def main(otu_table, mapping_data, categories, output_dir, \
samples_to_plot = None, legend = False, xaxis = True):
"""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 sample IDs and taxonomy for a collapsed
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.
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
LEVEL = 2
FILEPREFIX = 'Figure_4_'
MICHAEL_POLLAN = '000007108.1075657'
NUM_TAXA = 9
NUM_CATS_TO_PLOT = 7
# Loads the mapping file
map_dict = map_to_2D_dict(mapping_data)
(common_taxa, whole_sample_ids, whole_summary) = \
summarize_human_taxa(otu_table, LEVEL)
# Converts final taxa to a clean list
common_phyla = []
for taxon in common_taxa:
common_phyla.append(taxon[1].strip(' p__').strip('[').strip(']'))
common_taxa = common_phyla
# Checks that the correct sample ids are plotted
if samples_to_plot == None:
sample_ids = whole_sample_ids
else:
sample_ids = samples_to_plot
# Identifies Michael Pollan's pre-ABX sample
mp_sample_pos = whole_sample_ids.index(MICHAEL_POLLAN)
mp_sample_taxa = whole_summary[:,mp_sample_pos]
# Loads the category dictionary
categories = load_category_files(category_fp, LEVEL)
# Generates a figure for each sample
for idx, sample_id in enumerate(whole_sample_ids):
if sample_id in sample_ids:
# Preallocates a numpy array for the plotting data
tax_array = zeros((NUM_TAXA, NUM_CATS_TO_PLOT))
meta_data = map_dict[sample_id]
cat_list = ['You', 'Average', 'Similar Diet', ' Similar BMI',
'Same Gender', 'Similar Age',
'Michael Pollan', '']
#cat_list.append('Your Fecal Sample')
#cat_list.append('Average Fecal Samples')
tax_array[:,0] = whole_summary[:,idx]
tax_array[:,1] = mean(whole_summary, 1)
cat_watch = 2
# Identifies the appropriate metadata categories
for cat in categories:
# Pulls metadata for the sample and category
mapping_key = meta_data[cat]
# Pulls taxonomic summary and group descriptions for the category
tax_summary = categories[cat]['Taxa Summary']
group_descriptions = categories[cat]['Groups']
# Amends 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[:,cat_watch] = tax_summary[:,mapping_col]
cat_watch = cat_watch + 1
tax_array[:,-1] = mp_sample_taxa
# Plots the data
filename = pjoin(output_dir, '%s%s.pdf' \
% (FILEPREFIX, sample_id))
plot_american_gut(tax_array, filename)
def test_summarize_human_taxa(self):
# Defines the known values
known_ids = ['00010', '00100', '00200', '00111', '00112', '00211']
known_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'),
(u'k__Bacteria', u' p__Other')]
table_known = array([[0.5000, 0.2500, 0.5500, 0.3000, 0.45000, 0.1000],
[0.3000, 0.5000, 0.3500, 0.6200, 0.45000, 0.7500],
[0.0000, 0.0000, 0.0000, 0.0000, 0.00000, 0.0000],
[0.0800, 0.0000, 0.0000, 0.0030, 0.00000, 0.0000],
[0.0500, 0.0100, 0.0020, 0.0010, 0.00000, 0.0000],
[0.0200, 0.0800, 0.0500, 0.0020, 0.05000, 0.0000],
[0.0010, 0.0200, 0.0000, 0.0000, 0.00010, 0.0000],
[0.0080, 0.0000, 0.0020, 0.0000, 0.00000, 0.0500],
[0.0210, 0.1100, 0.0460, 0.0240, 0.01990, 0.0000]])
# Creates an otu table for testing which corresponds with the
sample_ids = ['00010', '00100', '00200', '00111', '00112', '00211']
observation_ids = ['1001', '2001', '2002', '2003', '3001', '3003',
'4001', '5001', '6001', '7001', '8001', '9001',
'9002', '9003']
observation_md = [{'taxonomy': (u'k__Bacteria', u' p__Bacteroidetes',
u'c__Bacteroidia')},
{'taxonomy': (u'k__Bacteria', u' p__Firmicutes',
u'c__Clostridia')},
{'taxonomy': (u'k__Bacteria', u' p__Firmicutes',
u'c__Erysipelotrichi')},
{'taxonomy': (u'k__Bacteria', u' p__Firmicutes',
u'c__Bacilli')},
{'taxonomy': (u'k__Bacteria', u' p__Proteobacteria',
u'c__Alphaproteobacteria')},
{'taxonomy': (u'k__Bacteria', u' p__Proteobacteria',
u'c__Gammaproteobacteria')},
{'taxonomy': (u'k__Bacteria', u' p__Tenericutes',
u'c__Mollicutes')},
{'taxonomy': (u'k__Bacteria', u' p__Actinobacteria',
u'c__Coriobacteriia')},
{'taxonomy': (u'k__Bacteria', u' p__Verrucomicrobia',
u'c__Verrucomicrobiae')},
{'taxonomy': (u'k__Bacteria', u' p__Cyanobacteria',
u'c__4C0d-2')},
{'taxonomy': (u'k__Bacteria', u' p__Fusobacteria',
u'c__Fusobacteriia')},
{'taxonomy': (u'k__Bacteria', u' p__TM7',
u'c__TM7-2')},
{'taxonomy': (u'k__Bacteria', u' p__Acidobacteria',
u'c__Chloracidobacteria')},
{'taxonomy': (u'k__Bacteria', u' p__', u'c__')}]
data = array([[ 1691, 3004, 18606, 6914, 1314, 22843],
[ 2019, 1091, 8163, 1112, 738, 2362],
[ 67, 4, 2835, 310, 85, 161],
[ 731, 407, 18240, 1924, 492, 522],
[ 8, 1, 0, 53, 8, 275],
[ 105, 179, 0, 504, 79, 2771],
[ 451, 0, 0, 33, 0, 0],
[ 282, 60, 106, 11, 0, 0],
[ 113, 481, 2658, 22, 146, 0],
[ 6, 120, 0, 0, 0, 0],
[ 45, 0, 106, 0, 0, 1523],
[ 39, 341, 1761, 139, 18, 0],
[ 21, 268, 153, 8, 15, 0],
[ 59, 51, 531, 120, 25, 0]])
otu_table = table_factory(data = data, sample_ids = sample_ids, \
observation_ids = observation_ids, \
observation_metadata = observation_md, \
constructor = SparseOTUTable)
# Tests that the table is summarized correctly
(test_taxa, test_ids, test_table) = summarize_human_taxa(otu_table, 2)
# Checks that all the outputs are correct
self.assertEqual(test_ids, known_ids)
self.assertEqual(test_taxa, known_taxa)
self.assertEqual(test_table.all(), table_known.all())