def test_summarize_common_categories(self):
"""Checks that summarize_common_categories is sane"""
# Defines the known values
known_ids = ('00010', '00100', '00200', '00111', '00112', '00211')
table_known = array([[ 0.49973390, 0.25004162, 0.55001035,
0.30008969, 0.45034247, 0.09997702],
[ 0.29998226, 0.50008324, 0.35000658,
0.62008969, 0.45000000, 0.75000821],
[ 0.02004612, 0.02996504, 0.00000000,
0.04995516, 0.02979452, 0.10000985],
[ 0.05002661, 0.00998835, 0.00199402,
0.00098655, 0.00000000, 0.00000000],
[ 0.02004612, 0.08007325, 0.05000094,
0.00197309, 0.05000000, 0.00000000],
[ 0.08000710, 0.00000000, 0.00000000,
0.00295964, 0.00000000, 0.00000000],
[ 0.00106440, 0.01997669, 0.00000000,
0.00000000, 0.00000000, 0.00000000],
[ 0.00798297, 0.00000000, 0.00199402,
0.00000000, 0.00000000, 0.05000492],
[ 0.02111052, 0.10987182, 0.04599409,
0.02394619, 0.01986301, 0.00000000]])
known_common_cats = [(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__Actinobacteria'),
(u'k__Bacteria', u'p__Verrucomicrobia'),
(u'k__Bacteria', u'p__Tenericutes'),
(u'k__Bacteria', u'p__Cyanobacteria'),
(u'k__Bacteria', u'p__Fusobacteria'),
(u'k__Bacteria', u'p__Other')]
# Checks that appropriate errors are raised when the wrong type of
# argument is passed.
with self.assertRaises(ValueError):
summarize_common_categories(biom_table=self.otu_table,
level=2,
common_categories=self.common_cats,
metadata_category='Billy_Joel_Song')
# Calculates the test values
[test_ids, test_table, test_common_cats] = \
summarize_common_categories(biom_table=self.otu_table,
level=2,
common_categories=self.common_cats)
# Checks that all the outputs are correct
self.assertEqual(tuple(test_ids), known_ids)
assert_almost_equal(test_table, table_known, decimal=4)
self.assertEqual(test_common_cats, known_common_cats)
def test_summarize_common_categories(self):
"""Checks that summarize_common_categories is sane"""
# Defines the known values
known_ids = ('00010', '00100', '00200', '00111', '00112', '00211')
table_known = array([[
0.49973390, 0.25004162, 0.55001035, 0.30008969, 0.45034247,
0.09997702
],
[
0.29998226, 0.50008324, 0.35000658,
0.62008969, 0.45000000, 0.75000821
],
[
0.02004612, 0.02996504, 0.00000000,
0.04995516, 0.02979452, 0.10000985
],
[
0.05002661, 0.00998835, 0.00199402,
0.00098655, 0.00000000, 0.00000000
],
[
0.02004612, 0.08007325, 0.05000094,
0.00197309, 0.05000000, 0.00000000
],
[
0.08000710, 0.00000000, 0.00000000,
0.00295964, 0.00000000, 0.00000000
],
[
0.00106440, 0.01997669, 0.00000000,
0.00000000, 0.00000000, 0.00000000
],
[
0.00798297, 0.00000000, 0.00199402,
0.00000000, 0.00000000, 0.05000492
],
[
0.02111052, 0.10987182, 0.04599409,
0.02394619, 0.01986301, 0.00000000
]])
known_common_cats = [(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__Actinobacteria'),
(u'k__Bacteria', u'p__Verrucomicrobia'),
(u'k__Bacteria', u'p__Tenericutes'),
(u'k__Bacteria', u'p__Cyanobacteria'),
(u'k__Bacteria', u'p__Fusobacteria'),
(u'k__Bacteria', u'p__Other')]
# Checks that appropriate errors are raised when the wrong type of
# argument is passed.
with self.assertRaises(ValueError):
summarize_common_categories(biom_table=self.otu_table,
level=2,
common_categories=self.common_cats,
metadata_category='Billy_Joel_Song')
# Calculates the test values
[test_ids, test_table, test_common_cats] = \
summarize_common_categories(biom_table=self.otu_table,
level=2,
common_categories=self.common_cats)
# Checks that all the outputs are correct
self.assertEqual(test_ids, known_ids)
assert_almost_equal(test_table, table_known, decimal=4)
self.assertEqual(test_common_cats, known_common_cats)
def main(otu_table,
mapping_data,
cat_tables,
output_dir,
sample_type='fecal',
samples_to_plot=None,
legend=False,
xaxis=True,
debug=False):
"""Creates stacked bar plots for an otu table
INPUTS:
otu_table -- an open OTU table
mapping_data -- a tab delimited string containing the mapping data
passed from the mapping file.
