def test_motif_scoring(self):
"""tests the motif scoring in integration"""
search_distances = {'upstream': (-20, 150)}
scan_distances = {'upstream': (-30, 250)}
matrix_factory = dm.DataMatrixFactory([dm.nochange_filter, dm.center_scale_filter])
infile = util.DelimitedFile.read('halo_ratios5.tsv', has_header=True, quote='\"')
ratio_matrix = matrix_factory.create_from(infile)
meme_suite = meme.MemeSuite430(remove_tempfiles=True)
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(search_distances['upstream'])]
organism = make_halo(ratio_matrix, search_distances, scan_distances)
membership = FakeMembership()
config_params = {'memb.min_cluster_rows_allowed': 3,
'memb.max_cluster_rows_allowed': 70,
'multiprocessing': False,
'num_clusters': 1,
'output_dir': 'out',
'num_iterations': 2000}
func = motif.MemeScoringFunction(organism, membership, ratio_matrix,
meme_suite,
sequence_filters=sequence_filters,
scaling_func=lambda iter: 1.0,
num_motif_func=motif.default_nmotif_fun,
config_params=config_params)
iteration_result = { 'iteration': 100 }
matrix = func.compute(iteration_result)
"""
def test_motif_scoring(self):
"""tests the motif scoring in integration"""
search_distances = {'upstream': (-20, 150)}
scan_distances = {'upstream': (-30, 250)}
matrix_factory = dm.DataMatrixFactory([dm.nochange_filter, dm.center_scale_filter])
infile = util.read_dfile('example_data/hal/halo_ratios5.tsv',
has_header=True, quote='\"')
ratio_matrix = matrix_factory.create_from(infile)
meme_suite = meme.MemeSuite430(remove_tempfiles=True)
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(search_distances['upstream'])]
organism = make_halo(ratio_matrix, search_distances, scan_distances)
membership = FakeMembership()
config_params = {'memb.min_cluster_rows_allowed': 3,
'memb.max_cluster_rows_allowed': 70,
'multiprocessing': False,
'num_clusters': 1,
'output_dir': 'out',
'debug': False,
'num_iterations': 2000}
func = motif.MemeScoringFunction(organism, membership, ratio_matrix,
meme_suite,
sequence_filters=sequence_filters,
scaling_func=lambda iter: 1.0,
num_motif_func=lambda iter: 1,
update_in_iteration=lambda x: True,
motif_in_iteration=lambda x: True,
config_params=config_params)
iteration_result = { 'iteration': 100 }
matrix = func.compute(iteration_result)
"""
def test_motif_scoring(self):
meme_suite = meme.MemeSuite430({'MEME': {'max_width': 24, 'background_order': 3, 'use_revcomp': 'True'}})
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(self.search_distances['upstream'])]
motif_scoring = motif.MemeScoringFunction(
self.organism, self.membership, self.ratio_matrix,
config_params=self.config_params)
motscores = motif_scoring.compute(self.iteration_result).sorted_by_row_name()
motscores.fix_extreme_values()
ref_motscores = read_matrix('testdata/motscores_fixed.tsv')
self.assertTrue(check_matrix_values(motscores, ref_motscores))
def make_row_scoring(self):
"""makes a row scoring function on demand"""
# Default row scoring functions
row_scoring = microarray.RowScoringFunction(
self.membership(), self.ratio_matrix,
scaling_func=lambda iteration: self['row_scaling'],
config_params=self.config_params)
self.row_scoring = row_scoring
meme_suite = meme.MemeSuite430()
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(self['search_distances']['upstream'])]
motif_scaling_fun = scoring.get_default_motif_scaling(
self['num_iterations'])
motif_scoring = motif.MemeScoringFunction(
self.organism(),
self.membership(),
self.ratio_matrix,
meme_suite,
sequence_filters=sequence_filters,
scaling_func=motif_scaling_fun,
num_motif_func=motif.default_nmotif_fun,
update_in_iteration=scoring.schedule(601, 3),
motif_in_iteration=scoring.schedule(600, 100),
#update_in_iteration=scoring.schedule(100, 10),
#motif_in_iteration=scoring.schedule(100, 100),
config_params=self.config_params)
self.motif_scoring = motif_scoring
network_scaling_fun = scoring.get_default_network_scaling(
