def test_format_permanova_results(self):
"""Test formatting results of PERMANOVA."""
exp = 'Method name\tPseudo-F statistic\tp-value\tNumber of ' + \
'permutations\nPERMANOVA\t.\t.\t\n'
obs = format_permanova_results(self.permanova_overview(999))
self.assertEqual(self.remove_nums(obs), exp)
exp = 'Method name\tPseudo-F statistic\tp-value\tNumber of ' + \
'permutations\nPERMANOVA\t.\tToo few iters to compute ' + \
'p-value (num_iters=)\t\n'
obs = format_permanova_results(self.permanova_overview(0))
self.assertEqual(self.remove_nums(obs), exp)
def test_format_permanova_results(self):
"""Test formatting results of PERMANOVA."""
exp = (
"Method name\tPseudo-F statistic\tp-value\tNumber of " "permutations\nPERMANOVA\t0.123456789\t0.111\t999\n"
)
obs = format_permanova_results(
{"method_name": "PERMANOVA", "f_value": 0.1234567890, "p_value": 0.11111, "num_perms": 999}
)
self.assertEqual(obs, exp)
exp = (
"Method name\tPseudo-F statistic\tp-value\tNumber of "
"permutations\nPERMANOVA\t0.123456789\tToo few iters to "
"compute p-value (num_iters=0)\t0\n"
)
obs = format_permanova_results(
{"method_name": "PERMANOVA", "f_value": 0.1234567890, "p_value": 1.0, "num_perms": 0}
)
self.assertEqual(obs, exp)
def compare_categories(dm_fp, map_fp, method, categories, num_perms, out_dir):
"""Runs the specified statistical method using the category of interest.
This method does not return anything; all output is written to results
files in out_dir.
Arguments:
dm_fp - filepath to the input distance matrix
map_fp - filepath to the input metadata mapping file
categories - list of categories in the metadata mapping file to
consider in the statistical test. Multiple categories will only be
considered if method is 'best', otherwise only the first category
will be considered
num_perms - the number of permutations to use when calculating the
p-value. If method is 'best' or 'morans_i', this parameter will be
ignored as they are not permutation-based methods
out_dir - path to the output directory where results files will be
written. It is assumed that this directory already exists and we
have write permissions to it
"""
# Make sure we were passed a list of categories, not a single string.
if not isinstance(categories, ListType):
raise TypeError("The supplied categories must be a list of "
"strings.")
# Special case: we do not allow SampleID as it is not a category, neither
# in data structure representation nor in terms of a statistical test (no
# groups are formed since all entries are unique IDs).
if 'SampleID' in categories:
raise ValueError("Cannot use SampleID as a category because it is a "
"unique identifier for each sample, and thus does "
"not create groups of samples (nor can it be used as "
"a numeric category in Moran's I or BEST analyses). "
"Please use a different metadata column to perform "
"statistical tests on.")
# Parse the mapping file and distance matrix.
md_map = MetadataMap.parseMetadataMap(open(map_fp, 'U'))
dm = DistanceMatrix.parseDistanceMatrix(open(dm_fp, 'U'))
# Remove any samples from the mapping file that aren't in the distance
# matrix (important for validation checks). Use strict=True so that an
# error is raised if the distance matrix contains any samples that aren't
# in the mapping file.
md_map.filterSamples(dm.SampleIds, strict=True)
# Run the specified statistical method.
if method in ['adonis', 'morans_i', 'mrpp', 'permdisp', 'dbrda']:
# These methods are run in R. Input validation must be done here before
# running the R commands. The pure-Python implementations perform all
# validation in the classes in the stats module.
# Make sure the input distance matrix is symmetric and hollow.
if not dm.is_symmetric_and_hollow():
raise ValueError("The distance matrix must be symmetric and "
"hollow.")
# Check to make sure all categories passed in are in mapping file and
# are not all the same value.
for category in categories:
if not category in md_map.CategoryNames:
raise ValueError("Category '%s' not found in mapping file "
"columns." % category)
if md_map.hasSingleCategoryValue(category):
raise ValueError("All values in category '%s' are the "
"same. The statistical method '%s' cannot "
"operate on a category that creates only "
"a single group of samples (e.g. there "
"are no 'between' distances because "
"there is only a single group)." %
(category, method))
# Build the command arguments string.
command_args = [
'-d %s -m %s -c %s -o %s' % (dm_fp, map_fp, categories[0], out_dir)
]
if method == 'morans_i':
# Moran's I requires only numeric categories.
for category in categories:
if not md_map.isNumericCategory(category):
raise TypeError(
"The category '%s' is not numeric. Not "
"all values could be converted to numbers." % category)
else:
# The rest require groups of samples, so the category values cannot
# all be unique.
for category in categories:
if md_map.hasUniqueCategoryValues(category):
raise ValueError("All values in category '%s' are unique. "
"This statistical method cannot operate "
"on a category with unique values (e.g. "
"there are no 'within' distances because "
"each group of samples contains only a "
"single sample)." % category)
# Only Moran's I doesn't accept a number of permutations.
if num_perms < 0:
raise ValueError("The number of permutations must be greater "
"than or equal to zero.")
