Creating rarefaction plots (%s)' % person_of_interest
cmd = 'make_rarefaction_plots.py -i %s -m %s -p %s -o %s' % (
collated_dir, personal_mapping_file_fp, prefs_fp,
rarefaction_dir)
commands.append([(cmd_title, cmd)])
raw_data_dirs.append(join(rarefaction_dir, 'average_plots'))
raw_data_dirs.append(join(rarefaction_dir, 'average_tables'))
command_handler(commands, status_update_callback, logger,
close_logger_on_success=False)
## Beta diversity steps
if not suppress_beta_diversity:
pcoa_dir = join(output_dir, person_of_interest, 'beta_diversity')
pcoa_time_series_dir = join(output_dir, person_of_interest,
'beta_diversity_time_series')
output_directories.append(pcoa_dir)
output_directories.append(pcoa_time_series_dir)
commands = []
cmd_title = 'Creating beta diversity time series plots (%s)' % \
person_of_interest
cmd = 'make_3d_plots.py -m %s -p %s -i %s -o %s --custom_axes=' % (
personal_mapping_file_fp, prefs_fp, coord_fp, pcoa_time_series_dir) +\
'\'%s\' --add_vectors=\'%s,%s\'' % (time_series_category,
site_id_category, time_series_category)
commands.append([(cmd_title, cmd)])
cmd_title = 'Creating beta diversity plots (%s)' % \
person_of_interest
cmd = 'make_3d_plots.py -m %s -p %s -i %s -o %s' % (personal_mapping_file_fp,
prefs_fp, coord_fp,
pcoa_dir)
commands.append([(cmd_title, cmd)])
command_handler(commands, status_update_callback, logger,
close_logger_on_success=False)
## Time series taxa summary plots steps
if not suppress_taxa_summary_plots:
area_plots_dir = join(output_dir, person_of_interest, 'time_series')
create_dir(area_plots_dir)
output_directories.append(area_plots_dir)
## Split OTU table into self/other per-body-site tables
commands = []
cmd_title = 'Splitting OTU table into self/other (%s)' % \
person_of_interest
cmd = 'split_otu_table.py -i %s -m %s -f %s -o %s' % (otu_table_fp,
personal_mapping_file_fp, column_title, area_plots_dir)
commands.append([(cmd_title, cmd)])
command_handler(commands, status_update_callback, logger,
close_logger_on_success=False)
for column_title_value in column_title_values:
biom_fp = join(area_plots_dir,
add_filename_suffix(otu_table_fp,
'_%s' % column_title_value))
column_title_map_fp = join(area_plots_dir, 'mapping_%s.txt' %
column_title_value)
raw_data_files.append(biom_fp)
raw_data_files.append(column_title_map_fp)
body_site_dir = join(area_plots_dir, column_title_value)
commands = []
cmd_title = 'Splitting "%s" OTU table by body site (%s)' % \
(column_title_value, person_of_interest)
cmd = 'split_otu_table.py -i %s -m %s -f %s -o %s' % (biom_fp,
personal_mapping_file_fp, category_to_split,
body_site_dir)
commands.append([(cmd_title, cmd)])
raw_data_dirs.append(body_site_dir)
command_handler(commands, status_update_callback, logger,
close_logger_on_success=False)
commands = []
for cat_value in cat_values:
body_site_otu_table_fp = join(body_site_dir,
add_filename_suffix(biom_fp, '_%s' % cat_value))
# We won't always get an OTU table if the mapping file
# category contains samples that aren't in the OTU table
# (e.g. the 'na' state for body site).
if exists(body_site_otu_table_fp):
plots = join(area_plots_dir, 'taxa_plots_%s_%s' % (
column_title_value, cat_value))
cmd_title = 'Creating taxa summary plots (%s)' % \
person_of_interest
cmd = ('summarize_taxa_through_plots.py -i %s '
'-o %s -c %s -m %s -s' %
(body_site_otu_table_fp, plots,
time_series_category,
personal_mapping_file_fp))
if parameter_fp is not None:
cmd += ' -p %s' % parameter_fp
commands.append([(cmd_title, cmd)])
raw_data_files.append(join(plots, '*.biom'))
raw_data_files.append(join(plots, '*.txt'))
create_comparative_taxa_plots_html(cat_value,
join(area_plots_dir, '%s_comparative.html' %
cat_value))
command_handler(commands, status_update_callback, logger,
close_logger_on_success=False)
# Generate OTU category significance tables (per body site).
otu_cat_sig_output_fps = []
otu_category_significance_html = ''
if not suppress_otu_category_significance:
otu_cat_sig_dir = join(output_dir, person_of_interest,
'otu_category_significance')
create_dir(otu_cat_sig_dir)
output_directories.append(otu_cat_sig_dir)
# For each body-site rarefied OTU table, run
# otu_category_significance.py using self versus other category.
# Keep track of each output file that is created because we need to
# parse these later on.
commands = []
for cat_value in cat_values:
body_site_otu_table_fp = join(per_body_site_dir,
add_filename_suffix(rarefied_otu_table_fp,
'_%s' % cat_value))
if exists(body_site_otu_table_fp):
otu_cat_output_fp = join(otu_cat_sig_dir,
'otu_cat_sig_%s.txt' % cat_value)
cmd_title = ('Testing for significant differences in '
'OTU abundances in "%s" body site (%s)' % (
cat_value, person_of_interest))
cmd = ('otu_category_significance.py -i %s -m %s -c %s '
'-o %s' % (body_site_otu_table_fp,
personal_mapping_file_fp,
column_title,
otu_cat_output_fp))
commands.append([(cmd_title, cmd)])
raw_data_files.append(otu_cat_output_fp)
otu_cat_sig_output_fps.append(otu_cat_output_fp)
command_handler(commands, status_update_callback, logger,
close_logger_on_success=False)
# Reformat otu category significance tables.
otu_cat_sig_html_filenames = \
format_otu_category_significance_tables_as_html(
otu_cat_sig_output_fps, alpha, otu_cat_sig_dir,
individual_titles, rep_set_fp=rep_set_fp)
# Create relative paths for use with the index page.
rel_otu_cat_sig_dir = basename(normpath(otu_cat_sig_dir))
otu_cat_sig_html_fps = [join(rel_otu_cat_sig_dir, html_filename)
for html_filename in otu_cat_sig_html_filenames]
otu_category_significance_html = \
create_otu_category_significance_html(otu_cat_sig_html_fps)
# Create the index.html file for the current individual.
create_index_html(person_of_interest, html_fp,
alpha_diversity_boxplots_html=alpha_diversity_boxplots_html,
otu_category_significance_html=otu_category_significance_html)
logger.close()
# Clean up the unnecessary raw data files and directories. glob will only
# grab paths that exist.
if not retain_raw_data:
for raw_data_fp_glob in raw_data_files:
remove_files(glob(raw_data_fp_glob))
for raw_data_dir_glob in raw_data_dirs:
for dir_to_remove in glob(raw_data_dir_glob):
rmtree(dir_to_remove)
return output_directories