categories -- a dictionary keying a mapping category to the
corresponding biom table
output_dir -- the location of the directory where output files should
be saved. If this directory does not exist, it will be
created.
samples_to_plot -- a list of sample ids to plot. If no value is passed,
then all samples in the biom table are analyzed.
debug -- ignore properly handling Michael Pollan's sample
OUTPUTS:
A pdf of stacked taxonomy will be generated for each sample and saved
in the output directory. These will follow the file name format
Figure_4_<SAMPLEID>.pdf
"""
# Sets constants for analyzing the data
LEVEL = 2
CATEGORY = 'taxonomy'
NUM_TAXA = 9
NUM_CATS_TO_PLOT = 7
# Sets up plotting constants
COLORMAP = array([[0.8353, 0.2421, 0.3098], [0.9569, 0.4275, 0.2627],
[0.9922, 0.6824, 0.3804], [0.9961, 0.8784, 0.5351],
[0.9020, 0.9608, 0.5961], [0.6706, 0.8667, 0.6431],
[0.4000, 0.7608, 0.6471], [0.1961, 0.5333, 0.7412],
[0.3333, 0.3333, 0.3333]])
FIG_DIMS = (4.44444, 3.33333)
AXIS_DIMS = array([[0.05, 0.05], [0.95, 0.95]])
# Common taxa are designated before processing to remain constant.
COMMON_TAXA = [(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__Actinobacteria'),
(u'k__Bacteria', u'p__Verrucomicrobia'),
(u'k__Bacteria', u'p__Tenericutes'),
(u'k__Bacteria', u'p__Cyanobacteria'),
(u'k__Bacteria', u'p__Fusobacteria')]
SKIPSET = set(('Sample', 'Average', 'MP'))
# Names categories being plotted
if sample_type == 'fecal':
michael_pollan = '10317.000007108'
cat_list = [
'You', 'Average', 'Similar Diet', ' Similar BMI', 'Same Gender',
'Similar Age', 'Michael Pollan'
]
order = [
'Sample', 'Average', 'DIET_TYPE', 'BMI_CAT', 'SEX', 'AGE_CAT', 'MP'
]
elif sample_type == 'skin':
michael_pollan = '10317.000007113'
cat_list = [
'You', 'Average', 'Similar Cosmetic Use', 'Same Dominant Hand',
'Same Gender', 'Same Age', 'Michael Pollan'
]
order = [
'Sample', 'Average', 'COSMETICS_FREQUENCY', 'DOMINANT_HAND', 'SEX',
'AGE_CAT', 'MP'
]
elif sample_type == 'oral':
michael_pollan = '10317.000007109'
cat_list = [
'You', 'Average', 'Similar Diet', 'Flossing Frequency',
'Same Gender', 'Same Age', 'Michael Pollan'
]
order = [
'Sample', 'Average', 'DIET_TYPE', 'FLOSSING_FREQUENCY', 'SEX',
'AGE_CAT', 'MP'
]
else:
raise ValueError('%s is not a supported sample type.' % sample_type)
# Gets the mapping file
map_dict = map_to_2D_dict(mapping_data)
# Gets the category file dictionary summarized with the common categories
# Generates the category file dictionary
categories = parse_category_files(raw_tables=cat_tables,
common_groups=COMMON_TAXA[:8],
level=LEVEL,
metadata=CATEGORY)
# Summarizes taxonomy for the category
(whole_sample_ids, whole_summary, new_common_taxa) = \
summarize_common_categories(biom_table=otu_table,
level=LEVEL,
common_categories=COMMON_TAXA[:8],
metadata_category=CATEGORY)
# Converts the final taxa to a cleaned up list
# Converts final taxa to a clean list
common_phyla = []
for taxon in new_common_taxa:
common_phyla.append(taxon[1].strip(' p__').strip('[').strip(']'))
new_common_taxa = common_phyla
# Checks that the crrect sample ids are plotted
if samples_to_plot is None:
sample_ids = whole_sample_ids
else:
sample_ids = samples_to_plot
if len(sample_ids) > 1:
# TODO: make the rest of the code reflect this...
raise ValueError("SCRIPT NO LONGER SUPPORTS MULTIPLE SAMPLES")
# Identifies Michael Pollan's pre-ABX sample
if debug:
mp_sample_pos = 2
else:
mp_sample_pos = whole_sample_ids.tolist().index(michael_pollan)
mp_sample_taxa = whole_summary[:, mp_sample_pos]
# Gets the table average
table_average = mean(whole_summary, 1)
# Generates a figure for each sample
for idx, sample_id in enumerate(whole_sample_ids):
if sample_id in sample_ids:
meta_data = map_dict[sample_id]
# Prealocates a numpy array to hold the data
tax_array = zeros((NUM_TAXA, NUM_CATS_TO_PLOT))
# Adds preset values to the array so the first column is the sample
# the second column is the average and the last column is Michael
# Pollan
tax_array[:, 0] = whole_summary[:, idx]
tax_array[:, 1] = table_average
tax_array[:, -1] = mp_sample_taxa
# Adds the categories to the table in the listed order
for idx, cat in enumerate(order):
# Skips over undesired categories
if cat in SKIPSET:
continue
# Gets the sample metadata
mapping_key = meta_data[cat]
# Pulls taxonomic summary and group descriptions
tax_summary = categories[cat]['Summary']
group_descriptions = categories[cat]['Groups'].tolist()
# Appends plotting tables
try:
mapping_col = group_descriptions.index(mapping_key)
except:
raise ValueError('The %s cannot be found in %s.' %
(mapping_key, cat))
tax_array[:, idx] = tax_summary[:, mapping_col]
# Sets up the file to save the data
filename = pjoin(output_dir, 'figure4.pdf')
# Plots the data
render_barchart(data_table=tax_array,
x_axis=False,
group_names=new_common_taxa,
legend=False,
sample_names=cat_list,
y_axis=False,
axis_dims=AXIS_DIMS,
fig_dims=FIG_DIMS,
file_out=filename,
show_edge=False,
colors=COLORMAP)
def main(otu_table, mapping_data, cat_tables, output_dir, sample_type='fecal',
samples_to_plot=None, legend=False, xaxis=True, debug=False):
"""Creates stacked bar plots for an otu table
INPUTS:
otu_table -- an open OTU table
mapping_data -- a tab delimited string containing the mapping data
passed from the mapping file.
categories -- a dictionary keying a mapping category to the
corresponding biom table
output_dir -- the location of the directory where output files should
be saved. If this directory does not exist, it will be
created.
samples_to_plot -- a list of sample ids to plot. If no value is passed,
then all samples in the biom table are analyzed.
debug -- ignore properly handling Michael Pollan's sample
OUTPUTS:
A pdf of stacked taxonomy will be generated for each sample and saved
in the output directory. These will follow the file name format
Figure_4_<SAMPLEID>.pdf
"""
# Sets constants for analyzing the data
LEVEL = 2
CATEGORY = 'taxonomy'
NUM_TAXA = 9
NUM_CATS_TO_PLOT = 7
# Sets up file name constants
FILEPREFIX = 'Figure_4_'
FILE_END = '.pdf'
# Sets up plotting constants
COLORMAP = array([[0.8353, 0.2421, 0.3098],
[0.9569, 0.4275, 0.2627],
[0.9922, 0.6824, 0.3804],
[0.9961, 0.8784, 0.5351],
[0.9020, 0.9608, 0.5961],
[0.6706, 0.8667, 0.6431],
[0.4000, 0.7608, 0.6471],
[0.1961, 0.5333, 0.7412],
[0.3333, 0.3333, 0.3333]])
FIG_DIMS = (4.44444, 3.33333)
AXIS_DIMS = array([[0.05, 0.05],
[0.95, 0.95]])