self['num_iterations'])
network_scoring = nw.ScoringFunction(
self.organism(),
self.membership(),
self.ratio_matrix,
scaling_func=network_scaling_fun,
run_in_iteration=scoring.schedule(1, 7),
config_params=self.config_params)
self.network_scoring = network_scoring
row_scoring_functions = [row_scoring, motif_scoring, network_scoring]
return scoring.ScoringFunctionCombiner(
self.membership(),
row_scoring_functions,
config_params=self.config_params,
log_subresults=True)
def test_motif_scoring(self):
meme_suite = meme.MemeSuite430()
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(self.search_distances['upstream'])]
motif_scaling_fun = scoring.get_default_motif_scaling(2000)
motif_scoring = motif.MemeScoringFunction(
self.organism,
self.membership,
self.ratio_matrix,
meme_suite,
sequence_filters=sequence_filters,
scaling_func=motif_scaling_fun,
num_motif_func=motif.default_nmotif_fun,
update_in_iteration=lambda x: True,
motif_in_iteration=lambda x: True,
config_params=self.config_params)
motscores = motif_scoring.compute(self.iteration_result).sorted_by_row_name()
ref_motscores = read_matrix('testdata/motscores_fixed.tsv')
self.assertTrue(check_matrix_values(motscores, ref_motscores))
def test_motif_scoring(self):
meme_suite = meme.MemeSuite430({
'MEME': {
'max_width': 24,
'background_order': 3,
'use_revcomp': 'True',
'arg_mod': 'zoops'
}
})
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(self.search_distances['upstream'])
]
motif_scoring = motif.MemeScoringFunction(
self.organism,
self.membership,
self.ratio_matrix,
config_params=self.config_params)
motscores = motif_scoring.compute(
self.iteration_result).sorted_by_row_name()
motscores.fix_extreme_values()
ref_motscores = read_matrix('testdata/ref_motscores.tsv')
self.assertTrue(check_matrix_values(motscores, ref_motscores))
def test_motif_scoring(self):
meme_suite = meme.MemeSuite430()
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(self.search_distances['upstream'])
]
motif_scaling_fun = get_default_motif_scaling(2000, offset=0)
motif_scoring = motif.MemeScoringFunction(
self.organism,
self.membership,
self.ratio_matrix,
meme_suite,
sequence_filters=sequence_filters,
scaling_func=motif_scaling_fun,
num_motif_func=lambda iteration: 1,
update_in_iteration=lambda x: True,
motif_in_iteration=lambda x: True,
config_params=self.config_params)
motscores = motif_scoring.compute(
self.iteration_result).sorted_by_row_name()
motscores.fix_extreme_values()
ref_motscores = read_matrix('testdata/motscores_fixed.tsv')
self.assertTrue(check_matrix_values(motscores, ref_motscores))
def make_row_scoring(self):
"""makes a row scoring function on demand"""
# Default row scoring functions
row_scoring = microarray.RowScoringFunction(
self.membership(), self.ratio_matrix,
scaling_func=lambda iteration: self['row_scaling'],
config_params=self.config_params)
meme_suite = meme.MemeSuite481(remove_tempfiles=True)
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(self['search_distances']['upstream'])]
motif_scoring = motif.MemeScoringFunction(
self.organism(),
self.membership(),
self.ratio_matrix,
meme_suite,
sequence_filters=sequence_filters,
pvalue_filter=motif.MinPValueFilter(-20.0),
scaling_func=lambda iteration: 1.0, # TODO
run_in_iteration=motif_iterations,
config_params=self.config_params)
network_scoring = nw.ScoringFunction(self.organism(),
self.membership(),
self.ratio_matrix,
scaling_func=lambda iteration: 0.0,
run_in_iteration=network_iterations,
config_params=self.config_params)
row_scoring_functions = [row_scoring, motif_scoring, network_scoring]
return scoring.ScoringFunctionCombiner(self.membership(),
row_scoring_functions,
log_subresults=True)
def make_row_scoring(self):
"""makes a row scoring function on demand"""
# Default row scoring functions
row_scaling_fun = scoring.get_scaling(self, 'row_')
row_scoring = microarray.RowScoringFunction(