command_args[0] += ' -n %d' % num_perms
rex = RExecutor(TmpDir=get_qiime_temp_dir())
rex(command_args, '%s.r' % method, output_dir=out_dir)
elif method == 'anosim':
anosim = Anosim(md_map, dm, categories[0])
anosim_results = anosim(num_perms)
out_f = open(join(out_dir, '%s_results.txt' % method), 'w+')
out_f.write(format_anosim_results(anosim_results))
out_f.close()
elif method == 'best':
best = Best(dm, md_map, categories)
best_results = best()
out_f = open(join(out_dir, '%s_results.txt' % method), 'w+')
out_f.write(format_best_results(best_results))
out_f.close()
elif method == 'permanova':
permanova = Permanova(md_map, dm, categories[0])
permanova_results = permanova(num_perms)
out_f = open(join(out_dir, '%s_results.txt' % method), 'w+')
out_f.write(format_permanova_results(permanova_results))
out_f.close()
else:
raise ValueError("Unrecognized method '%s'. Valid methods: %r" %
(method, methods))
def compare_categories(dm_fp, map_fp, method, categories, num_perms, out_dir):
"""Runs the specified statistical method using the category of interest.
This method does not return anything; all output is written to results
files in out_dir.
Arguments:
dm_fp - filepath to the input distance matrix
map_fp - filepath to the input metadata mapping file
categories - list of categories in the metadata mapping file to
consider in the statistical test. Multiple categories will only be
considered if method is 'best', otherwise only the first category
will be considered
num_perms - the number of permutations to use when calculating the
p-value. If method is 'best' or 'morans_i', this parameter will be
ignored as they are not permutation-based methods
out_dir - path to the output directory where results files will be
written. It is assumed that this directory already exists and we
have write permissions to it
"""
# Make sure we were passed a list of categories, not a single string.
if not isinstance(categories, ListType):
raise TypeError("The supplied categories must be a list of "
"strings.")
# Special case: we do not allow SampleID as it is not a category, neither
# in data structure representation nor in terms of a statistical test (no
# groups are formed since all entries are unique IDs).
if 'SampleID' in categories:
raise ValueError("Cannot use SampleID as a category because it is a "
"unique identifier for each sample, and thus does "
"not create groups of samples (nor can it be used as "
"a numeric category in Moran's I or BEST analyses). "
"Please use a different metadata column to perform "
"statistical tests on.")
# Parse the mapping file and distance matrix.
with open(map_fp, 'U') as map_f:
md_map = MetadataMap.parseMetadataMap(map_f)
with open(dm_fp, 'U') as dm_f:
dm = SymmetricDistanceMatrix.from_file(dm_f)
# Remove any samples from the mapping file that aren't in the distance
# matrix (important for validation checks). Use strict=True so that an
# error is raised if the distance matrix contains any samples that aren't
# in the mapping file.
md_map.filterSamples(dm.sample_ids, strict=True)
# Run the specified statistical method.
if method in ['adonis', 'morans_i', 'mrpp', 'permdisp', 'dbrda']:
# These methods are run in R. Input validation must be done here before
# running the R commands. The pure-Python implementations perform all
# validation in the classes in the stats module.
# Check to make sure all categories passed in are in mapping file and
# are not all the same value.
for category in categories:
if not category in md_map.CategoryNames:
raise ValueError("Category '%s' not found in mapping file "
"columns." % category)
if md_map.hasSingleCategoryValue(category):
raise ValueError("All values in category '%s' are the "
"same. The statistical method '%s' cannot "
"operate on a category that creates only "
"a single group of samples (e.g. there "
"are no 'between' distances because "
"there is only a single group)."
% (category, method))
# Build the command arguments string.
command_args = ['-d %s -m %s -c %s -o %s'
% (dm_fp, map_fp, categories[0], out_dir)]
if method == 'morans_i':
# Moran's I requires only numeric categories.
for category in categories:
if not md_map.isNumericCategory(category):
raise TypeError("The category '%s' is not numeric. Not "
"all values could be converted to numbers."
% category)
else:
# The rest require groups of samples, so the category values cannot
# all be unique.
for category in categories:
if md_map.hasUniqueCategoryValues(category):
raise ValueError("All values in category '%s' are unique. "
"This statistical method cannot operate "
"on a category with unique values (e.g. "
"there are no 'within' distances because "
"each group of samples contains only a "
"single sample)." % category)
# Only Moran's I doesn't accept a number of permutations.
if num_perms < 0:
raise ValueError("The number of permutations must be greater "
"than or equal to zero.")
command_args[0] += ' -n %d' % num_perms
rex = RExecutor(TmpDir=get_qiime_temp_dir())
rex(command_args, '%s.r' % method, output_dir=out_dir)
elif method == 'anosim':
anosim = Anosim(md_map, dm, categories[0])
anosim_results = anosim(num_perms)
out_f = open(join(out_dir, '%s_results.txt' % method), 'w+')
out_f.write(format_anosim_results(anosim_results))
out_f.close()
elif method == 'best':
best = Best(dm, md_map, categories)
best_results = best()
out_f = open(join(out_dir, '%s_results.txt' % method), 'w+')
out_f.write(format_best_results(best_results))
out_f.close()
elif method == 'permanova':
permanova = Permanova(md_map, dm, categories[0])
permanova_results = permanova(num_perms)
out_f = open(join(out_dir, '%s_results.txt' % method), 'w+')
out_f.write(format_permanova_results(permanova_results))
out_f.close()
else:
raise ValueError("Unrecognized method '%s'. Valid methods: %r"
% (method, methods))