# Common taxa are designated before processing to remain constant.
COMMON_TAXA = [(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__Actinobacteria'),
(u'k__Bacteria', u'p__Verrucomicrobia'),
(u'k__Bacteria', u'p__Tenericutes'),
(u'k__Bacteria', u'p__Cyanobacteria'),
(u'k__Bacteria', u'p__Fusobacteria')]
SKIPSET = set(('Sample', 'Average', 'MP'))
# Names categories being plotted
if sample_type == 'fecal':
michael_pollan = '000007108.1075657'
cat_list = ['You', 'Average', 'Similar Diet', ' Similar BMI',
'Same Gender', 'Similar Age', 'Michael Pollan']
order = ['Sample', 'Average', 'DIET_TYPE', 'BMI_CATEGORY', 'SEX',
'AGE_CATEGORY', 'MP']
elif sample_type == 'skin':
michael_pollan = '7113.1075702'
cat_list = ['You', 'Average', 'Similar Cosmetic Use',
'Same Dominant Hand', 'Same Gender', 'Same Age',
'Michael Pollan']
order = ['Sample', 'Average', 'COSMETICS_FREQUENCY',
'DOMINANT_HAND', 'SEX', 'AGE_CATEGORY', 'MP']
elif sample_type == 'oral':
michael_pollan = '7109.1075688'
cat_list = ['You', 'Average', 'Similar Diet', 'Flossing Frequency',
'Same Gender', 'Same Age', 'Michael Pollan']
order = ['Sample', 'Average', 'DIET_TYPE', 'FLOSSING_FREQUENCY',
'SEX', 'AGE_CATEGORY', 'MP']
else:
raise ValueError('%s is not a supported sample type.' % sample_type)
# Gets the mapping file
map_dict = map_to_2D_dict(mapping_data)
# Gets the category file dictionary summarized with the common categories
# Generates the category file dictionary
categories = parse_category_files(raw_tables=cat_tables,
common_groups=COMMON_TAXA[:8],
level=LEVEL,
metadata=CATEGORY)
# Summarizes taxonomy for the category
(whole_sample_ids, whole_summary, new_common_taxa) = \
summarize_common_categories(biom_table=otu_table,
level=LEVEL,
common_categories=COMMON_TAXA[:8],
metadata_category=CATEGORY)
# Converts the final taxa to a cleaned up list
# Converts final taxa to a clean list
common_phyla = []
for taxon in new_common_taxa:
common_phyla.append(taxon[1].strip(' p__').strip('[').strip(']'))
new_common_taxa = common_phyla
# Checks that the crrect sample ids are plotted
if samples_to_plot is None:
sample_ids = whole_sample_ids
else:
sample_ids = samples_to_plot
# Identifies Michael Pollan's pre-ABX sample
if debug:
mp_sample_pos = 2
else:
mp_sample_pos = whole_sample_ids.tolist().index(michael_pollan)
mp_sample_taxa = whole_summary[:, mp_sample_pos]
# Gets the table average
table_average = mean(whole_summary, 1)
# Generates a figure for each sample
for idx, sample_id in enumerate(whole_sample_ids):
if sample_id in sample_ids:
meta_data = map_dict[sample_id]
# Prealocates a numpy array to hold the data
tax_array = zeros((NUM_TAXA, NUM_CATS_TO_PLOT))
# Adds preset values to the array so the first column is the sample
# the second column is the average and the last column is Michael
# Pollan
tax_array[:, 0] = whole_summary[:, idx]
tax_array[:, 1] = table_average
tax_array[:, -1] = mp_sample_taxa
# Adds the categories to the table in the listed order
for idx, cat in enumerate(order):
# Skips over undesired categories
if cat in SKIPSET:
continue
# Gets the sample metadata
mapping_key = meta_data[cat]
# Pulls taxonomic summary and group descriptions
tax_summary = categories[cat]['Summary']
group_descriptions = categories[cat]['Groups'].tolist()
# Appends plotting tables
try:
mapping_col = group_descriptions.index(mapping_key)
except:
raise ValueError('The %s cannot be found in %s.'
% (mapping_key, cat))
tax_array[:, idx] = tax_summary[:, mapping_col]
# Sets up the file to save the data
filename = pjoin(output_dir, '%s%s%s'
% (FILEPREFIX, sample_id, FILE_END))
# Plots the data
render_barchart(data_table=tax_array,
x_axis=False,
group_names=new_common_taxa,
legend=False,
sample_names=cat_list,
y_axis=False,
axis_dims=AXIS_DIMS,
fig_dims=FIG_DIMS,
file_out=filename,
show_edge=False,
colors=COLORMAP)