self.organism(),
self.membership(),
self.ratio_matrix,
scaling_func=row_scaling_fun,
schedule=self["row_schedule"],
config_params=self.config_params)
row_scoring_functions = [row_scoring]
if self['domotifs']:
background_file = None
if self['global_background']:
background_file = meme.global_background_file(
self.organism(), self.ratio_matrix.row_names,
self['sequence_types'][0])
if self['meme_version'] == '4.3.0':
meme_suite = meme.MemeSuite430(background_file=background_file)
elif (self['meme_version']
and (self['meme_version'].startswith('4.8')
or self['meme_version'].startswith('4.9'))):
meme_suite = meme.MemeSuite481(background_file=background_file)
else:
logging.error("MEME version %s currently not supported !",
self['meme_version'])
raise Exception("unsupported MEME version")
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(
self['search_distances']['upstream'])
]
motif_scaling_fun = scoring.get_scaling(self, 'motif_')
nmotif_fun = motif.num_meme_motif_fun(self)
motif_scoring = motif.MemeScoringFunction(
self.organism(),
self.membership(),
self.ratio_matrix,
meme_suite,
sequence_filters=sequence_filters,
scaling_func=motif_scaling_fun,
num_motif_func=nmotif_fun,
update_in_iteration=self['motif_schedule'],
motif_in_iteration=self['meme_schedule'],
config_params=self.config_params)
row_scoring_functions.append(motif_scoring)
if self['donetworks']:
network_scaling_fun = scoring.get_scaling(self, 'network_')
network_scoring = nw.ScoringFunction(
self.organism(),
self.membership(),
self.ratio_matrix,
scaling_func=network_scaling_fun,
schedule=self['network_schedule'],
config_params=self.config_params)
row_scoring_functions.append(network_scoring)
return scoring.ScoringFunctionCombiner(
self.organism(),
self.membership(),
row_scoring_functions,
config_params=self.config_params)
def make_row_scoring(self):
"""makes a row scoring function on demand"""
# Default row scoring functions
row_scaling_fun = scoring.get_scaling(self, 'row_')
row_scoring = microarray.RowScoringFunction(self.organism(),
self.membership(), self.ratio_matrix,
scaling_func=row_scaling_fun,
schedule=self["row_schedule"],
config_params=self.config_params)
row_scoring_functions = [row_scoring]
if self['domotifs']:
background_file = None
if self['global_background']:
background_file = meme.global_background_file(
self.organism(), self.ratio_matrix.row_names,
self['sequence_types'][0])
if self['meme_version'] == '4.3.0':
meme_suite = meme.MemeSuite430(background_file=background_file)
elif (self['meme_version'] and
(self['meme_version'].startswith('4.8') or
self['meme_version'].startswith('4.9'))):
meme_suite = meme.MemeSuite481(background_file=background_file)
else:
logging.error("MEME version %s currently not supported !", self['meme_version'])
raise Exception("unsupported MEME version")
sequence_filters = [
motif.unique_filter,
motif.get_remove_low_complexity_filter(meme_suite),
motif.get_remove_atgs_filter(self['search_distances']['upstream'])]
motif_scaling_fun = scoring.get_scaling(self, 'motif_')
nmotif_fun = motif.num_meme_motif_fun(self)
motif_scoring = motif.MemeScoringFunction(self.organism(),
self.membership(),
self.ratio_matrix,
meme_suite,
sequence_filters=sequence_filters,
scaling_func=motif_scaling_fun,
num_motif_func=nmotif_fun,
update_in_iteration=self['motif_schedule'],
motif_in_iteration=self['meme_schedule'],
config_params=self.config_params)
row_scoring_functions.append(motif_scoring)
if self['donetworks']:
network_scaling_fun = scoring.get_scaling(self, 'network_')
network_scoring = nw.ScoringFunction(self.organism(),
self.membership(),
self.ratio_matrix,
scaling_func=network_scaling_fun,
schedule=self['network_schedule'],
config_params=self.config_params)
row_scoring_functions.append(network_scoring)
return scoring.ScoringFunctionCombiner(
self.organism(),
self.membership(),
row_scoring_functions,
config_params=self.config_params)
